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 .
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Priority
Acknowledgment is made of applicant's claim for foreign priority based on a application 2022113372844 filed in China on October 28, 2022.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1 – 10 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1- 10 of copending Application No. 18/495,300. This is a provisional nonstatutory double patenting rejection as the claims have not been patented. Although the claims at issue are not identical, they are not patentably distinct from each other because they are simple changes of a statutory category. A person of ordinary skill in the art would conclude that the invention defined in the claims at issue would have been an obvious variation of the invention defined in the claims of the ‘300 application. Comparisons of selected claims 1 – 2 in the instant application are shown in the following table. A mapping of claims to those disclosed in the ‘300 application is as shown in the table; and claims 3 - 10 could also be mapped to similar claims in the patent.
Claims in instant App: 18/495,451
Claims in US App. 18/495,300
1. A method of partial-form model-free adaptive disturbance compensation control in the presence of measurable disturbances, executed on a hardware platform for controlling a controlled plant subject to measurable disturbances, said controlled plant being a multi-input
multi-output (MIMO) system with a predetermined number of control inputs and a predetermined number of system outputs, said method comprising:
step 1: obtaining measurable disturbances at time k, establishing a dynamic data model of said
controlled plant subject to measurable disturbances, wherein said dynamic data model is described by a pseudo Jacobian input matrix 0 ( k) and a pseudo Jacobian disturbance matrix x(k);
step 2: constructing cost functions and solving optimization problems for said cost functions to
find an optimal value of said pseudo Jacobian input matrix 0 ( k) in said step I and an optimal value of said pseudo Jacobian disturbance matrix x(k) in said step 1;
step 3: utilizing said measurable disturbances at time k, employing said dynamic data model
described by said optimal value of said pseudo Jacobian input matrix 0 ( k) and said optimal value of said pseudo Jacobian disturbance matrix x ( k) in said step 2, designing a partial-form
model-free adaptive disturbance compensation control law in the presence of measurable disturbances, wherein said control law comprising a partial-form adaptive input matrix nP (k) and a partial-form adaptive disturbance matrix cop (k);
step 4: constructing an energy function and solving said energy function by usmg a momentum gradient descent method to find an optimal value of said partial-form adaptive input matrix nP (k) in said step 3 and an optimal value of said partial-form adaptive disturbance matrix cop (k) in said step 3;
step 5: controlling said controlled plant by using said partial-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances with said optimal value of partial-form adaptive input matrix nP ( k) and said optimal value of partial-form
adaptive disturbance matrix co P ( k) in said step 4, weakening the effect of measurable disturbances on actual system outputs of said controlled plant, achieving effective tracking of
desired system outputs of said controlled plant.
1. A method of compact-form model-free adaptive disturbance compensation control in the presence of measurable disturbances, executed on a hardware platform for controlling a controlled plant subject to measurable disturbances, said controlled plant being a multi-input multi-output (MIMO) system with a predetermined number of control inputs and a predetermined number of system outputs, said method comprising:
step 1: obtaining measurable disturbances at time k, establishing a dynamic data model of said controlled plant subject to measurable disturbances, wherein said dynamic data model is described by a pseudo Jacobian input matrix θ(k) and a pseudo Jacobian disturbance matrix χ(k);
step 2: constructing cost functions and solving optimization problems for said cost functions to find an optimal value of said pseudo Jacobian input matrix θ(k) in said step 1 and an optimal value of said pseudo Jacobian disturbance matrix χ(k) in said step 1;
step 3: utilizing said measurable disturbances at time k, employing said dynamic data model described by said optimal value of said pseudo Jacobian input matrix θ(k) and said optimal value of said pseudo Jacobian disturbance matrix χ(k) in said step 2, designing a compact-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances, wherein said control law comprising a compact-form adaptive input matrix π.sub.c(k) and a compact-form adaptive disturbance matrix ω.sub.c(k);
step 4: constructing an energy function and solving said energy function by using a momentum gradient descent method to find an optimal value of said compact-form adaptive input matrix π.sub.c(k) in said step 3 and an optimal value of said compact-form adaptive disturbance matrix ω.sub.c(k) in said step 3;
step 5: controlling said controlled plant by using said compact-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances with said optimal value of compact-form adaptive input matrix π.sub.c(k) and said optimal value of compact-form adaptive disturbance matrix ω.sub.c(k) in said step 4, weakening the effect of measurable disturbances on actual system outputs of said controlled plant, achieving effective tracking of desired system outputs of said controlled plant.
