IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Direct heuristic dynamic programming for nonlinear tracking control with filtered tracking error
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive dynamic programming: an introduction
IEEE Computational Intelligence Magazine
Behavioral-fusion control based on reinforcement learning
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Expert Systems with Applications: An International Journal
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A novel neural network (NN)-based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input and multi-output (MIMO) strict feedback nonlinear discrete-time systems. Reinforcement learning is proposed for the output feedback controller, which uses three NNs: 1) an NN observer to estimate the system states with the input-output data, 2) a critic NN to approximate certain strategic utility function, and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. Using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the state estimation errors, the tracking errors and weight estimates is shown.