Constrained-Cost Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems
For discrete-time nonlinear systems, this research is concerned with optimal control problems (OCPs) with constrained cost, and a novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialize...
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| Vydáno v: | IEEE transaction on neural networks and learning systems Ročník 35; číslo 3; s. 1 - 14 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
IEEE
01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
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| Abstract | For discrete-time nonlinear systems, this research is concerned with optimal control problems (OCPs) with constrained cost, and a novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialized through a value function constructed by a feasible control law. It is proven that the iterative value function is nonincreasing and converges to the solution of the Bellman equation with constrained cost. The feasibility of the iterative control law is proven. The method to find the initial feasible control law is given. Implementation using neural networks (NNs) is introduced, and the convergence is proven by considering the approximation error. Finally, the property of the present VICC method is shown by two simulation examples. |
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| AbstractList | For discrete-time nonlinear systems, this research is concerned with optimal control problems (OCPs) with constrained cost, and a novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialized through a value function constructed by a feasible control law. It is proven that the iterative value function is nonincreasing and converges to the solution of the Bellman equation with constrained cost. The feasibility of the iterative control law is proven. The method to find the initial feasible control law is given. Implementation using neural networks (NNs) is introduced, and the convergence is proven by considering the approximation error. Finally, the property of the present VICC method is shown by two simulation examples. For discrete-time nonlinear systems, this research is concerned with optimal control problems (OCPs) with constrained cost, and a novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialized through a value function constructed by a feasible control law. It is proven that the iterative value function is nonincreasing and converges to the solution of the Bellman equation with constrained cost. The feasibility of the iterative control law is proven. The method to find the initial feasible control law is given. Implementation using neural networks (NNs) is introduced, and the convergence is proven by considering the approximation error. Finally, the property of the present VICC method is shown by two simulation examples.For discrete-time nonlinear systems, this research is concerned with optimal control problems (OCPs) with constrained cost, and a novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialized through a value function constructed by a feasible control law. It is proven that the iterative value function is nonincreasing and converges to the solution of the Bellman equation with constrained cost. The feasibility of the iterative control law is proven. The method to find the initial feasible control law is given. Implementation using neural networks (NNs) is introduced, and the convergence is proven by considering the approximation error. Finally, the property of the present VICC method is shown by two simulation examples. |
| Author | Wei, Qinglai Li, Tao |
| Author_xml | – sequence: 1 givenname: Qinglai orcidid: 0000-0001-7002-9800 surname: Wei fullname: Wei, Qinglai organization: Institute of Automation, State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Tao surname: Li fullname: Li, Tao organization: Institute of Automation, State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, Beijing, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37022391$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adaptive control Adaptive dynamic programming (ADP) approximate dynamic programming constrained cost Control theory Convergence Cost function Costs Discrete time systems Dynamic programming Feasibility Neural networks Nonlinear control Nonlinear systems Optimal control Performance analysis reinforcement learning value iteration (VI) |
| Title | Constrained-Cost Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems |
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