An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs

In this paper, the adaptive dynamic programming (ADP) approach is employed for designing an optimal controller of unknown discrete-time nonlinear systems with control constraints. A neural network is constructed for identifying the unknown dynamical system with stability proof. Then, the iterative A...

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Veröffentlicht in:Information sciences Jg. 220; S. 331 - 342
Hauptverfasser: Liu, Derong, Wang, Ding, Yang, Xiong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 20.01.2013
Schlagworte:
Adaptive dynamic programming
Approximate dynamic programming
Control constraints
Globalized dual heuristic programming
Neural networks
Optimal control
Approximate dynamic programming
Adaptive dynamic programming
Neural networks
Control constraints
Globalized dual heuristic programming
Optimal control
ISSN:0020-0255, 1872-6291
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Zusammenfassung:In this paper, the adaptive dynamic programming (ADP) approach is employed for designing an optimal controller of unknown discrete-time nonlinear systems with control constraints. A neural network is constructed for identifying the unknown dynamical system with stability proof. Then, the iterative ADP algorithm is developed to solve the optimal control problem with convergence analysis. Two other neural networks are introduced for approximating the cost function and its derivatives and the control law, under the framework of globalized dual heuristic programming technique. Furthermore, two simulation examples are included to verify the theoretical results.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2012.07.006