Deadbeat Predictive Control of Permanent Magnet Motor Using DUKF Parameter Identification

Permanent magnet synchronous motor(PMSM) undergoes real-time variations in motor parameters during operation due to factors such as magnetic saturation and temperature rise. Parameter mismatch in the controller can cause a decrease in the control performance of deadbeat predictive control and even lead to instability in the motor drive system. To address this issue, a PMSM deadbeat predictive control using the dual unscented Kalman filter(DUKF) is proposed. DUKF enables real-time estimation of motor parameters and updates the deadbeat predictive model to mitigate the impact of parameter errors on control performance. The basic principles, estimator construction, and parameter design methods of DUKF are elaborated in detail. Additionally, a comparative study is conducted on parameter identification methods using the dual extended Kalman filter(DEKF), unscented Kalman filter(UKF), and extended Kalman filter(EKF). In the experimental section, the parameter estimation errors and convergence speeds of DUKF, DEKF, UKF, and EKF methods under motor steady-state and dynamic conditions are systematically studied. The sensitivity to initial values and algorithm complexity of four methods are analyzed. Eventually, the performance of integrating DUKF in deadbeat predictive control of PMSM is evaluated. The experimental results of performance evaluation for the four parameter identification methods demonstrate that the parameter estimation accuracy of DUKF is superior to the other three methods.