Adaptive Dynamic Programming for Stochastic Systems With State and Control Dependent Noise

Tao Bian, Yu Jiang, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

In this technical note, the adaptive optimal control problem is investigated for a class of continuous-time stochastic systems subject to multiplicative noise. A novel non-model-based optimal control design methodology is employed to iteratively update the control policy on-line by using directly the data of the system state and input. Both adaptive dynamic programming (ADP) and robust ADP algorithms are developed, along with rigorous stability and convergence analysis. The effectiveness of the obtained methods is illustrated by an example arising from biological sensorimotor control.

Original languageEnglish (US)
Article number7447723
Pages (from-to)4170-4175
Number of pages6
JournalIEEE Transactions on Automatic Control
Volume61
Issue number12
DOIs
StatePublished - Dec 2016

Keywords

  • Adaptive dynamic programming
  • adaptive optimal control
  • stochastic systems

ASJC Scopus subject areas

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

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