Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is responsive to the applicant’s arguments filed on 12/28/2025.
Claims 1-4 and 6-24 are pending.
Claims 1, 6-7, 9-21, and 23 are amended. Claim 5 is canceled. Claim 24 is new.
Response to Arguments
Regarding rejections under 35 USC § 112:
The rejection has been updated in view of amendments.
Regarding rejections under 35 USC § 101:
The rejection has been withdrawn in view of amendments.
Regarding rejections under 35 USC § 103:
Applicant's arguments filed 12/28/2025 have been fully considered but they are not persuasive.
With respect to the remarks, page 12-13, regarding tensor multiplication, the Examiner respectfully disagrees.
To clarify, Examiner notes that according to description of specification para [0094], a tensor is a type of data structure or an array for holding values ([0094]: “A tensor is a multi-dimensional array with a uniform type. In other words, a tensor is an algebraic object that describes a multilinear relationship between sets of algebraic objects related to a vector space. Objects that tensors may map between include vectors and scalars, and even other tensors. An example of a zero-order tensor is a fixed power setpoint, while a first-order tensor is a vector, such as phasor representing the phase shift between two waveforms (e.g., voltage and current). A second-order tensors is a matrix, where two matrices might be used to represent estimated future values of reflected power and load impedance at two future times, and where multiplying those matrices together may be used as a simplified mathematical operation to predict a trajectory of reflected power and load impedance into the future.”). Cline discloses data including reference signal ([0029]: “Compensator module 8 and reference model module 10 may operate together in a closed-loop, in which reference model module 10 outputs the reference state trajectory signal, Xref, to compensator module 8, which adjusts the reference control signal, Rref, based on the reference state trajectory signal, Xref. … By operating in a closed loop, compensator module 8 may use modeled engine information to adjust the control reference signal upstream of control module 12.”); a measurement of an output of the power system ([0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time. Adaptation module 16 includes a set of adaptation laws. The set of adaptation laws may be configured to generate an output, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, to control module 12 to reduce or substantially cancel effects of uncertainties in power management system 2.”) ([0031]: “In addition to measured engine control variables, adaptation module 16 may be configured to receive unmeasured engine parameter estimates from, for example, a model-based engine module, which may be further described in FIG. 4. Adaptation module 16 may determine the adaptation signal, Radp, based on the reference state trajectory signal, Xref, the engine state trajectory signal, Xeng, and an unmeasured engine parameter estimate.”) ([0024]: “The reference control signal, Rref, may represent a measurable, manipulated engine variable associated with gas turbine engine operation. Engine control variables may include, but are not limited to, fuel flow and air flow. Each measurable engine control variable may have an associated engine component that controls the measurable engine control variable.”); control for a previous control sample ([0027]: “In some examples, closed-loop reference module 6 may include reference model module 10.”) ([0029]: “By operating in a closed loop, compensator module 8 may use modeled engine information to adjust the control reference signal upstream of control module 12.”) ([0058]: “Model-based engine module 50 may include tracking module 46 to use measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng, and produce a RTEM update based on the tracked measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng.”); and estimated model parameter tensor ([0020]: “The adaptation module may receive the unmeasured engine parameter signal and adapt the controller to account for changes in performance of the gas turbine engine and cause the estimate of the unmeasured parameter to track the parameters reference model trajectory.”).
Karpman discloses storing parameters related to adaptive engine in a tensor and teaches parameter feedback/difference adjustment process involving tensor multiplication. The motivation for the combination would be that using a tensor allow performing operations that would ease the calculations with multiple values.
Therefore, Examiner notes that the combination is proper because Karpman provides a teaching for using a tensor and calculations involving tensor multiplication. The specific data/use of the tensor disclosed in Karpman does not have to be identical, since they are in the same field and there is a motivation incorporate the teaching. Regarding the remarks that the conventional processing does not suggest the tensor-based parameter-regressor multiplication claimed, Examiner notes that the motivation is provided by the secondary reference, not by the primary reference.
Claim Interpretation
Claims 1, 10, and 18 recite the limitations “control portion” and “estimation portion”. These limitations are interpreted in view of the specification para [00261]: “From this generalized from of the model, one sees that both the control portion and the estimation portion are functions of the estimated model parameter tensor, Θ, and the input regressor, Ø.”
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
“controller” in claims 10-17
“sub-engine” in claims 11-15
“selector” in claims 10, 12, and 16
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Specification para [00365] discloses that the controller amounts to a sub-engine. Specification para [00666] discloses that the structure for the “sub-engine” is a processing device such as a FPGA (optional field programmable gate array). Therefore, “controller” and “sub-engines” are interpreted as processing devices such as a FPGA or equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 10-17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 10, claim 10 recites the limitation “a selector configured to control the actuator, and/or the power system controlled by the actuator based on the possible control signal and the estimated system output”. This limitation is not disclosed in specification. At best, specification discloses a “selector module”, but this limitation appears to be performing a different function from the one recited in claim for “selector” (For example, see specification para [0021]: “In some aspects, the techniques described herein relate to a Lyapunov-based controller, further including a selector module configured to perform the selecting based on (1) a set of possible control signals that includes the possible control signal and (2) a set of estimated system outputs that includes the estimated system output.”). Moreover, specification describes that the controlling of an actuator is performed by an adaptive engine rather than a selector module ([00199]: “controlling one or more actuators controlling a system (e.g., a plasma processing system and/or power supply for the plasma processing system) via an adaptive engine.”) ([00282]: “Further, while the control law modules or sub-engines are described as part of a larger adaptive engine, in some embodiments, the control law modules or sub-engines can operate independently, such that each on its own can control one or more actuators of a power system.”). Therefore, the limitation “a selector configured to control the actuator, and/or the power system controlled by the actuator based on the possible control signal and the estimated system output” is not described in specification.
