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

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 th...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 61; no. 12; pp. 4170 - 4175
Main Authors Bian, Tao, Jiang, Yu, Jiang, Zhong-Ping
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Adaptive dynamic programming
adaptive optimal control
Adaptive systems
Algorithm design and analysis
Algorithms
Convergence
Dynamic programming
Noise control
Optimal control
Robustness
Stability analysis
Stochastic processes
Stochastic systems
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ISSN0018-9286
1558-2523
DOI10.1109/TAC.2016.2550518

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Summary: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.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2550518