Neuro-Dynamic Programming
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
A retrospective on adaptive dynamic programming for control
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
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
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Online learning control by association and reinforcement
IEEE Transactions on Neural Networks
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, the adaptive dynamic programming (ADP) approach is utilized to design a neural-network-based optimal controller for a class of unknown discrete-time nonlinear systems with quadratic cost function. To begin with, a neural network identifier is constructed to learn the unknown dynamic system with stability proof. Then, the iterative ADP algorithm is developed to handle the nonlinear optimal control problem with convergence analysis. Moreover, the single network dual heuristic dynamic programming (SN-DHP) technique, which eliminates the use of action network, is introduced to implement the iterative ADP algorithm. Finally, two simulation examples are included to illustrate the effectiveness of the present approach.