In this paper, a model predictive torque control (MPTC) method based on dynamic forgetting factor (DFF) parameter recognition (DFF‐MPTC) is proposed to reduce the impact of time delay, nonlinearity and parameter mismatch on the performance of permanent magnet synchronous motor (PMSM) control system. It uses one‐beat delay compensation and improved Euler formula to construct a prediction model for reducing the influence of time delay and nonlinearity. Combined with the DFF‐recursive least squares (DFF‐RLS) method, the motor parameters are identified online and updated in real time, reducing the sensitivity of system parameters. A hardware‐in‐the‐loop (HIL) experimental platform is established based on a PMSM with a rated speed of 3000 r/min, rated torque of 0.36 N·m, and 4 pole pairs. Under the control parameter configuration—including a current/voltage sampling frequency of 10 kHz, PI controller proportional gain k p = 0.22, integral gain k i = 0.11, flux linkage weight coefficient ξ = 20, and adjustable parameters of the DFF, α = 0.96/ γ = 2, multicondition verifications are carried out. These conditions include the start‐up process at a reference speed of 1500 r/min, steady‐state operation under rated load, load mutation, and parameter mismatch scenarios. Comparing with the traditional MPTC, the experimental results under different working conditions and parameter mismatch show that, the dynamic response speed of DFF‐MPTC is increased by about 40%, the steady‐state speed error is reduced by 42.9%, the torque tracking error is reduced by 20%, and the current total harmonic distortion (THD) is reduced by 34.7%. This study provides a new idea for improving the parameter robustness and dynamic performance of PMSM control system.
Ruan et al. (Thu,) studied this question.