Dynamic Response of Novel Adaptive Modified Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Because an electric scooter driven by permanent magnet synchronous motor (PMSM) servo-driven system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-driven electric scooter under the external disturbances and parameter variations in this study. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem and the gradient descent method. Furthermore, the modified recurrent Legendre NN with variable learning rate is proposed to raise convergence speed. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.