Neural-Network-Based Optimal Control for a Class of Unknown Discrete-Time Nonlinear Systems Using Globalized Dual Heuristic Programming

In this paper, a neuro-optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is developed. The iterative adaptive dynamic programming algorithm using globalized dual heuristic programming technique is introduced to obtain the optimal...

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Veröffentlicht in:	IEEE transactions on automation science and engineering Jg. 9; H. 3; S. 628 - 634
Hauptverfasser:	Liu, Derong, Wang, Ding, Zhao, Dongbin, Wei, Qinglai, Jin, Ning
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway, NJ IEEE 01.07.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive dynamic programming Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences approximate dynamic programming Computer science; control theory; systems Computer systems and distributed systems. User interface Control system synthesis Control theory. Systems Controllers Convergence Discrete time systems Dynamic programming Exact sciences and technology globalized dual heuristic programming Heuristic Intelligent control Neural networks Optimal control Software Theoretical computing Adaptive algorithm Control program neural networks Non linear control Iterative method Heuristic programming Efficiency Cost function Dynamic programming Optimal control (mathematics) Generic programming Numerical convergence Intelligent control approximate dynamic programming Adaptive dynamic programming globalized dual heuristic programming Neural network Non linear system Cost control Neurocontrollers Optimal control Discrete time Distributed control Function derivative Derivative control
ISSN:	1545-5955, 1558-3783
Online-Zugang:	Volltext
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