Data-driven adaptive optimal control of linear uncertain systems with unknown jumping dynamics

Meng Zhang, Ming Gang Gan, Jie Chen, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper focuses on the optimal control of a DC torque motor servo system which represents a class of continuous-time linear uncertain systems with unknown jumping internal dynamics. A data-driven adaptive optimal control strategy based on the integration of adaptive dynamic programming (ADP) and switching control is presented to minimize a predefined cost function. This takes the first step to develop switching ADP methods and extend the application of ADP to time-varying systems. Moreover, an analytical method to give the initial stabilizing controller for policy iteration ADP is proposed. It is shown that under the proposed adaptive optimal control law, the closed-loop switched system is asymptotically stable at the origin. The effectiveness of the strategy is validated via simulations on the DC motor system model.

Original languageEnglish (US)
Pages (from-to)6087-6105
Number of pages19
JournalJournal of the Franklin Institute
Volume356
Issue number12
DOIs
StatePublished - Aug 2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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