Neural network dynamic surface position control of n-joint robot driven by PMSM with unknown load observer

To solve the problems of low accuracy and poor stability due to modeling error, external disturbance and unknown load, which exist in the position servo control of permanent magnet synchronous motor (PMSM) driven joint robot, this article is to propose the radial basis function (RBF) neural networks dynamic surface control strategy with the Sage-Husa adaptive Kalman filter load torque observer. For the unknown load torque of the robot, the PMSM load torque observer is established by using the Sage-Huga adaptive Kalman filter. The RBF neural network dynamic surface controller is designed using the online approximation capability of the neural network, which is used to approximate the modeling error, external interference and filtering error generated by the dynamic surface control of the joint robot online. Combining the above strategies, the n-joint robot position controller is designed. The stability of this control strategy is demonstrated by stability analysis. Simulations and experiments on the two-joint robot show that the control strategy ensures the accuracy and stability of the joint robot position control.