Cascaded Nonlinear predictive control of induction motor

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 that includes both electrical and mechanical dynamics. 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. A key feature of the control law is that its implementation does not need to perform an online optimization and asymptotic tracking of smooth reference signal is guaranteed. Simulations show that the proposed controllers are suitable for high dynamic performance applications. A good robustness with respect to parameters variation and load torque disturbance are achieved.