Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
A course in fuzzy systems and control
A course in fuzzy systems and control
Fuzzy Sets and Systems - Theme: Fuzzy control
Multivariable predictive control of a pressurized tank using neural networks
Neural Computing and Applications
Neural Computing and Applications
Pulse density recurrent neural network systems with learning capability using FPGA
WSEAS Transactions on Circuits and Systems
Credit assigned CMAC and its application to online learning robust controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive CMAC-based supervisory control for uncertain nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust and fast learning for fuzzy cerebellar model articulation controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
Missile guidance law design using adaptive cerebellar model articulation controller
IEEE Transactions on Neural Networks
Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Self - tuning control of continuous - time systems
WSEAS Transactions on Systems and Control
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The main advantage of the recurrent cerebellar model articulation controller (RCMAC) is its rapid learning rate compared to other neural networks. This paper proposes an adaptive RCMAC control system for a brushless DC (BLDC) motor. The proposed control scheme is composed of an RCMAC controller and a compensation controller. The RCMAC controller is used to mimic an ideal controller, and the compensation controller is designed to compensate for the approximation error between the ideal controller and the RCMAC controller. The Lyapunov stability theory is utilized to derive the parameter tuning algorithm, so that the uniformly ultimately bound stability of the closed-loop system can be achieved. As compared with standard adaptive controller, the proposed control scheme does not require persistent excitation condition. Then, the developed adaptive RCMAC control system is implemented on a field programmable gate array (FPGA) chip for controlling a brushless DC motor. Experimental results reveal that the proposed adaptive RCMAC control system can achieve favorable tracking performance. Since the developed adaptive RCMAC control system uses a hyperbolic tangent function to compensate for the approximation error, there is no chattering phenomenon in the control effort. Thus, the proposed control system is more suitable for real-time practical control applications.