Claims 11-17 are rejected by the virtue of their dependency on the reject claim(s).
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10-17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 10, claim limitation “a selector” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
To clarify, specification discloses a “selector module,” but this module is a module configured to select possible control signals and estimated system outputs, not to control an actuator as recited in claim (See specification para [0021]: “In some aspects, the techniques described herein relate to a Lyapunov-based controller, further including a selector module configured to perform the selecting based on (1) a set of possible control signals that includes the possible control signal and (2) a set of estimated system outputs that includes the estimated system output.”).
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
For examining purposes, the “selector” is interpreted as any hardware/software device capable of controlling an actuator.
Claims 11-17 are rejected by the virtue of their dependency on the reject claim(s).
Claim Objections
Claim 18 is objected to because of the following informalities: the limitation “accessing a model of actuator” should read “accessing a model of an actuator”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6-12, 16-18, and 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cline et al. (US20180119629A1), hereinafter Cline, in view of Karpman et al. (US20110054704A1), hereinafter Karpman.
Regarding claim 1, Cline discloses
receiving an input regressor ([0030]: “Adaptation module 16 may receive the reference state trajectory signal, Xref, from reference model module 10 and the engine state trajectory signal, Xeng, from gas turbine engine 14. … The adaptation module may determine the adaptation signal, Radp, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, and one or more adaptation laws.”);
receiving an estimated model parameter … for a model of an actuator and/or a power system controlled by the actuator ([0020]: “The adaptation module may receive the unmeasured engine parameter signal and adapt the controller to account for changes in performance of the gas turbine engine and cause the estimate of the unmeasured parameter to track the parameters reference model trajectory.”),
the model comprising a time-varying linear system dependent on the estimated model parameter … ([0028]: “Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14. For example, a linear design model may be piecewise linear to handle changes in operating conditions.”) ([0058]: “In some examples, tracking module 46 may track the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, over a period of time to determine any changes in engine performance.”) ([0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time.”);
accessing a structure of the time-varying linear system and applying it to the model ([0028]: “Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14. For example, a linear design model may be piecewise linear to handle changes in operating conditions.”) ([0019]: “By using modeled engine information to adjust to changes in engine performance and control, the power management system described herein may be used to accommodate nonlinearities, uncertainties, variations and degradations in gas turbine engine control and performance to enhance steady-state and transient performance while maintaining safe operation.”);
applying a control portion of the model … to generate a possible control signal ([0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”);
applying an estimation portion of the model involving using the possible control signal from the control portion as an input to the time-varying linear system to estimate an estimated system output ([0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time. Adaptation module 16 includes a set of adaptation laws. The set of adaptation laws may be configured to generate an output, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, to control module 12 to reduce or substantially cancel effects of uncertainties in power management system 2.”) ([0031]: “In addition to measured engine control variables, adaptation module 16 may be configured to receive unmeasured engine parameter estimates from, for example, a model-based engine module, which may be further described in FIG. 4. Adaptation module 16 may determine the adaptation signal, Radp, based on the reference state trajectory signal, Xref, the engine state trajectory signal, Xeng, and an unmeasured engine parameter estimate.”);
controlling the actuator and/or the system controlled by the actuator, via a control output, the control output based on the possible control signal and the estimated system output ([0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. … Control module 12 may output, to gas turbine engine 14, the demand signal, Rdmd, to control operation of at least one component of gas turbine engine 14.”) ([0033]: “Gas turbine engine 14 may receive the demand signal, Rdmd, from control module 12. Gas turbine engine 14 may include control components such … actuators to control engine control variables.”) ([0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. Loop selection logic 30 may be configured to receive at least one of the steady state demand signal, Rss,dmd, the transient demand signal, Rtrn,dmd, or the limit protection demand signal, Rlmt,dmd. In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal.”) ([0044]: “For example, during steady state engine operation, a control system may control loop selection logic 30 to select steady state demand signal, Rss,dmd, to output to actuator 32. If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”).
Cline does not explicitly disclose
estimated model parameter being tensor; and
tensor multiplication of the estimated model parameter tensor and the input regressor, or a modified input regressor including filtered sub-component of the input regressor.
However, Karpman teaches a parameter being a tensor and parameter feedback/difference adjustment process involving tensor multiplication ([0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”) ([0092]: “In SISO embodiments, the feedback contribution in Eq. 14 is approximated by the following single-input, single-output model state estimator equation: … Essentially, Eq. 19 provides a difference equation (or a set of difference equations) for obtaining time-advanced constraint vector UK k+1. The difference equations define the change in time-advanced constraint vector UK k+1 from its value at base point BP, in terms of the tensor product of gain matrix S and error vector E, scaled by time step ΔT.”).
