Sliding Mode Control of Manipulator Based on Adaptive Parameter Reaching Law
Aiming at the problems such as low precision,large buffeting amplitude and easy interference of image acquisition ma-nipulator in the process of sliding mode control,a neural network sliding mode control method based on adaptive parameter adjustment approach law was proposed.Based on the classical exponential function approach law,the constant velocity coefficients were adaptive controlled.The nominal model of the manipulator was constructed by introducing parameters such as the length of the connecting rod and the moment of inertia.The adaptive RBF neural network was used to facilitate real-time control and adaptive improvement,and was com-bined with the designed linear sliding mode surface.A suitable Lyapunov function was designed to prove the stability of the designed sys-tem.At the same time,it was compared with the adaptive exponential reaching law and adaptive saturation function reaching law of neu-ral network.The results show that the method can effectively suppress chattering and achieve high precision trajectory tracking under the same conditions.
万方数据
维普
