Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optim...

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Veröffentlicht in:2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) S. 242 - 249
Hauptverfasser: Derong Liu, Ding Wang, Dongbin Zhao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2011
Schlagworte:
Adaptive critic designs
adaptive dynamic programming
approximate dynamic programming
Artificial neural networks
globalized dual heuristic programming
Integrated optics
intelligent control
neural dynamic programming
neural networks
Neurons
Optimal control
Riccati equations
ISBN:1424498872, 9781424498871
ISSN:2325-1824
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Zusammenfassung:An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach.
ISBN:1424498872
9781424498871
ISSN:2325-1824
DOI:10.1109/ADPRL.2011.5967357