2. The method as claimed in claim 1 wherein said step 1, obtaining measurable disturbances at time k, establishing a dynamic data model of said controlled plant subject to measurable disturbances as
Δy(k+1)=θ(k)Δu(k)+χ(k)Δd(k) where k is a sampling time, k is a positive integer; y (k+1) is an actual system output vector of said controlled plant at time k+1, y(k+1)=[y.sub.1(k+1), . . . , y.sub.n(k +1)].sup.T, Δy(k+1)=y(k+1)−y(k); n is a total number of system outputs in said controlled plant, n is a positive integer greater than 1; u(k) is a control input vector of said controlled plant at time k,
u(k)=[u.sub.1(k), . . . , μ.sub.m(k)].sup.T, Δu(k)=u(k)−u(k−1); m is a total number of control inputs in said controlled plant, m is a positive integer greater than 1; d(k) is a measurable disturbance vector in said controlled plant at time k, d(k)=[d.sub.1(k), . . . , d.sub.q(k)].sup.T, Δd(k)=d(k)−d(k−1); q is a total number of measurable disturbances in said controlled plant, q is a positive integer; θk) is said pseudo Jacobian input matrix at time k and χ(k) is said pseudo Jacobian disturbance matrix at time k.
2. The method as claimed in claim 1 wherein said step 1, obtaining measurable disturbances at time k, establishing a dynamic data model of said controlled plant subject to measurable disturbances as
Δy(k+1)=θ(k)Δu(k)+χ(k)Δd(k) where k is a sampling time, k is a positive integer; y(k+1) is an actual system output vector of said controlled plant at time k+1, y(k+1)=[y.sub.1 (k+1), . . . ,y.sub.n(k+1)].sup.T, y(k+1)=y(k+1)−y(k); n is a total number of system outputs in said controlled plant, n is a positive integer greater than 1; u(k) is a control input vector of said controlled plant at time k,
u(k)=[u.sub.1(k), . . . ,u.sub.n(k)].sup.T, Δu(k)=u(k)−u(k−1); m is a total number of control inputs in said controlled plant, m is a positive integer greater than 1; d(k) is a measurable disturbance vector in said controlled plant at time k, d (k)=[d.sub.1(k), . . . , d.sub.q (k)].sup.T, Δd(k)=d(k)−d(k−1); q is a total number of measurable disturbances in said controlled plant, q is a positive integer; θ(k) is said pseudo Jacobian input matrix at time k and χ(k) is said pseudo Jacobian disturbance matrix at time k.
Claims 1 – 10 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1- 10 of copending Application No. 18/495,529. This is a provisional nonstatutory double patenting rejection as the claims have not been patented. Although the claims at issue are not identical, they are not patentably distinct from each other because they are simple changes of a statutory category. A person of ordinary skill in the art would conclude that the invention defined in the claims at issue would have been an obvious variation of the invention defined in the claims of the ‘529 application. Comparisons of selected claims 1 – 2 in the instant application are shown in the following table. A mapping of claims to those disclosed in the ‘529 application is as shown in the table; and claims 3 - 10 could also be mapped to similar claims in the patent.
Claims in instant App: 18/495,451
Claims in US App. 18/495,529
1. A method of partial-form model-free adaptive disturbance compensation control in the presence of measurable disturbances, executed on a hardware platform for controlling a controlled plant subject to measurable disturbances, said controlled plant being a multi-input
multi-output (MIMO) system with a predetermined number of control inputs and a predetermined number of system outputs, said method comprising:
step 1: obtaining measurable disturbances at time k, establishing a dynamic data model of said
controlled plant subject to measurable disturbances, wherein said dynamic data model is described by a pseudo Jacobian input matrix 0 ( k) and a pseudo Jacobian disturbance matrix x(k);
step 2: constructing cost functions and solving optimization problems for said cost functions to
find an optimal value of said pseudo Jacobian input matrix 0 ( k) in said step I and an optimal value of said pseudo Jacobian disturbance matrix x(k) in said step 1;
step 3: utilizing said measurable disturbances at time k, employing said dynamic data model
described by said optimal value of said pseudo Jacobian input matrix 0 ( k) and said optimal value of said pseudo Jacobian disturbance matrix x ( k) in said step 2, designing a partial-form
model-free adaptive disturbance compensation control law in the presence of measurable disturbances, wherein said control law comprising a partial-form adaptive input matrix nP (k) and a partial-form adaptive disturbance matrix cop (k);
step 4: constructing an energy function and solving said energy function by usmg a momentum gradient descent method to find an optimal value of said partial-form adaptive input matrix nP (k) in said step 3 and an optimal value of said partial-form adaptive disturbance matrix cop (k) in said step 3;
step 5: controlling said controlled plant by using said partial-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances with said optimal value of partial-form adaptive input matrix nP ( k) and said optimal value of partial-form
adaptive disturbance matrix co P ( k) in said step 4, weakening the effect of measurable disturbances on actual system outputs of said controlled plant, achieving effective tracking of
desired system outputs of said controlled plant.
