title: Adaptive neural/fuzzy control for interpolated nonlinear systems
abstract: 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
keyphrases: adaptive neural/fuzzy control interpolated nonlinear systems time-varying systems spatially localized models Takagi-Sugeno fuzzy systems radial basis function neural networks online approximators unknown dynamics tracking error jet engine control input-output feedback linearizable systems indirect control direct control stability analysis