Finite horizon optimal tracking control for a class of discrete-time nonlinear systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Modeling and monitoring of multimodes process
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Temperature control in water-gas shift reaction with adaptive dynamic programming
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Information Sciences: an International Journal
Optimal tracking control for a class of nonlinear time-delay systems with actuator saturation
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
Optimal tracking control scheme for discrete-time nonlinear systems with approximation errors
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an -error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.