Two cascaded nonlinear predictive controls of induction motor

Abstract

Two versions of continuous-time feedback nonlinear predictive control laws with a fixed end point prediction and a finite horizon prediction are presented. They are applied in a cascade structure to induction motor. Both speed and torque/flux tracking objectives are achieved in matched and mismatched parameters case. The rotor flux is estimated by the Kalman filter. The derived nonlinear predictive laws minimize a quadratic performance index of the predicted tracking error. Its implementation does not need to perform an on line optimization and asymptotic tracking of smooth reference signal is guaranteed. Simulations show that the proposed controllers are suitable for high dynamic performance applications.