Cline and Karpman are analogous to the claimed invention because they are in the same field of nonlinear power control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Karpman on representing the model parameters as tensors and utilizing tensor multiplication in calculations with the teachings from Cline on the model parameters and generation of control signals. The motivation to combine would have been that using such a generalized tensor form would allow performing vector/matrix operations which would ease the calculations with multiple values (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”) (Karpman, [0092]: “In SISO embodiments, the feedback contribution in Eq. 14 is approximated by the following single-input, single-output model state estimator equation: … Essentially, Eq. 19 provides a difference equation (or a set of difference equations) for obtaining time-advanced constraint vector UK k+1. The difference equations define the change in time-advanced constraint vector UK k+1 from its value at base point BP, in terms of the tensor product of gain matrix S and error vector E, scaled by time step ΔT.”).
Therefore, the combination of Cline and Karpman teaches
receiving an estimated model parameter tensor for a model of an actuator and/or a power system controlled by the actuator (Cline, [0020]: “The adaptation module may receive the unmeasured engine parameter signal and adapt the controller to account for changes in performance of the gas turbine engine and cause the estimate of the unmeasured parameter to track the parameters reference model trajectory.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”),
the model comprising a time-varying linear system dependent on the estimated model parameter tensor (Cline, [0028]: “Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14. For example, a linear design model may be piecewise linear to handle changes in operating conditions.”) (Cline, [0058]: “In some examples, tracking module 46 may track the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, over a period of time to determine any changes in engine performance.”) (Cline, [0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”); and
applying a control portion of the model involving tensor multiplication of the estimated model parameter tensor and the input regressor, or a modified input regressor including filtered sub-component of the input regressor, to generate a possible control signal (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”) (Karpman, [0092]: “In SISO embodiments, the feedback contribution in Eq. 14 is approximated by the following single-input, single-output model state estimator equation: … Essentially, Eq. 19 provides a difference equation (or a set of difference equations) for obtaining time-advanced constraint vector UK k+1. The difference equations define the change in time-advanced constraint vector UK k+1 from its value at base point BP, in terms of the tensor product of gain matrix S and error vector E, scaled by time step ΔT.”).
Regarding claim 6, Cline/Karpman teaches
updating the estimated model parameter tensor and the time-varying linear system at multiple control samples (Cline, [0028]: “Reference model module 10 may utilize any reference model capable of producing reference state trajectory signals, Xref, of engine controlled variables, including multiple reference models over a range of operating conditions for gas turbine engine 14. Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14.”) (Cline, [0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time. Adaptation module 16 includes a set of adaptation laws. The set of adaptation laws may be configured to generate an output, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, to control module 12 to reduce or substantially cancel effects of uncertainties in power management system 2.”) (Cline, [0072]: “Model-based engine module 50 may track the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, to determine changes in engine performance. Model-based engine module 50 may update the RTEM based on the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, to more accurately reflect engine performance. In some examples, model-based engine module 50 may track the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, over a period of time to determine any changes in engine performance. Adaptation module 16 may determine the adaptation signal, Radp, based on the difference between the reference parameter trajectory signal, Xe,ref, and the engine parameter estimate signal, Xest, which control module 12 may use to adjust the control laws.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”).
The already provided combination is applicable.
Regarding claim 7, Cline/Karpman teaches
updating the structure of the time-varying linear system at multiple frames (Cline, [0028]: “Reference model module 10 may utilize any reference model capable of producing reference state trajectory signals, Xref, of engine controlled variables, including multiple reference models over a range of operating conditions for gas turbine engine 14. Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14.”) (Cline, [0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time. Adaptation module 16 includes a set of adaptation laws. The set of adaptation laws may be configured to generate an output, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, to control module 12 to reduce or substantially cancel effects of uncertainties in power management system 2.”) (Cline, [0072]: “Model-based engine module 50 may track the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, to determine changes in engine performance. Model-based engine module 50 may update the RTEM based on the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, to more accurately reflect engine performance. In some examples, model-based engine module 50 may track the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, over a period of time to determine any changes in engine performance. Adaptation module 16 may determine the adaptation signal, Radp, based on the difference between the reference parameter trajectory signal, Xe,ref, and the engine parameter estimate signal, Xest, which control module 12 may use to adjust the control laws.”).
Regarding claim 8, Cline/Karpman teaches
selecting the possible control signal as the control output (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”) (Cline, [0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. Loop selection logic 30 may be configured to receive at least one of the steady state demand signal, Rss,dmd, the transient demand signal, Rtrn,dmd, or the limit protection demand signal, Rlmt,dmd. In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal.”) (Cline, [0044]: “For example, during steady state engine operation, a control system may control loop selection logic 30 to select steady state demand signal, Rss,dmd, to output to actuator 32. If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”).
Regarding claim 9, Cline/Karpman teaches
wherein the influence is a weight applied to the possible control signal when blending the possible control signal with one or more other possible control signals to give the control output (Cline, [0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. … In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal. Loop selection logic 30 may output the demand signal to one or more actuator(s) 32 to control a component of gas turbine engine 14.”) (Cline, [0044]: “If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”).
