Robust control for linear induction motor servo drive using neural network uncertainty observer

A robust controller, which combines the merits of integral-proportional (IP) position control and neural network (NN) control, is designed for a linear induction motor (LIM) servo drive in this study. First, the secondary flux of the LIM is estimated using a sliding-mode flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, the IP position controller is designed according to the estimated mover parameters to match the time-domain command tracking specifications. Moreover, a robust controller is formulated using the NN uncertainty observer, which is implemented to estimate the lumped uncertainty of the controlled plant, as an inner-loop force controller to increase the robustness of the LIM servo drive system. Furthermore, in the derivation of the on-line training algorithm of the NN, an error function is used in the Lyapunov function to avoid the real-time identification of the system Jacobian.