Passive control strategy of lower limb rehabilitation robot based on RBF neural network

Abstract

In practical application, the model of a lower limb rehabilitation robot has uncertain parameters and external interference. The RBF neural network is employed to fit the uncertain part of the system and the sliding mode approach rate is designed to compensate for the external interference and the deviation of the neural network in this paper. The conventional sliding mode control uses a discontinuous symbolic function, which causes chatting phenomenon in the output of the controller. In this paper, a new sliding mode approximation rate is proposed to reduce the jitter phenomenon in the system output, and the Lyapunov method is used to ensure the final asymptotic stability. Finally, the effectiveness of the algorithm is verified by the Simulink.