Optimal control for a class of unknown nonlinear systems via the iterative GDHP algorithm

Authors:
Ding Wang;Derong Liu
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

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Abstract

Using the neural-network-based iterative adaptive dynamic programming (ADP) algorithm, an optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is proposed in this paper. The optimal controller is designed with convergence analysis in terms of cost function and control law. In order to implement the algorithm via globalized dual heuristic programming (GDHP) technique, a neural network is constructed first to identify the unknown nonlinear system, and then two other neural networks are used to approximate the cost function and the control law, respectively. An example is provided to verify the effectiveness of the present approach.