Fuzzy rules emulated network and its application on nonlinear control systems
Applied Soft Computing
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
Reinforcement learning and adaptive dynamic programming for feedback control
IEEE Circuits and Systems Magazine
Asymptotically stable adaptive critic design for uncertain nonlinear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Adaptive dynamic programming: an introduction
IEEE Computational Intelligence Magazine
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Wavelet based control for a class of delayed nonlinear systems with input constraints
Expert Systems with Applications: An International Journal
Automatica (Journal of IFAC)
A multivariable predictive fuzzy PID control system
Applied Soft Computing
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time
Journal of Intelligent and Robotic Systems
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A novel adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints. The constraints of the actuator are treated in the controller design as the saturation nonlinearity. The adaptive critic NN controller architecture based on state feedback includes two NNs: the critic NN is used to approximate the "strategic" utility function, whereas the action NN is employed to minimize both the strategic utility function and the unknown nonlinear dynamic estimation errors. The critic and action NN weight updates are derived by minimizing certain quadratic performance indexes. Using the Lyapunov approach and with novel weight updates, the uniformly ultimate boundedness of the closed-loop tracking error and weight estimates is shown in the presence of NN approximation errors and bounded unknown disturbances. The proposed NN controller works in the presence of multiple nonlinearities, unlike other schemes that normally approximate one nonlinearity. Moreover, the adaptive critic NN controller does not require an explicit offline training phase, and the NN weights can be initialized at zero or random. Simulation results justify the theoretical analysis