Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach |
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| Authors: | Ding WangAuthor Vitae Qinglai WeiAuthor Vitae |
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| Affiliation: | a State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China
b Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL 60607, USA |
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| Abstract: | In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming (ADP) algorithm via heuristic dynamic programming (HDP) technique is introduced to obtain the finite-horizon optimal tracking controller which makes the cost function close to its optimal value within an ?-error bound. Three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics, respectively. Two simulation examples are included to complement the theoretical discussions. |
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| Keywords: | Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Finite-horizon optimal tracking control Learning control Neural networks Reinforcement learning |
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