Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Multivariable predictive control of a pressurized tank using neural networks
Neural Computing and Applications
Adaptive CMAC-based supervisory control for uncertain nonlinear systems
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
High-order MS CMAC neural network
IEEE Transactions on Neural Networks
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
Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
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This paper proposes an adaptive RCMAC system for a brushless DC (BLDC) motor. This control system is composed of a recurrent cerebellar model controller (RCMAC) and a compensation controller. RCMAC is used to mimic an ideal controller, and the compensation controller is designed to compensate for the approximation error between the ideal controller and RCMAC. 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. The stability analysis shows that the output of the system can exponentially converge to a small neighborhood of the trajectory command. Then, the developed adaptive RCMAC 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 system can achieve favorable tracking performance for the brushless DC motor control.