A method of partial-form model-free adaptive disturbance compensation control in the presence of unmeasurable disturbances, executed on a hardware platform for controlling a controlled plant subject to unmeasurable disturbances, said controlled plant being a multi-input multi-output (MIMO) system with a predetermined number of control inputs and a predetermined number of system outputs, said method comprising:
step 1: at time k, establishing a dynamic data model of said controlled plant subject to unmeasurable disturbances, wherein said dynamic data model is described by a pseudo Jacobian input matrix θ(k) and a pseudo Jacobian disturbance matrix χ(k);
step 2: constructing cost functions and solving optimization problems for said cost functions to find an optimal value of said pseudo Jacobian input matrix θ(k) in said step 1 and an optimal value of said pseudo Jacobian disturbance matrix χ(k) in said step 1;
step 3: employing said dynamic data model described by said optimal value of said pseudo Jacobian input matrix θ(k) and said optimal value of said pseudo Jacobian disturbance matrix χ(k) in said step 2, designing a partial-form model-free adaptive disturbance compensation control law in the presence of unmeasurable disturbances, wherein said control law comprising a partial-form adaptive input matrix π.sub.p(k) and a partial-form adaptive disturbance matrix ω.sub.p(k);
step 4: constructing an energy function and solving said energy function by using a momentum gradient descent method to find an optimal value of said partial-form adaptive input matrix π.sub.p(k) in said step 3 and an optimal value of said partial-form adaptive disturbance matrix ω.sub.p(k) in said step 3;
step 5: controlling said controlled plant by using said partial-form model-free adaptive disturbance compensation control law in the presence of unmeasurable disturbances with said optimal value of partial-form adaptive input matrix π.sub.p(k) and said optimal value of partial-form adaptive disturbance matrix ω.sub.p(k) in said step 4, weakening the effect of unmeasurable disturbances on actual system outputs of said controlled plant, achieving effective tracking of desired system outputs of said controlled plant
2. The method as claimed in claim 1 wherein said step 1, obtaining measurable disturbances at time k, establishing a dynamic data model of said controlled plant subject to measurable disturbances as
Δy(k+1)=θ(k)Δu(k)+χ(k)Δd(k) where k is a sampling time, k is a positive integer; y (k+1) is an actual system output vector of said controlled plant at time k+1, y(k+1)=[y.sub.1(k+1), . .
. , y.sub.n(k +1)].sup.T, Δy(k+1)=y(k+1)−y(k); n is a total number of system outputs in said controlled plant, n is a positive integer greater than 1; u(k) is a control input vector of said controlled plant at time k,
u(k)=[u.sub.1(k), . . . , μ.sub.m(k)].sup.T, Δu(k)=u(k)−u(k−1); m is a total number of control inputs in said controlled plant, m is a positive integer greater than 1; d(k) is a measurable disturbance vector in said controlled plant at time k, d(k)=[d.sub.1(k), . . . , d.sub.q(k)].sup.T, Δd(k)=d(k)−d(k−1); q is a total number of measurable disturbances in said controlled plant, q is a positive integer; θk) is said pseudo Jacobian input matrix at time k and χ(k) is said pseudo Jacobian disturbance matrix at time k.
2. The method as claimed in claim 1 wherein said step 1, at time k, establishing a dynamic data model of said controlled plant subject to unmeasurable disturbances as
Δy(k+1)=θ(k)Δu(k)+χ(k).Math.1.sub.q×1 where k is a sampling time, k is a positive integer; y(k+1) is an actual system output vector of said controlled plant at time k+1, y(k+1)=[y.sub.1(k+1), . . . ,
y.sub.n(k+1)].sup.T, Δy(k+1)=y(k+1)−y(k); n is a total number of system outputs in said controlled plant, n is a positive integer greater than 1; u(k) is a control input vector of said controlled plant at time k, u(k)=[u.sub.1(k), . . .
, u.sub.m(k)].sup.T, Δu(k)=u(k)−u(k−1); m is a total number of control inputs in said controlled plant, m is a positive integer greater than 1; 1.sub.q×1=[1; 1; . . . ; 1].sub.q×1, is a total number of unmeasurable disturbances in said controlled plant, q is a positive integer; θ(k) is said pseudo Jacobian input matrix at time k and χ(k) is said pseudo Jacobian disturbance matrix at time k.
Claim Rejections - 35 USC § 112
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.
Claim 3 is rejected under 35 USC 112(b). Claim 3 recites the limitation “where U.sub.1 is the first weighting factor…” There is insufficient antecedent basis for this limitation in the claim and should read “where U.sub.1 is a first weighting factor.” Also Claim 3 recites the limitation “where U.sub.2 is the second weighting factor…” There is insufficient antecedent basis for this limitation in the claim and should read “where U.sub.2 is a second weighting factor.” Appropriate action is required.
