Adaptive critic designs: a case study for neurocontrol
Neural Networks
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy)
Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy)
IEEE Transactions on Neural Networks
Reinforcement learning and adaptive dynamic programming for feedback control
IEEE Circuits and Systems Magazine
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issues on Stability of ADP Feedback Controllers for Dynamical Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatica (Journal of IFAC)
An optimal tracking neuro-controller for nonlinear dynamic systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Online learning control by association and reinforcement
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A self-learning call admission control scheme for CDMA cellular networks
IEEE Transactions on Neural Networks
Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this paper, an optimal tracking control scheme is proposed for a class of unknown discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) algorithm. First, in order to obtain the dynamics of the system, an identifier is constructed by a three-layer feedforward neural network (NN). Second, a feedforward neuro-controller is designed to get the desired control input of the system. Third, via system transformation, the original tracking problem is transformed into a regulation problem with respect to the state tracking error. Then, the iterative ADP algorithm based on heuristic dynamic programming is introduced to deal with the regulation problem with convergence analysis. In this scheme, feedforward NNs are used as parametric structures for facilitating the implementation of the iterative algorithm. Finally, simulation results are also presented to demonstrate the effectiveness of the proposed scheme.