Neuro-Control for Continuous-Time Stochastic Nonlinear Systems via Online Policy Iteration Algorithm

This paper is concerned with the neuro-control for continuous-time nonlinear systems subject to stochastic disturbance. Due to the stochastic disturbance, the traditional value function in existing literature cannot meet the stochastic control problems, since mixed second partial derivatives are emp...

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Veröffentlicht in:	Chinese Control and Decision Conference S. 1499 - 1503
Hauptverfasser:	Zhou, Tianmin, Hou, Jiaxu, Li, Handong, Di, Zengru, Zhao, Bo
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.08.2020
Schlagworte:	Adaptive Dynamic Programming Approximation algorithms Artificial neural networks Mathematical model Nonlinear systems Optimal control Policy Iteration Reinforcement Learning Stochastic Nonlinear Stochastic processes Stochastic systems
ISSN:	1948-9447
Online-Zugang:	Volltext
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