A menu of designs for reinforcement learning over time
Neural networks for control
IEEE Transactions on Neural Networks
Bio-inspired algorithms for autonomous deployment and localization of sensor nodes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatica (Journal of IFAC)
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
IEEE Transactions on Neural Networks
Online learning control by association and reinforcement
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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, a finite horizon iterative adaptive dynamic programming (ADP) algorithm is proposed to solve the optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state. A new @e-optimal control algorithm based on the iterative ADP approach is proposed that makes the performance index function iteratively converge to the greatest lower bound of all performance indices within an error @e in finite time. The convergence analysis of the proposed ADP algorithm in terms of performance index function and control policy is conducted. The optimal number of control steps can also be obtained by the proposed @e-optimal control algorithm for the unfixed initial state. Neural networks are used to approximate the performance index function, and compute the optimal control policy, respectively, for facilitating the implementation of the @e-optimal control algorithm. Finally, a simulation example is given to show the effectiveness of the proposed method.