Design and analysis of direct-action CMAC PID controller
Neurocomputing
Optimal design of CMAC neural-network controller for robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
A self-organizing CMAC network with gray credit assignment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
A robust design criterion for interpretable fuzzy models with uncertain data
IEEE Transactions on Fuzzy Systems
A controller design method for constructing a robust track-following system
IEEE Transactions on Consumer Electronics
Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks
IEEE Transactions on Neural Networks
Missile guidance law design using adaptive cerebellar model articulation controller
IEEE Transactions on Neural Networks
Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
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In this paper, a robust parametric cerebellar model articulation controller (RP-CMAC) with self-generating design, called RPCSGD, is proposed for uncertain nonlinear systems. The proposed controller consists of two parts: one is the parametric CMAC with self-generating design (PCSGD), which is utilized to approximate the ideal controller and the other is the robust controller, which is designed to achieve a specified H^~ robust tracking performance of the system. The corresponding memory size of the proposed controller can be suitably constructed via the self-generating design. Thus, the useless or untrained memories will not take possession of the space. Besides, the concept of sliding-mode control (SMC) is adopted so that the proposed controller has more robustness against the approximated error and uncertainties. The stability of the system can be guaranteed surely due to the derivations of the adaptive laws of the proposed RPCSGD based on the Lyapunov function. Finally, the proposed controller is applied to the second-order chaotic system and the one-link rigid robotic manipulator. The tracking performance and effectiveness of the proposed controller are verified by simulations of the computer.