Stability analysis and design of fuzzy control systems
Fuzzy Sets and Systems
Credit assigned CMAC and its application to online learning robust controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive control for uncertain nonlinear systems based on multiple neural networks
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
RCMAC-Based Adaptive Control for Uncertain Nonlinear Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The adaptive control of nonlinear systems using the Sugeno-type of fuzzy logic
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Learning convergence of CMAC technique
IEEE Transactions on Neural Networks
Generalizing CMAC architecture and training
IEEE Transactions on Neural Networks
Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs
IEEE Transactions on Neural Networks
Neighborhood sequential and random training techniques for CMAC
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper, a novel cerebellar model articulation controller (CMAC)-Based compensator is proposed to limit bound required in supervisory control systems. There are two structures in the proposed schemes: one is supervisory controller and the other is the CMAC-Based compensator. The supervisory controller can ensure Lyapunov stability of the controlled system in the presence of significant plant uncertainties, if the perfect control is estimated. The CMAC is employed to learn the perfect control, but a model error will exist in the learning process. The object of CMAC-based compensator is to suppress this model error, so that the supervisory of can be rationalized for uncertain nonlinear systems. Finally, simulation results demonstrate that the CMAC-based compensator not only can limit the bound required in supervisory controllers, but also can significantly improve the control performance.