Neural-Network-Based Online HJB Solution for Optimal Robust Guaranteed Cost Control of Continuous-Time Uncertain Nonlinear Systems

In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 44; H. 12; S. 2834 - 2847
Hauptverfasser:	Liu, Derong, Wang, Ding, Wang, Fei-Yue, Li, Hongliang, Yang, Xiong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive critic designs adaptive/approximate dynamic programming (ADP) Algorithms Artificial Intelligence Computer Simulation Control systems Cost function Dynamical systems Equations Feedback Feedback control Hamilton-Jacobi-Bellman (HJB) equation Mathematical analysis Models, Statistical Neural networks Neural Networks (Computer) Nonlinear Dynamics Nonlinear systems Online Optimal control optimal robust guaranteed cost \hbox{control} Optimization Robustness uncertain nonlinear systems optimal robust guaranteed cost control uncertain nonlinear systems adaptive/approximate dynamic programming (ADP) Adaptive critic designs Hamilton–Jacobi–Bellman (HJB) equation neural networks
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
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