Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
IEEE Transactions on Neural Networks
Perspectives of fuzzy systems and control
Fuzzy Sets and Systems
Neural-network inverse dynamic online learning control on physical exoskeleton
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. We propose an adaptive control methodology for a class of nonlinear systems with a time-varying structure. This class of systems is composed of interpolations of nonlinear subsystems which are input-output feedback linearizable. Both indirect and direct adaptive control methods are developed, where the spatially localized models (in the form of Takagi-Sugeno fuzzy systems or radial basis function neural networks) are used as online approximators to learn the unknown dynamics of the system. Without assumptions on rate of change of system dynamics, the proposed adaptive control methods guarantee that all internal signals of the system are bounded and the tracking error is asymptotically stable. The performance of the adaptive controller is demonstrated using a jet engine control problem.