Model reference adaptive fuzzy control of robot manipulator

Abstract

An model reference adaptive fuzzy control, MRAFC, scheme for manipulator control is proposed to incorporate with nonlinear and time-varying dynamic behavior of the system. The scheme employs a reference model to provide closed-loop performance feedback. The basic idea of MRAFC scheme for manipulator control is to perform adaptive feedback linearization, i.e. to asymptotically cancel the nonlinearity in the system and place system poles in the desired locations as specified in the reference model. The type of controller used is a class of fuzzy controllers, which can be expressed in an explicit form. The stability of the fuzzy rule adaptive laws is assured by the existence of the Lyapunov function. In the simulation, two adaptive fuzzy controllers are applied on a two-link manipulator. The results show that the MRAFC system is stable and the performance in tracking the reference model response can be improved by simply repeating the same task.