Fuzzy Sets and Systems - Theme: Fuzzy control
Stable adaptive fuzzy controllers with application to inverted pendulum tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy sliding mode control of nonlinear system
IEEE Transactions on Fuzzy Systems
Direct adaptive NN control of a class of nonlinear systems
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Effects of kinematics design on tracking performance of model-based adaptive control
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Neural-network-based sliding mode control for missile electro-hydraulic servo mechanism
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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A robust sliding mode adaptive tracking controller using RBF neural networks is proposed for uncertain SISO nonlinear dynamical systems with unknown nonlinearity. The Lyapunov synthesis approach and sliding mode method are used to develop a state-feedback adaptive control algorithm by using RBF neural networks. Furthermore, the H∞tracking design technique and the sliding mode control method are incorporated into the adaptive neural networks control scheme so that the derived controller is robust with respect to disturbances and approximate errors. Compared with conventional methods, the proposed approach assures closed-loop stability and guarantees an H∞ tracking performance for the overall system. Simulation results verify the effectiveness of the designed scheme and the theoretical discussions.