Model reference adaptive fuzzy control for one linear actuator hopping robot

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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.

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