Enhanced Performance of Model Predictive Speed Control with Taylor Series and Inertia Identification

This paper proposes a novel method to reduce speed errors and torque oscillations, aiming to provide high-performance speed control for dual three-phase permanent magnet motors. The proposed control system eliminates the conventional cascade proportional-integral (PI) scheme, thereby avoiding the need for parameter tuning and enhancing the algorithm's versatility. Additionally, fast dynamic response is achieved by predicting the future speed in discrete steps. To improve the accuracy of the prediction model, a second-order Taylor series expansion for speed is employed, effectively reducing static speed errors caused by motor parameter mismatches. Online estimation of load torque and moment of inertia is also incorporated to enhance disturbance rejection capabilities. Moreover, considering load torque feedforward compensation results in lower overshoot in both speed and torque. Experimental results demonstrate the feasibility and effectiveness of the proposed method.