Improved MS_CMAC Neural Networks by Integrating a Simplified UFN Model
Neural Processing Letters
Intelligent adaptive control for MIMO uncertain nonlinear systems
Expert Systems with Applications: An International Journal
FPGA-based real-time implementation of an adaptive RCMAC control system
WSEAS Transactions on Circuits and Systems
Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm
Expert Systems with Applications: An International Journal
FPGA-implemented adaptive RCMAC design for BLDC motors
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
CMAC-based compensator for limiting bound required in supervisory control systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Standalone CMAC control system with online learning ability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two-stage rule-based precision positioning control of a piezoelectrically actuated table
International Journal of Systems Science
ART-type CMAC network classifier
Neurocomputing
Hybrid controller design for wing rock systems via CMAC approach
ROCOM'06 Proceedings of the 6th WSEAS international conference on Robotics, control and manufacturing technology
Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Expert Systems with Applications: An International Journal
Chaos synchronization of nonlinear gyros using self-learning PID control approach
Applied Soft Computing
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
Engineering Applications of Artificial Intelligence
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An adaptive cerebellar-model-articulation-controller (CMAC)-based supervisory control system is developed for uncertain nonlinear systems. This adaptive CMAC-based supervisory control system consists of an adaptive CMAC and a supervisory controller. In the adaptive CMAC, a CMAC is used to mimic an ideal control law and a compensated controller is designed to recover the residual of the approximation error. The supervisory controller is appended to the adaptive CMAC to force the system states within a predefined constraint set. In this design, if the adaptive CMAC can maintain the system states within the constraint set, the supervisory controller will be idle. Otherwise, the supervisory controller starts working to pull the states back to the constraint set. In addition, the adaptive laws of the control system are derived in the sense of Lyapunov function, so that the stability of the system can be guaranteed. Furthermore, to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Finally, the proposed control system is applied to control a robotic manipulator, a chaotic circuit and a linear piezoelectric ceramic motor (LPCM). Simulation and experimental results demonstrate the effectiveness of the proposed control scheme for uncertain nonlinear systems.