Regarding claim 10, Cline/Karpman teaches
a control portion of a model of an actuator and/or a power system controlled by the actuator (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”),
the control portion configured to calculate a possible control signal by tensor multiplying (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”)
(1) a first tensor comprising at least a reference signal, a measurement of an output of the power system, and a control for a previous control sample (Cline, [0029]: “Compensator module 8 and reference model module 10 may operate together in a closed-loop, in which reference model module 10 outputs the reference state trajectory signal, Xref, to compensator module 8, which adjusts the reference control signal, Rref, based on the reference state trajectory signal, Xref. … By operating in a closed loop, compensator module 8 may use modeled engine information to adjust the control reference signal upstream of control module 12.”) (Cline, [0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time. Adaptation module 16 includes a set of adaptation laws. The set of adaptation laws may be configured to generate an output, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, to control module 12 to reduce or substantially cancel effects of uncertainties in power management system 2.”) (Cline, [0031]: “In addition to measured engine control variables, adaptation module 16 may be configured to receive unmeasured engine parameter estimates from, for example, a model-based engine module, which may be further described in FIG. 4. Adaptation module 16 may determine the adaptation signal, Radp, based on the reference state trajectory signal, Xref, the engine state trajectory signal, Xeng, and an unmeasured engine parameter estimate.”) (Cline, [0024]: “The reference control signal, Rref, may represent a measurable, manipulated engine variable associated with gas turbine engine operation. Engine control variables may include, but are not limited to, fuel flow and air flow. Each measurable engine control variable may have an associated engine component that controls the measurable engine control variable.”) (Cline, [0027]: “In some examples, closed-loop reference module 6 may include reference model module 10.”) (Cline, [0029]: “By operating in a closed loop, compensator module 8 may use modeled engine information to adjust the control reference signal upstream of control module 12.”) (Cline, [0058]: “Model-based engine module 50 may include tracking module 46 to use measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng, and produce a RTEM update based on the tracked measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”), and
(2) a second tensor comprising an estimated model parameter tensor (Cline, [0020]: “The adaptation module may receive the unmeasured engine parameter signal and adapt the controller to account for changes in performance of the gas turbine engine and cause the estimate of the unmeasured parameter to track the parameters reference model trajectory.”);
an estimation portion of the model configured to estimate an estimated system output for the power system based on the time-varying linear system and the possible control signal (Cline, [0028]: “Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14. For example, a linear design model may be piecewise linear to handle changes in operating conditions.”) (Cline, [0030]: “Adaptation module 16 may receive the reference state trajectory signal, Xref, from reference model module 10 and the engine state trajectory signal, Xeng, from gas turbine engine 14. … The adaptation module may determine the adaptation signal, Radp, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, and one or more adaptation laws. … The adaptation signal, Radp, may represent an adjustment to one or more control parameters of control module 12 based on the trajectory difference between the reference state trajectory signals, Xref, and the engine state trajectory signal, Xeng.”) (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”),
a selector configured to control the actuator, and/or the power system controlled by the actuator based on the possible control signal and the estimated system output (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. … Control module 12 may output, to gas turbine engine 14, the demand signal, Rdmd, to control operation of at least one component of gas turbine engine 14.”) (Cline, [0033]: “Gas turbine engine 14 may receive the demand signal, Rdmd, from control module 12. Gas turbine engine 14 may include control components such … actuators to control engine control variables.”) (Cline, [0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. Loop selection logic 30 may be configured to receive at least one of the steady state demand signal, Rss,dmd, the transient demand signal, Rtrn,dmd, or the limit protection demand signal, Rlmt,dmd. In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal.”) (Cline, [0044]: “For example, during steady state engine operation, a control system may control loop selection logic 30 to select steady state demand signal, Rss,dmd, to output to actuator 32. If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”).
The already provided combination is applicable.
Regarding claim 11, Cline/Karpman teaches
being a sub-engine of an adaptive controller (Cline, [0016]: “A controller may include a control module, a closed-loop reference module, and an adaptation module.”),
wherein the possible control signal is selected from other possible control signals provided by other sub-engines in the adaptive controller, as the control (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”) (Cline, [0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. Loop selection logic 30 may be configured to receive at least one of the steady state demand signal, Rss,dmd, the transient demand signal, Rtrn,dmd, or the limit protection demand signal, Rlmt,dmd. In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal.”) (Cline, [0044]: “For example, during steady state engine operation, a control system may control loop selection logic 30 to select steady state demand signal, Rss,dmd, to output to actuator 32. If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”).
Regarding claim 12, Cline/Karpman teaches
being a sub-engine of an adaptive controller (Cline, [0016]: “A controller may include a control module, a closed-loop reference module, and an adaptation module.”),
wherein the possible control signal is blended with other possible control signals from other sub-engines in the selector (Cline, [0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. … In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal. Loop selection logic 30 may output the demand signal to one or more actuator(s) 32 to control a component of gas turbine engine 14.”) (Cline, [0044]: “If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”).
Regarding claim 16, Cline/Karpman teaches
wherein the selector is configured to perform the control based on (1) a set of possible control signals that includes the possible control signal and (2) a set of estimated system outputs that includes the estimated system output (Cline, [0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. Loop selection logic 30 may be configured to receive at least one of the steady state demand signal, Rss,dmd, the transient demand signal, Rtrn,dmd, or the limit protection demand signal, Rlmt,dmd. In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal.”) (Cline, [0044]: “For example, during steady state engine operation, a control system may control loop selection logic 30 to select steady state demand signal, Rss,dmd, to output to actuator 32. If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”) (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”).
Regarding claim 17, Cline does not explicitly teach a derivative of the estimated model parameter tensor.
