ADP and Adaptive Optimal Tracking of Strict-Feedback Non-Linear Systems
Document Type
Conference Proceeding
Publication Date
2017
Publication Title
Proceedings of the IEEE Symposium Series on Computational Intelligence
DOI
10.1109/SSCI.2017.8280834
ISBN
978-1-5386-2726-6
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 employed to compute an adaptive near-optimal tracker without 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.
Recommended Citation
Gao, Weinan, Zhong-Ping Jiang.
2017.
"ADP and Adaptive Optimal Tracking of Strict-Feedback Non-Linear Systems."
Proceedings of the IEEE Symposium Series on Computational Intelligence: 1-8 Honolulu, HI: IEEE.
doi: 10.1109/SSCI.2017.8280834 isbn: 978-1-5386-2726-6
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/143
