Adaptive control for a class of nonlinear time-varying systems

Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. We present an adaptive control methodology for a class of nonlinear systems with a time-varying structure where radial basis function neural networks are used as on-line approximators. This class of systems is composed of interpolations of nonlinear subsystems which are input-output feedback linearizable. Without assumptions on rate of change of system dynamics, a stable indirect adaptive control method is presented with analysis of stability for all signals in the closed-loop as well as asymptotic tracking. The performance of the controller is demonstrated using a jet engine control problem.