However, Karpman teaches a derivative of a parameter tensor ([0056]: “OLM 21 also generates physics state vector derivatives defined by: … Physics state vector derivatives {dot over (X)}ƒ model the time rates of change of physics state vectors Xƒ, which are generated by model state estimator 24, below. … COM 22 forms corrector output vector YC, as a subset of output vector Y and physics state vector derivatives Xƒ from OLM 21.”) ([0097]: “In the control formulation approach, the elements of error vector E are assumed to depend upon the time derivative of an unknown state vector Xd.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Karpman on using a derivative of a parameter tensor with the teachings from Cline on the estimated model parameter tensor. The motivation to combine would have been that using a derivative allows analyzing changes over time which would allow a more accurate analysis (Karpman, [0056]: “Physics state vector derivatives {dot over (X)}ƒ model the time rates of change of physics state vectors Xƒ, which are generated by model state estimator 24, below.”).
Therefore, Cline/Karpman teaches
wherein the second tensor further comprises a derivative of the estimated model parameter tensor (Cline, [0030]: “The adaptation module may determine the adaptation signal, Radp, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, and one or more adaptation laws. The adaptation signal, Radp, may represent an adjustment to one or more control parameters of control module 12 based on the trajectory difference between the reference state trajectory signals, Xref, and the engine state trajectory signal, Xeng.”) (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”) (Cline, [0058]: “Model-based engine module 50 may include tracking module 46 to use measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng, and produce a RTEM update based on the tracked measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng.”) (Cline, [0020]: “The adaptation module may receive the unmeasured engine parameter signal and adapt the controller to account for changes in performance of the gas turbine engine and cause the estimate of the unmeasured parameter to track the parameters reference model trajectory.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”) (Karpman, [0056]: “OLM 21 also generates physics state vector derivatives defined by: … Physics state vector derivatives {dot over (X)}ƒ model the time rates of change of physics state vectors Xƒ, which are generated by model state estimator 24, below. … COM 22 forms corrector output vector YC, as a subset of output vector Y and physics state vector derivatives Xƒ from OLM 21.”) (Karpman, [0097]: “In the control formulation approach, the elements of error vector E are assumed to depend upon the time derivative of an unknown state vector Xd.”).
Regarding claim 18, Claim 18 is substantially similar to claim 1. Therefore, the similar analysis as claim 1 is applicable.
Regarding claim 22, Cline/Karpman teaches
wherein the estimation portion comprises a time-varying linear system that is a function of the estimated model parameter tensor and the possible control signal (Cline, [0028]: “Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14. For example, a linear design model may be piecewise linear to handle changes in operating conditions.”) (Cline, [0030]: “Adaptation module 16 may receive the reference state trajectory signal, Xref, from reference model module 10 and the engine state trajectory signal, Xeng, from gas turbine engine 14. … The adaptation module may determine the adaptation signal, Radp, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, and one or more adaptation laws. … The adaptation signal, Radp, may represent an adjustment to one or more control parameters of control module 12 based on the trajectory difference between the reference state trajectory signals, Xref, and the engine state trajectory signal, Xeng.”) (Cline, [0032]: “Control module 12 may receive a variety of engine signals, e.g. engine state trajectory signal, Xeng, engine parameter estimate signal (not shown), reference state trajectory signal, Xref, reference control signal, Rref, and adaptation signal, Radp, operational mode signal (not shown) to generate a signal from those inputs to control actuators (not shown) for gas turbine engine 14 based on a set of control laws. The set of control laws may be algorithms and gain schedules with configurable parameters selected and configured to convert the reference control signal, Rref, the adaptation signal, Radp, and the engine state trajectory signal, Xeng, into a demand signal, Rdmd, that actuators or other control components in gas turbine engine 14 may use to control engine control variables.”) (Cline, [0024]: “The reference control signal, Rref, may represent a measurable, manipulated engine variable associated with gas turbine engine operation. Engine control variables may include, but are not limited to, fuel flow and air flow. Each measurable engine control variable may have an associated engine component that controls the measurable engine control variable.”) (Cline, [0058]: “Model-based engine module 50 may include tracking module 46 to use measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng, and produce a RTEM update based on the tracked measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng.”) (Cline, [0029]: “Compensator module 8 and reference model module 10 may operate together in a closed-loop, in which reference model module 10 outputs the reference state trajectory signal, Xref, to compensator module 8, which adjusts the reference control signal, Rref, based on the reference state trajectory signal, Xref. … By operating in a closed loop, compensator module 8 may use modeled engine information to adjust the control reference signal upstream of control module 12.”) (Cline, [0020]: “The adaptation module may receive the unmeasured engine parameter signal and adapt the controller to account for changes in performance of the gas turbine engine and cause the estimate of the unmeasured parameter to track the parameters reference model trajectory.”) (Karpman, [0068]: “Note that Eqs. 10-12 utilize a generalized tensor form, in which the product of tensors Aj i and vectors Xƒ, UK and UX tend to mix or cross-couple contributions from different vector elements.”).
The already provided combination is applicable.
