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Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

Weinan Gao, Zhong-Ping Jiang, Weinan Gao

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    |May 18, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a new data-driven method for adaptive optimal tracking in nonlinear systems. It uses adaptive dynamic programming and nonlinear output regulation to create trackers without prior system knowledge.

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    Area of Science:

    • Control Theory
    • Machine Learning
    • Nonlinear Systems

    Background:

    • Adaptive optimal tracking is crucial for nonlinear systems but challenging due to unknown dynamics.
    • Existing methods struggle with non-positive definite Hamilton-Jacobi-Bellman (HJB) equations.

    Purpose of the Study:

    • To develop a novel data-driven control approach for adaptive optimal tracking in strict-feedback nonlinear systems.
    • To integrate Adaptive Dynamic Programming (ADP) and nonlinear output regulation for near-optimal tracking without system knowledge.

    Main Methods:

    • A novel policy iteration technique is proposed for solving positive semidefinite HJB equations.
    • A two-phase data-driven learning method is developed and implemented online using ADP.
    • Rigorous convergence analysis is provided for the proposed policy iteration method.

    Main Results:

    • The study successfully computes an adaptive near-optimal tracker for nonlinear systems.
    • The proposed method overcomes limitations of existing iterative techniques for HJB equations.
    • The approach is validated using a Van der Pol oscillator with time-varying signals.

    Conclusions:

    • The proposed data-driven adaptive optimal tracking control is effective for nonlinear systems.
    • This work offers a new perspective on solving HJB equations in adaptive control.
    • The integration of ADP and nonlinear output regulation provides a powerful framework.