• Data-driven optimal tracking c...

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

Title:
Data-driven optimal tracking control for nonlinear systems with performance constraints via adaptive dynamic programming.
Authors:
Zhang L; College of Information Science and Engineering, Northeastern University, China. Electronic address: zlulu2022@126.com., Zhang H; College of Information Science and Engineering, Northeastern University, China; State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), China. Electronic address: hgzhang@ieee.org., Yue X; College of Information Science and Engineering, Northeastern University, China. Electronic address: yxhui0915@126.com., Wang T; College of Information Science and Engineering, Northeastern University, China. Electronic address: wangtianbiao96@163.com.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2025 Nov; Vol. 191, pp. 107852. Date of Electronic Publication: 2025 Jul 09.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Neural Networks, Computer* , Nonlinear Dynamics*, Algorithms ; Computer Simulation ; Dynamic Programming
Contributed Indexing:
Keywords: Adaptive dynamic programming; Data-driven policy iteration; Neural networks; Prescribed performance
Entry Date(s):
Date Created: 20250718 Date Completed: 20250906 Latest Revision: 20250908
Update Code:
20250908
DOI:
10.1016/j.neunet.2025.107852
PMID:
40680337
Database:
MEDLINE

Weitere Informationen

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.
(Copyright © 2025. Published by Elsevier Ltd.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.