Regarding claim 23, Cline does not but Karpman teaches a discrete/grouped time frames and performing calculations on each time frame ([0064]: “MSE 24 generates time-advanced physics state vector Xƒ k+1 and time-advanced constraint input state vector UK k+1 in order to minimize error vector E, physics state vector derivative {dot over (X)}ƒ, or both.”) ([0065]: “ΔT is the step time”) ([0071]: “In larger-scale practical engineering systems where the model cycle time (the step size) must be increased to keep up with the processing rate of apparatus 11, nonlinearities must be addressed in a more sophisticated way… where error integral ∫E dt is performed over a number of control system time steps.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Karpman on grouping time frames to perform calculations on each time frame with the teachings from Cline on approximating changes of the nonlinear system over time. The motivation to combine would have been that doing so allows approximating the dynamics of the system (Karpman, [0065]: “Since vector Xƒ typically has relatively slow dynamic response with respect to the execution speed of CLM 12, the time-advanced form (Xƒ k+1) can be estimated using an appropriate numerical approximation such as the forward rectangle rule.”) (Karpman, [0071]: “In larger-scale practical engineering systems where the model cycle time (the step size) must be increased to keep up with the processing rate of apparatus 11, nonlinearities must be addressed in a more sophisticated way… where error integral ∫E dt is performed over a number of control system time steps.”) (Karpman, [0075]: “As with other elements of physics state vector Xƒ, shaft speed N is determined by engineering principles, has relatively slow response time compared to the model time step, and reflects system dynamics that are accurately modeled by OLS 118.”).
Therefore, Cline/Karpman teaches
comprising grouping the control samples into frames and updating a structure of the time-varying linear system once per frame to approximate large changes in nonlinear behavior of the actuator and/or the power system controlled by the actuator (Cline, [0028]: “Reference models that may be used include, but are not limited to, linear models such as piecewise linear models; nonlinear models such as nonlinear thermodynamic cycle models; and any other model capable of representing dynamic or steady-state performance of gas turbine engine 14. For example, a linear design model may be piecewise linear to handle changes in operating conditions.”) (Cline, [0058]: “In some examples, tracking module 46 may track the engine parameter estimate signal, Xest, and the engine state trajectory signal, Xeng, over a period of time to determine any changes in engine performance.”) (Cline, [0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time.”) (Karpman, [0064]: “MSE 24 generates time-advanced physics state vector Xƒ k+1 and time-advanced constraint input state vector UK k+1 in order to minimize error vector E, physics state vector derivative {dot over (X)}ƒ, or both.”) (Karpman, [0065]: “ΔT is the step time”) (Karpman, [0071]: “In larger-scale practical engineering systems where the model cycle time (the step size) must be increased to keep up with the processing rate of apparatus 11, nonlinearities must be addressed in a more sophisticated way… where error integral ∫E dt is performed over a number of control system time steps.”).
Regarding claim 24, Cline/Karpman teaches
selecting an influence that the possible control signal has on a control output based on the estimated system output (Cline, [0043]: “Loop selection logic 30 may be configured to select an appropriate demand signal for a particular gas turbine engine operating mode or combine two or more demand signals for a comprehensive control signal. Loop selection logic 30 may be configured to receive at least one of the steady state demand signal, Rss,dmd, the transient demand signal, Rtrn,dmd, or the limit protection demand signal, Rlmt,dmd. In some examples, loop selection logic 30 may select at least one of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, for output to actuator 32. In other examples, loop selection logic 30 may combine at least two of the steady state demand signal, Rss,dmd, transient demand signal, Rtrn,dmd, or limit protection demand signal, Rlmt,dmd, into a combined demand signal.”) (Cline, [0044]: “For example, during steady state engine operation, a control system may control loop selection logic 30 to select steady state demand signal, Rss,dmd, to output to actuator 32. If the demand request signal, Preq, increases or decreases, the control system may control loop selection logic 30 to select transient demand signal, Rtrn,dmd, to output to actuator 32. If the engine exceeds a limit during either steady-state operation or transient operation, loop selection logic 30 may select limit protection demand signal, Rlmt,dmd, for output to actuator 32. By utilizing closed-loop MRAC 18 in a controller having at least one of steady-state, transient, and limit protection operation, the controller may operate an engine with reduced design margins for improved performance.”).
Claim(s) 2-4 and 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cline in view of Karpman in further view of Limanond et al. (“Model Reference Adaptive and non-Adaptive Control of Multivariable Linear Time-Varying Plants: The Exact Matching Case”), hereinafter Limanond.
Regarding claim 2, Cline/Karpman teaches
wherein the … sub-component of the input regressor is a measured system output (Cline, [0030]: “Adaptation module 16 may receive the reference state trajectory signal, Xref, from reference model module 10 and the engine state trajectory signal, Xeng, from gas turbine engine 14. … The adaptation module may determine the adaptation signal, Radp, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, and one or more adaptation laws.”) (Cline, [0024]: “The reference control signal, Rref, may represent a measurable, manipulated engine variable associated with gas turbine engine operation. Engine control variables may include, but are not limited to, fuel flow and air flow. Each measurable engine control variable may have an associated engine component that controls the measurable engine control variable.”) (Cline, [0058]: “Model-based engine module 50 may include tracking module 46 to use measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng, and produce a RTEM update based on the tracked measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng.”).
Cline/Karpman does not explicitly teach the filtered sub-component.
