A New Cerebellar Model Articulation Controller for Rehabilitation Robots
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Synchronization of an uncertain Genesio chaotic system via adaptive CMAC
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Adaptive statistic tracking control based on two-step neural networks with time delays
IEEE Transactions on Neural Networks
Adaptive filter design using recurrent cerebellar model articulation controller
IEEE Transactions on Neural Networks
MIMO CMAC neural network classifier for solving classification problems
Applied Soft Computing
Expert Systems with Applications: An International Journal
Journal of Control Science and Engineering - Special issue on Dynamic Neural Networks for Model-Free Control and Identification
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
Engineering Applications of Artificial Intelligence
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Expert Systems with Applications: An International Journal
Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
Applied Soft Computing
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A hybrid control system, integrating principal and compensation controllers, is developed for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. This hybrid control system is based on sliding-mode technique and uses a recurrent cerebellar model articulation controller (RCMAC) as an uncertainty observer. The principal controller containing an RCMAC uncertainty observer is the main controller, and the compensation controller is a compensator for the approximation error of the system uncertainty. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. The Taylor linearization technique is employed to increase the learning ability of RCMAC and the adaptive laws of the control system are derived based on Lyapunov stability theorem and Barbalat's lemma so that the asymptotical stability of the system can be guaranteed. Finally, the proposed design method is applied to control a biped robot. Simulation results demonstrate the effectiveness of the proposed control scheme for the MIMO uncertain nonlinear system