document: Adaptive neural/fuzzy control for interpolated nonlinear systems 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 -LRB- in the form of Takagi-Sugeno fuzzy systems or radial basis function neural networks -RRB- 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
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