However, Limanond teaches filtering values in a non-linear adaptive control problem (Pg. 3075, Right column: “Under suitable conditions on the plant and the reference model we show that the parameters of the previously derived TV MRC structure can be updated on-line by a suitable gradient-based adaptive law so as to ensure the boundedness of all closed-loop signals and the smallness, in a mean-square, normalized sense, of a filtered version of the tracking error y-y*.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Limanond on a filter with the teachings from Cline/Karpman on the input regressor. The motivation to combine would have been that using a filter allows obtaining/converting values in a desired format (Limanond, Pg. 3076, Right column: “Notice that the reason of using the filtered output y in the estimation error €1 is to obtain the desired inner-product form in the case where the entries of Z(s) are not identical PDO’s. The implication of this construction is that the final performance guarantees are given in terms of a filtered version of the tracking error.”).
Therefore, the combination of Cline/Karpman and Limanond teaches
wherein the filtered sub-component of the input regressor is a measured system output (Cline, [0030]: “Adaptation module 16 may receive the reference state trajectory signal, Xref, from reference model module 10 and the engine state trajectory signal, Xeng, from gas turbine engine 14. … The adaptation module may determine the adaptation signal, Radp, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, and one or more adaptation laws.”) (Cline, [0024]: “The reference control signal, Rref, may represent a measurable, manipulated engine variable associated with gas turbine engine operation. Engine control variables may include, but are not limited to, fuel flow and air flow. Each measurable engine control variable may have an associated engine component that controls the measurable engine control variable.”) (Cline, [0058]: “Model-based engine module 50 may include tracking module 46 to use measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng, and produce a RTEM update based on the tracked measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng.”) (Limanond, Pg. 3075, Right column: “Under suitable conditions on the plant and the reference model we show that the parameters of the previously derived TV MRC structure can be updated on-line by a suitable gradient-based adaptive law so as to ensure the boundedness of all closed-loop signals and the smallness, in a mean-square, normalized sense, of a filtered version of the tracking error y-y*.”).
Regarding claim 3, Cline/Karpman/Limanond teaches
wherein another filtered sub-component in the modified input regressor is a control output from a previous control sample (Cline, [0029]: “Compensator module 8 and reference model module 10 may operate together in a closed-loop, in which reference model module 10 outputs the reference state trajectory signal, Xref, to compensator module 8, which adjusts the reference control signal, Rref, based on the reference state trajectory signal, Xref. … By operating in a closed loop, compensator module 8 may use modeled engine information to adjust the control reference signal upstream of control module 12.”) (Cline, [0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time. Adaptation module 16 includes a set of adaptation laws. The set of adaptation laws may be configured to generate an output, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, to control module 12 to reduce or substantially cancel effects of uncertainties in power management system 2.”) (Limanond, Pg. 3075, Right column: “Under suitable conditions on the plant and the reference model we show that the parameters of the previously derived TV MRC structure can be updated on-line by a suitable gradient-based adaptive law so as to ensure the boundedness of all closed-loop signals and the smallness, in a mean-square, normalized sense, of a filtered version of the tracking error y-y*.”).
The already provided combination is applicable.
Regarding claim 4, Cline/Karpman/Limanond teaches
wherein the modified input regressor comprises, a reference signal, a measured system output from the previous control sample, a filtered version of the measured system output from the previous control sample, and a filtered version of the control output from the previous control sample (Cline, [0029]: “Compensator module 8 and reference model module 10 may operate together in a closed-loop, in which reference model module 10 outputs the reference state trajectory signal, Xref, to compensator module 8, which adjusts the reference control signal, Rref, based on the reference state trajectory signal, Xref. … By operating in a closed loop, compensator module 8 may use modeled engine information to adjust the control reference signal upstream of control module 12.”) (Cline, [0027]: “In some examples, closed-loop reference module 6 may include reference model module 10.”) (Cline, [0030]: “Adaptation module 16 may be included in power management system 2 to determine error between actual engine operation and modeled engine operation and output a signal that allows control module 12 to compensate for this error. Error may include, for example, manufacturing variations in gas turbine engine 14 that deviate from the engine model, degradations in performance of gas turbine engine 14 over its life, and variations in sensors and actuators of the engine that may change over time. Adaptation module 16 includes a set of adaptation laws. The set of adaptation laws may be configured to generate an output, based on the trajectory difference between the reference state trajectory signal, Xref, and the engine state trajectory signal, Xeng, to control module 12 to reduce or substantially cancel effects of uncertainties in power management system 2.”) (Cline, [0031]: “In addition to measured engine control variables, adaptation module 16 may be configured to receive unmeasured engine parameter estimates from, for example, a model-based engine module, which may be further described in FIG. 4. Adaptation module 16 may determine the adaptation signal, Radp, based on the reference state trajectory signal, Xref, the engine state trajectory signal, Xeng, and an unmeasured engine parameter estimate.”) (Cline, [0024]: “The reference control signal, Rref, may represent a measurable, manipulated engine variable associated with gas turbine engine operation. Engine control variables may include, but are not limited to, fuel flow and air flow. Each measurable engine control variable may have an associated engine component that controls the measurable engine control variable.”) (Cline, [0058]: “Model-based engine module 50 may include tracking module 46 to use measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng, and produce a RTEM update based on the tracked measured engine parameter estimate signals, Xest, and engine state trajectory signals, Xeng.”) (Limanond, Pg. 3075, Right column: “Under suitable conditions on the plant and the reference model we show that the parameters of the previously derived TV MRC structure can be updated on-line by a suitable gradient-based adaptive law so as to ensure the boundedness of all closed-loop signals and the smallness, in a mean-square, normalized sense, of a filtered version of the tracking error y-y*.”).
The already provided combination is applicable.
