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

A robust controller, that combines the merits of integral-proportional (IP) position control and neural network (NN) observed technique, 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 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 online training algorithm of the NN, an error function is used in the Lyapunov function to avoid the real-time identification of the system Jacobian. In addition, to increase the speed and accuracy of the estimated flux, the sliding-mode flux observer is implemented using a 32 bit floating-point digital signal processor (DSP) with a high sampling rate. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results.