Model reference adaptive control of nonlinear system using feedback linearization

Abstract

Develops an indirect adaptive controller using feedback linearization (IACFL). IACFL addresses the problem of designing a robust controller for a nonlinear system with observable nonlinear functions and state variables. IACFL consists of strictly positive real (STR)-Lyapunov synthesis algorithm, Input state feedback linearization (ISFL) algorithm, and model reference adaptive controller (MRAC). IACFL provides that, when estimated parameters of the nonlinear system are given in ISFL repeatedly, the output of nonlinear system can follow that of a stable reference linear model.