Regarding claims 19-21, Claims 19-21 are substantially similar to claims 2-3. Therefore, the similar analysis as claims 2-3 is applicable.
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cline in view of Karpman in further view of Park et al. (“Decoupling Control of A Class of Underactuated Mechanical Systems Based on Sliding Mode Control”), hereinafter Park.
Regarding claim 13, Cline/Karpman does not explicitly teach a Lyapunov framework.
However, Park teaches using a Lyapunov framework in a nonlinear system (Pg. 806, Left column: “The sliding mode control (SMC) [7-9], a kind of variable structure control systems, is a nonlinear feedback control whose structure is intentionally changed to achieve the desired performance.”) (Pg. 807, Right column: “nonlinear functions gis and fis are defined”) (Pg. 806, Right column: “This is accomplished by introducing a linear combination of sliding surfaces for each subsystem as a coupled sliding surface for whole system and applying Lyapunov stability theorem.”) (Pg. 808, Left column: “For the first subsystem (6), consider a Lyapunov function”).
Cline/Karpman and Park are analogous to the claimed invention because they are in the same field of nonlinear control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Park on using a Lyapunov framework with the teachings from Cline/Karpman on the sub-engine. The motivation to combine would have been that doing so allows designing a stable controller in a nonlinear system (Park, Pg. 806, Right column: “By considering the zero-dynamics due to uncontrolled subsystem and defining a 'coupled' sliding surface, we design a controller for the whole system by coupling each individual controller. This is accomplished by introducing a linear combination of sliding surfaces for each subsystem as a coupled sliding surface for whole system and applying Lyapunov stability theorem.”) (Park, Pg. 810, Left column: “These controller are coupled in a way that the defined coupled sliding surface is stable in a Lyapunov sense.”).
Therefore, the combination of Cline/Karpman and Park teaches
wherein the sub-engine is configured to use a Lyapunov framework (Cline, [0016]: “A controller may include a control module, a closed-loop reference module, and an adaptation module.”) (Park, Pg. 806, Right column: “This is accomplished by introducing a linear combination of sliding surfaces for each subsystem as a coupled sliding surface for whole system and applying Lyapunov stability theorem.”) (Park, Pg. 808, Left column: “For the first subsystem (6), consider a Lyapunov function”).
Regarding claim 14, Cline/Karpman does not explicitly teach optimally operating in stable zero-dynamics regimes.
However, Park teaches designing a controller for a nonlinear system that stably operating in stable zero-dynamics regimes (Pg. 806, Right column: “By considering the zero-dynamics due to uncontrolled subsystem and defining a 'coupled' sliding surface, we design a controller for the whole system by coupling each individual controller.”) (Pg. 808, Right column: “As a consequence, the subsystem (6) can be stabilized from the global domain of attraction and the resulting zero dynamics is as follows”) (Pg. 810, Left column: “These controller are coupled in a way that the defined coupled sliding surface is stable in a Lyapunov sense.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Park on designing a controller for a nonlinear system that stably operating in stable zero-dynamics regimes with the teachings from Cline/Karpman on the controller/sub-engine. The motivation to combine would have been that doing so allows designing a stable controller in a nonlinear system (Park, Pg. 806, Right column: “By considering the zero-dynamics due to uncontrolled subsystem and defining a 'coupled' sliding surface, we design a controller for the whole system by coupling each individual controller. This is accomplished by introducing a linear combination of sliding surfaces for each subsystem as a coupled sliding surface for whole system and applying Lyapunov stability theorem.”) (Park, Pg. 810, Left column: “These controller are coupled in a way that the defined coupled sliding surface is stable in a Lyapunov sense.”).
Therefore, the combination of Cline/Karpman and Park teaches
wherein the sub-engine is configured to operate in stable zero-dynamics regimes (Cline, [0016]: “A controller may include a control module, a closed-loop reference module, and an adaptation module.”) (Park, Pg. 806, Right column: “By considering the zero-dynamics due to uncontrolled subsystem and defining a 'coupled' sliding surface, we design a controller for the whole system by coupling each individual controller.”) (Park, Pg. 808, Right column: “As a consequence, the subsystem (6) can be stabilized from the global domain of attraction and the resulting zero dynamics is as follows”) (Park, Pg. 810, Left column: “These controller are coupled in a way that the defined coupled sliding surface is stable in a Lyapunov sense.”).
Regarding claim 15, Cline/Karpman does not explicitly teach stable adaptation at the sacrifice of convergence speed.
However, Park teaches that a nonlinear system can have a stable adaptation at the sacrifice of convergence speed (Pg. 806: “The stabilization results are global but showing slow convergence.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Park on designing a controller for a nonlinear system that stably operating in stable zero-dynamics regimes with the teachings from Cline/Karpman on the controller/sub-engine. The motivation to combine would have been that doing so allows designing a stable controller in a nonlinear system (Park, Pg. 806: “The stabilization results are global but showing slow convergence.”).
Therefore, the combination of Cline/Karpman and Park teaches
wherein the sub-engine is configured for stable adaptation at the sacrifice of convergence speed (Cline, [0016]: “A controller may include a control module, a closed-loop reference module, and an adaptation module.”) (Park, Pg. 806: “The stabilization results are global but showing slow convergence.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Crncich-DeWolf et al. (US20160147201A1) discloses implementing parameters as a tensor
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/HEIN JEONG/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186