Model reference adaptive fuzzy control for one linear actuator hopping robot

Abstract

The present paper is concerned with an adaptive fuzzy control approach to control one linear actuator hopping robot. Our simple approach uses linearized model to design a state feedback servo controller. Thereafter, by using fuzzy networks we developed model reference adaptive fuzzy control in which a fuzzy network is used to compensate the nonlinearity of robot dynamics. The role of the fuzzy network is to construct a linearized model by minimizing the output error caused by nonlinearity in the robot control system through the proposed learning mechanism. The continuous hopping gait was realized by simulation.