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 journalArticle

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)
JournalJournal of the Franklin Institute
DOIs
StatePublished - Jan 1 2019

Fingerprint

Adaptive Dynamics
Uncertain systems
Uncertain Systems
Dynamic programming
Data-driven
Adaptive Control
Dynamic Programming
Optimal Control
Linear Systems
Unknown
DC motors
Policy Iteration
Torque motors
Switching Control
Servo System
DC Motor
Time-varying Systems
Time varying systems
Switched Systems
Servomechanisms

ASJC Scopus subject areas

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

Cite this

Data-driven adaptive optimal control of linear uncertain systems with unknown jumping dynamics. / Zhang, Meng; Gan, Ming Gang; Chen, Jie; Jiang, Zhong-Ping.

In: Journal of the Franklin Institute, 01.01.2019.

Research output: Contribution to journalArticle

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