OPTIMIZING MOTOR PERFORMANCE USING THE INTEGRATION OF MPC, KALMAN FILTER, AND DIGITAL NOTCH FILTER
Keywords:
model predictive control, Kalman filter, digital notch filter, state-space, ITAE, overshoot, settling time, spectral error.Abstract
This paper proposes a modern approach to motor speed control based on the integration of Model Predictive Control (MPC), a Kalman Filter (KF), and a digital notch filter. The efficiency of traditional approaches decreases due to the sensitivity of the PID controller to noise and ripple. Therefore, a notch filter that selectively suppresses 50 Hz grid ripple, a Kalman filter that provides optimal estimation from noisy measurements, and MPC, which accounts for future dynamics and control variations, were introduced into the system. The mathematical model was derived based on the electromechanical state-space equations of a DC motor and converted into a digital control framework through a discretization process. The notch filter was developed using a biquad structure, the Kalman filter via the classic prediction-correction algorithm, and the MPC by solving an optimized quadratic function over a prediction horizon. Simulation results demonstrated that the proposed MPC+KF+Notch integration reduced the ITAE index by 2.4 times, decreased overshoot from 17% to 8%, and lowered the control signal RMS value compared to the baseline PID controller. Additionally, the component around 50 Hz in the error spectrum was significantly suppressed.
This approach is effective in industrial drives where high stability and noise immunity are required, and it can be further developed in the future using constrained MPC, adaptive notch filters, and online identification algorithms.
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