Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator

Abstract This paper proposes a control scheme based on the fraction integral terminal sliding mode control and adaptive neural network. It deals with the system model uncertainties and the disturbances to improve the control performance of the Inchworm robot manipulator. A fraction integral terminal sliding mode control applies to the Inchworm robot manipulator to obtain the initial stability. Also, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena. The weight matrix of the proposed adaptive neural network can be updated online, according to the current state error information. The stability of the proposed control method is proved by Lyapunov theory. The performance of the adaptive neural network fraction integral terminal sliding mode control is compared with three other conventional controllers such as sliding mode control, integral terminal sliding mode control and fraction integral terminal sliding mode control. Simulation results show the effectiveness of the proposed control method.

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