Stochastic and adaptive optimal control of uncertain interconnected systems

A data-driven approach

Research output: Contribution to journalArticle

Abstract

This paper provides a novel non-model-based, data-driven stochastic H control design for linear continuous-time stochastic interconnected systems with unknown dynamics. Our contributions are three-fold. First, we develop a tool to show how to assign an arbitrarily small input-to-output stochastic L2 gain of the closed-loop system, by combining the gain assignment technique with the zero-sum dynamic game-based H control design. Second, robustness to dynamic uncertainties is tackled using the small-gain theory. Third, we develop a non-model-based stochastic robust adaptive dynamic programming (RADP) algorithm for adaptive optimal controller design. In sharp contrast to the existing methods, the obtained algorithm is based on value iteration (VI), and the knowledge of an initial stabilizing control policy is no longer needed. An example of a power electronic system is adopted to illustrate the obtained results.

Original languageEnglish (US)
Pages (from-to)48-54
Number of pages7
JournalSystems and Control Letters
Volume115
DOIs
StatePublished - May 1 2018

Fingerprint

Large scale systems
Power electronics
Dynamic programming
Closed loop systems
Controllers
Uncertainty

Keywords

  • H control
  • Robust adaptive dynamic programming (RADP)
  • Small-gain
  • Stochastic system
  • Value iteration
  • Zero-sum differential game

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Cite this

Stochastic and adaptive optimal control of uncertain interconnected systems : A data-driven approach. / Bian, Tao; Jiang, Zhong-Ping.

In: Systems and Control Letters, Vol. 115, 01.05.2018, p. 48-54.

Research output: Contribution to journalArticle

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