Data-Driven Optimal Tracking Control for Discrete-Time Nonlinear Systems With Unknown Dynamics Using Deterministic ADP

This article aims to solve the optimal tracking problem (OTP) for a class of discrete-time (DT) nonlinear systems with completely unknown dynamics. A novel data-driven deterministic approximate dynamic programming (ADP) algorithm is proposed to solve this kind of problem with only input-output (I/O)...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 36; no. 1; pp. 1184 - 1198
Main Authors: Song, Shijie, Gong, Dawei, Zhu, Minglei, Zhao, Yuyang, Huang, Cong
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2025
Subjects:
Approximate dynamic programming (ADP)
Approximation algorithms
Artificial neural networks
Cost function
data-driven
Heuristic algorithms
neural network (NN)
Nonlinear dynamical systems
Optimal control
optimal tracking problem (OTP)
Q-learning
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:This article aims to solve the optimal tracking problem (OTP) for a class of discrete-time (DT) nonlinear systems with completely unknown dynamics. A novel data-driven deterministic approximate dynamic programming (ADP) algorithm is proposed to solve this kind of problem with only input-output (I/O) data. The proposed algorithm has two advantages compared to existing data-driven deterministic ADP algorithms for the OTP. First, our algorithm can guarantee optimality while achieving better performance in the aspects of time-saving and robustness to data. Second, the near-optimal control policy learned by our algorithm can be implemented without considering expected control and enable the system states to track the user-specified reference signals. Therefore, the tracking performance is guaranteed while simplifying the algorithm implementation. Furthermore, the convergence and stability of the proposed algorithm are strictly proved through theoretical analysis, in which the errors caused by neural networks (NNs) are considered. At the end of this article, the developed algorithm is compared with two representative deterministic ADP algorithms through a numerical example and applied to solve the tracking problem for a two-link robotic manipulator. The simulation results demonstrate the effectiveness and advantages of the developed algorithm.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3323142