Title

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.

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