ADP-based adaptive optimal tracking of strict-feedback nonlinear systems

Weinan Gao, Zhong-Ping Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
Volume2018-January
ISBN (Electronic)9781538627259
DOIs
StatePublished - Feb 2 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: Nov 27 2017Dec 1 2017

Other

Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period11/27/1712/1/17

Fingerprint

Adaptive Dynamics
Feedback Systems
Dynamic programming
Dynamic Programming
Nonlinear systems
Hamilton-Jacobi-Bellman Equation
Nonlinear Systems
Feedback
Data-driven
Iterative methods
Output Regulation
Policy Iteration
Positive Definite Functions
Dynamical systems
Stabilization
Positive semidefinite
Convergence Analysis
System Dynamics
Iteration

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

Cite this

Gao, W., & Jiang, Z-P. (2018). ADP-based adaptive optimal tracking of strict-feedback nonlinear systems. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (Vol. 2018-January, pp. 1-8). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8280834

ADP-based adaptive optimal tracking of strict-feedback nonlinear systems. / Gao, Weinan; Jiang, Zhong-Ping.

2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gao, W & Jiang, Z-P 2018, ADP-based adaptive optimal tracking of strict-feedback nonlinear systems. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 11/27/17. https://doi.org/10.1109/SSCI.2017.8280834
Gao W, Jiang Z-P. ADP-based adaptive optimal tracking of strict-feedback nonlinear systems. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-8 https://doi.org/10.1109/SSCI.2017.8280834
Gao, Weinan ; Jiang, Zhong-Ping. / ADP-based adaptive optimal tracking of strict-feedback nonlinear systems. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-8
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