A menu of designs for reinforcement learning over time
Neural networks for control
On-Line Learning Control for Discrete Nonlinear Systems Via an Improved ADDHP Method
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Adaptive Critic Designs for Discrete-Time Zero-Sum Games With Application to Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear optimal tracking control with application to super-tankers for autopilot design
Automatica (Journal of IFAC)
A self-learning call admission control scheme for CDMA cellular networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this paper, a new iterative ADP algorithm is proposed to solve the finite horizon optimal tracking control problem for a class of discrete-time nonlinear systems. The idea is that using system transformation, the optimal tracking problem is transformed into optimal regulation problem, and then the iterative ADP algorithm is introduced to deal with the regulation problem with convergence guarantee. Three neural networks are used to approximate the performance index function, compute the optimal control policy and model the unknown system dynamics, respectively, for facilitating the implementation of iterative ADP algorithm. An example is given to demonstrate the validity of the proposed optimal tracking control scheme.