Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems
Document Type
Article
Publication Date
11-9-2017
Publication Title
IEEE Transactions on Neural Networks and Learning Systems
DOI
10.1109/TNNLS.2017.2761718
ISSN
2162-2388
Abstract
This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.
Recommended Citation
Gao, Weinan, Zhong-Ping Jiang.
2017.
"Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems."
IEEE Transactions on Neural Networks and Learning Systems: 1-11: IEEE Xplore.
doi: 10.1109/TNNLS.2017.2761718
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/118
