Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics

Research output: Contribution to journalArticle

Abstract

This paper presents a novel policy iteration approach for finding online adaptive optimal controllers for continuous-time linear systems with completely unknown system dynamics. The proposed approach employs the approximate/adaptive dynamic programming technique to iteratively solve the algebraic Riccati equation using the online information of state and input, without requiring the a priori knowledge of the system matrices. In addition, all iterations can be conducted by using repeatedly the same state and input information on some fixed time intervals. A practical online algorithm is developed in this paper, and is applied to the controller design for a turbocharged diesel engine with exhaust gas recirculation. Finally, several aspects of future work are discussed.

Original languageEnglish (US)
Pages (from-to)2699-2704
Number of pages6
JournalAutomatica
Volume48
Issue number10
DOIs
StatePublished - Oct 2012

Fingerprint

Linear systems
Exhaust gas recirculation
Controllers
Riccati equations
Dynamic programming
Diesel engines
Dynamical systems

Keywords

  • Adaptive optimal control
  • Linear-quadratic regulator (LQR)
  • Policy iterations

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. / Jiang, Yu; Jiang, Zhong-Ping.

In: Automatica, Vol. 48, No. 10, 10.2012, p. 2699-2704.

Research output: Contribution to journalArticle

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