Claim 5 is rejected under 35 USC 112(b). Claim 5 recites the limitation “where σ.sub.1 is the first learning rate…” There is insufficient antecedent basis for this limitation in the claim and should read “where σ.sub.1 is a first learning rate.” Also claim 5 recites the limitation “where n.sub.1 is the first momentum factor …” There is insufficient antecedent basis for this limitation in the claim and should read “where n.sub.1 is a first momentum factor.” Also in claim 5 recites the limitation “where σ.sub.2 is the second learning rate…” There is insufficient antecedent basis for this limitation in the claim and should read “where σ.sub.2 is a second learning rate.” Also claim 5 recites the limitation “where n.sub.2 is the second momentum factor …” There is insufficient antecedent basis for this limitation in the claim and should read “where n.sub.2 is a second momentum factor. Appropriate action is required.
Allowable Subject Matter
Claims 1-10 are allowable over the prior art of record pending resolving all intervening issues such as the non-statutory double patenting rejections and the 35 U.S.C. §112(b) rejections above. In so far as a possible 35 USC 101 rejection concerning an abstract idea for mathematical concept: The claim recites several mathematical formulas or calculations such as Jacobian input matrix and partial-form adaptive disturbance matrix. However, the claim contains a combination of additional elements and uses the mathematical formulas and calculations in a specific manner that sufficiently limits the use of the mathematical concepts to the practical application of controlling said plant as stated in step 5 of claim 1. Thus the claim contains eligible subject matter. Reasons for allowance will be held in abeyance pending final recitation of the claims. The prior art does not disclose the elements of the single independent claim 1.
The closest prior art is the Chinese Patent document Ren et al. (CN 109814386 A), herein “Ren.” Ren teaches a method of partial-form model-free adaptive disturbance compensation control in the presence of measurable disturbances, (Abstract: “…one part is used for total disturbance compensation system…”) executed on a hardware platform for controlling a controlled plant (industrial robot) subject to measurable disturbances, (Abstract: “…the other part is used for robot trajectory tracking control, proportional plus derivative feedback.”)
said controlled plant being a multi-input multi-output (MIMO) system with a predetermined number of control inputs and a predetermined number of system outputs, said method comprising: (Page 2, Par. 1: “…since omnidirectional mobile robot system to be the input of nonlinear and strong coupling multi-output system…”)
step 1: obtaining measurable disturbances at time k, establishing a dynamic data model of said controlled plant subject to measurable disturbances, wherein said dynamic data model is described by a pseudo Jacobian input matrix θ(k) and a pseudo Jacobian disturbance matrix χ(k); (Claim 1: “A robot track compensation based on model tracking auto-disturbance-rejection control method, step is as follows: step one: establishing omnidirectional mobile robot system dynamics model defined in the world coordinate system (W) and moving coordinate system (M);… “…according to dynamic model formula (1) design a tension status observer defined sampling time is T, the k-th time pose of the robot is q (k), the k-th control input of time as u (k)….”)
Ren does not teach a cost function, nor step 4 of solving an energy function using a momentum gradient, nor solving optimization problems for the cost function using a pseudo Jacobian input matrix according to the instant application step 2. Ren uses an optimal control algorithm, but does not control a plant (or the robot) by using said partial-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances with said optimal value of partial-form adaptive input matrix…
Ren also does not teach a Jacobian disturbance matrix and also does not teach step 5 wherein: controlling said controlled plant by using said partial-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances with said optimal value of partial-form adaptive input matrix π.sub.p(k) and said optimal value of partial-form adaptive disturbance matrix ω.sub.p(k) in said step 4, weakening the effect of measurable disturbances on actual system outputs of said controlled plant, achieving effective tracking of desired system outputs of said controlled plant.
Other prior art teaches compensation disturbance and control of an industrial device such as a robot and Jacobian matrix. Herr et al. (US PG Pub. No. 20230390087) teaches these elements similar to the instant application. Herr in paragraph 0013 teaches: “A prediction error in the predicted values of the signal with reference to the values of the signals detected at the sensors is computed. A prediction error Jacobian matrix is calculated by analytically computing elements of the prediction error Jacobian matrix with respect to the estimated parameters of the sensors for each measurement and, from the prediction error and the prediction error Jacobian matrix, a state of the parameters of the sensors is determined.” Paragraphs 0066 and 0075 teach the disturbance compensation. However, these paragraphs only touch on the commonality of the instant application and Herr does not teach the detailed elements of steps 1 – 5. Specifically Herr does not teach a cost function, step 4 of solving an energy function using a momentum gradient, and solving optimization problems for the cost function using a pseudo Jacobian input matrix.
No other prior art could be found that specifically teaches steps 1 – 5 of the instant application.
Conclusion
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116