Improved Cubature Kalman Filter–Based Online Identification and Sliding Mode Speed Control for Direct‐Drive Propeller PMSM Systems

To address the issue that the online identification accuracy of the moment of inertia and the aerodynamic load in direct‐drive motor‐driven propeller systems under complex operating conditions is susceptible to unknown measurement noise and state coupling, an online identification and adaptive control strategy is proposed in this paper. First, the aerodynamic load is equivalently modeled by a speed‐squared proportional model, and a joint state‐space model incorporating the moment of inertia and the equivalent aerodynamic torque coefficient is established. For the identification algorithm, the square‐root cubature Kalman filter (SRCKF) framework is adopted to ensure numerical stability; meanwhile, a strong tracking filter (STF) is introduced to enhance the dynamic response to abrupt parameter variations, and the variational Bayesian (VB) approach is incorporated to estimate the measurement noise covariance online, which improves estimation robustness when the measurement noise statistics are unknown or inaccurately specified. Building on this, the online identification results are incorporated as feedforward compensation into a sliding‐mode speed controller based on a new reaching law, enabling adaptive feedforward compensation based on the online parameter estimates. Experimental results demonstrate that the proposed method not only improves the accuracy of online parameter identification but also effectively enhances the dynamic response of the direct‐drive propeller system.