Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
CMAC with general basis functions
Neural Networks
Feedback linearization using CMAC neural networks
Automatica (Journal of IFAC)
Optimal design of CMAC neural-network controller for robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
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
A direct adaptive neural-network control for unknown nonlinear systems and its application
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
Grey adaptive growing CMAC network
Applied Soft Computing
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
Engineering Applications of Artificial Intelligence
Gait Pattern Based on CMAC Neural Network for Robotic Applications
Neural Processing Letters
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This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.