Application of adaptive matching pursuit to adaptive control of nonlinear dynamic systems

Abstract

A common problem in the adaptive control of nonlinear systems is the selection of an appropriate trade-off between performance and computational complexity. A reduced-complexity recursive algorithm is proposed for the identification of nonlinear auto-regressive with exogenous inputs (NARX) models of a nonlinear system. This algorithm, which follows the matching pursuit algorithm, is used for the adaptive control of nonlinear dynamic systems, where quadratic spline functions are used as the basic building blocks. The adaptive controller is applied to several nonlinear systems, previously solved by other nonlinear control methods.