Data-driven optimal tracking control for nonlinear systems with performance constraints via adaptive dynamic programming

This paper studies the optimal tracking problem for an unknown nonlinear systems subject to input and performance constraints. A data-driven constrained optimal tracking control scheme is designed to make the system states pursue the desired trajectory while minimizing the cost and strictly limiting...

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Vydané v:Neural networks Ročník 191; s. 107852
Hlavní autori: Zhang, Lulu, Zhang, Huaguang, Yue, Xiaohui, Wang, Tianbiao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.11.2025
Predmet:
Adaptive dynamic programming
Algorithms
Computer Simulation
Data-driven policy iteration
Dynamic Programming
Neural networks
Neural Networks, Computer
Nonlinear Dynamics
Prescribed performance
Data-driven policy iteration
Prescribed performance
Adaptive dynamic programming
Neural networks
ISSN:0893-6080, 1879-2782, 1879-2782
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Shrnutí:This paper studies the optimal tracking problem for an unknown nonlinear systems subject to input and performance constraints. A data-driven constrained optimal tracking control scheme is designed to make the system states pursue the desired trajectory while minimizing the cost and strictly limiting the tracking error within thepredefined zones. Specifically, a finite-time performance function is deployed to ensure that errors converge to steady-state regions within a user-defined time. Furthermore, by employing a nonquadratic cost function, a modified Hamilton-Jacobi-Bellman equation is constructed to ensure input limitations are satisfied. Subsequently, the adaptive dynamic programming algorithm, implemented with neural networks (NNs) in an actor-critic structure, is employed to learn the optimal control policy without relying on any prior information about the system dynamics. Meanwhile, the weights of the actor-critic NNs are tuned using the least-squares method based on the collected dataset. Finally, simulations on Chua’s circuit demonstrate the effectiveness and benefits of the designed algorithm.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2025.107852