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 .
Status of the Application
2. Claims 1-10 have been examined in this application. This communication is the first action on the merits.
Drawings
3. The drawing filed on 10/26/23 is acceptable for examination proceedings.
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.
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.
4. Claim 1 recites the limitation "the presence of measurable disturbances";
Claim 3 recites “the first weighting factor”, “the second weighting factor”, “the first step size”, “the second step size”;
Claim 5 recites “the first learning rate”, “the second learning rate”, “the first momentum factor”, “the second momentum factor”.
There is insufficient antecedent basis for this limitation in the claim.
Claim 2-10 are also rejected under 35 U.S.C 112(b) rejection due to their direct/indirect dependency over the claim 1.
Double Patenting
5. 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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
6. Claims 1-10 are provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-10 of co-pending Application No. 18/495,451. This is a provisional non-statutory 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 ‘451 application. Comparisons of selected claims 1 in the instant application are shown in the following table. A mapping of claims to those disclosed in the ‘451 application is as shown in the table; and claims 2-10 could also be mapped to similar claims in the co-pending application.
This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1 of instant application: 18495300
Claim 1 of the application 18495451
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,
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:
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);
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 θ(k) in said step 1 and an optimal value of said pseudo Jacobian disturbance matrix χ(k) in said step 1;
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 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 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 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 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 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.
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.
7. Claim 1-10 of the instant application provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claim 1 of co-pending Application No.18495321. 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 ‘321 application. Comparisons of selected claims 1 in the instant application are shown in the following table. A mapping of claims to those disclosed in the ‘321 application is as shown in the table; and claims 2-10 could also be mapped to similar claims in the co-pending application.
This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1 of instant application: 18495300
Claim 1 of the application 18495321
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,
A method of compact-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: 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);
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 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 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 compact-form model-free adaptive disturbance compensation control law in the presence of unmeasurable 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 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.
step 5: controlling said controlled plant by using said compact-form model-free adaptive disturbance compensation control law in the presence of unmeasurable 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 unmeasurable disturbances on actual system outputs of said controlled plant, achieving effective tracking of desired system outputs of said controlled plant.
8. Claim 1-10 of the instant application provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claim 1 of co-pending Application No.18495529. 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. A mapping of claim 1 to those disclosed in the ‘529 application is as shown in the table; and claims 2-10 could also be mapped to similar claims in the co-pending application.
Claim 1 of this instant application: 18495300
Claim 1 of the application 18495529
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,
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: 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);
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 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 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 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 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 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.
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
This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
9. Claim 1-10 of the instant application provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claim 1 of co-pending Application No.18495424. 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 ‘424 application. A mapping of claim 1 to those disclosed in the ‘424 application is as shown in the table; and claims 2-10 could also be mapped to similar claims in the co-pending application.
Claim 1 of this instant application: 18495300
Claim 1 of the application 18495424
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,
A method of full-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: 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);
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 θθ(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 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 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 full-form model-free adaptive disturbance compensation control law in the presence of unmeasurable disturbances, wherein said control law comprising a full-form adaptive input matrix π.sub.f(k) and a full-form adaptive disturbance matrix ω.sub.f(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 4: constructing an energy function and solving said energy function by using a momentum gradient descent method to find an optimal value of said full-form adaptive input matrix π.sub.f(k) in said step 3 and an optimal value of said full-form adaptive disturbance matrix ω.sub.f(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.
step 5: controlling said controlled plant by using said full-form model-free adaptive disturbance compensation control law in the presence of unmeasurable disturbances with said optimal value of full-form adaptive input matrix π.sub.f(k) and said optimal value of full-form adaptive disturbance matrix ω.sub.f(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.
This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
10. Claim 1-10 of the instant application provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claim 1 of co-pending Application No.18495442. 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 ‘442 application. A mapping of claim 1 to those disclosed in the ‘442 application is as shown in the table; and claims 2-10 could also be mapped to similar claims in the co-pending application.
Claim 1 of this instant application: 18495300
Claim 1 of the application 18495442
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,
A method of full-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);
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 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 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 full-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances, wherein said control law comprising a full-form adaptive input matrix π.sub.f(k) and a full-form adaptive disturbance matrix ω.sub.f(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 4: constructing an energy function and solving said energy function by using a momentum gradient descent method to find an optimal value of said full-form adaptive input matrix π(k) in said step 3, and an optimal value of said full-form adaptive disturbance matrix ω.sub.f(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.
step 5: controlling said controlled plant by using said full-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances with said optimal value of full-form adaptive input matrix π.sub.f(k) and said optimal value of full-form adaptive disturbance matrix ω.sub.f(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.
This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Allowable Subject Matter
11. 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 as mentioned above.
The closest prior art Lu (Pub: 2020/0249659) teaches method of compact-form model-free adaptive disturbance compensation control in the presence of measurable disturbances, (FIG. 1 shows a schematic diagram according to the embodiments of the invention. For a MIMO system with m inputs (m is a positive integer greater than 1) and n outputs (n is a positive integer), the MIMO different-factor compact-form model-free control method is adopted to control the system, Para. [0030]) executed on a hardware platform for controlling a controlled plant subject to measurable disturbances, (The hardware platform for running the inventive control method is the industrial control computer, Para. [0038])
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: (FIG. 4 shows the control inputs when controlling the two-input two-output MIMO system in the first exemplary embodiment by using the inventive MIMO different-factor compact-form model-free control method; Para. [0021], Fig. 4);
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); (when a controlled plant is a MIMO system, namely a multi-input multi-output system, a mathematical formula for calculating the i-th control input u.sub.i(k) at time k using said method is as follows:
[00002]ui(k)=ui(k-1)+ρi.Math..Math.j=1n.Math.φj,i(k).Math.ej(k)λi+.Math.Φ(k).Math.2
where k is a positive integer; n is the total number of system outputs in said MIMO system, n is a positive integer; i denotes the i-th of the total number of control inputs in said MIMO system, i is a positive integer, 1≤i≤m, where m is the total number of control inputs in said MIMO system and m is a positive integer greater than 1; j denotes the j-th of the total number of system outputs in said MIMO system, j is a positive integer, 1≤j≤n; u.sub.i(k) is the i-th control input at time k; e.sub.j(k) is the j-th error at time k, namely the j-th element in the error vector e(k)=[e.sub.1(k), . . . ,e.sub.n(k)].sup.T; Φ(k) is the estimated value of pseudo partitioned Jacobian matrix for said MIMO system at time k, ϕ.sub.j,i(k) is the j-th row and the i-th column of matrix Φ(k), ∥Φ(k)∥ is the 2-norm of matrix Φ(k); λ.sub.i is the penalty factor for the i-th control input; ρ.sub.i is the step-size factor for the i-th control input; Para. [0008]-[0009]).
None of the cited prior art alone or in combination teach disclosed 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, a Jacobian disturbance matrix and also does not teach “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.”
Conclusion
Herr (US PG Pub. 2023/0390087) disclose “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” (Para. [0013]).
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/JIGNESHKUMAR C PATEL/Primary Examiner, Art Unit 2116