Tracking control of thrust active magnetic bearing system via Hermite polynomial-based recurrent neural network

Abstract

A Hermite polynomial-based recurrent neural network (HPBRNN) is proposed to control the rotor position on the axial direction of a thrust active magnetic bearing (TAMB) system for the tracking of various reference trajectories in this study. First, the operating principles and dynamic model of the TAMB system using the non-linear electromagnetic force model is derived. Then, the HPBRNN is developed for the TAMB system with enhanced control performance and robustness. In the proposed HPBRNN, each hidden neuron employs a different orthonormal Hermite polynomial basis function (OHPBF) as an activation function. Therefore the learning ability of the HPBRNN is effective with high convergence precision and fast convergence time. Moreover, the connective weights of the HPBRNN using the supervised gradient descent method are updated online and the convergence analysis of the tracking error using the discrete-type Lyapunov function is provided. Finally, some experimental results of various reference trajectories tracking show that the control performance of the HPBRNN is significantly improved compared to the conventional proportional-integral-derivative and recurrent neural network controllers and demonstrate the validity of the proposed HPBRNN for practical applications.