Global adaptive dynamic programming for continuous-time nonlinear systems

Research output: Contribution to journalArticle

Abstract

This paper presents a novel method of global adaptive dynamic programming (ADP) for the adaptive optimal control of nonlinear polynomial systems. The strategy consists of relaxing the problem of solving the Hamilton-Jacobi-Bellman (HJB) equation to an optimization problem, which is solved via a new policy iteration method. The proposed method distinguishes from previously known nonlinear ADP methods in that the neural network approximation is avoided, giving rise to significant computational improvement. Instead of semiglobally or locally stabilizing, the resultant control policy is globally stabilizing for a general class of nonlinear polynomial systems. Furthermore, in the absence of the a priori knowledge of the system dynamics, an online learning method is devised to implement the proposed policy iteration technique by generalizing the current ADP theory. Finally, three numerical examples are provided to validate the effectiveness of the proposed method.

Original languageEnglish (US)
Article number7063901
Pages (from-to)2917-2929
Number of pages13
JournalIEEE Transactions on Automatic Control
Volume60
Issue number11
DOIs
StatePublished - Nov 1 2015

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Dynamic programming
Nonlinear systems
Programming theory
Polynomials
Dynamical systems
Neural networks

Keywords

  • Adaptive dynamic programming
  • global stabilization
  • nonlinear systems
  • optimal control

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

Cite this

Global adaptive dynamic programming for continuous-time nonlinear systems. / Jiang, Yu; Jiang, Zhong-Ping.

In: IEEE Transactions on Automatic Control, Vol. 60, No. 11, 7063901, 01.11.2015, p. 2917-2929.

Research output: Contribution to journalArticle

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