Research on moment of inertia identification and parameter self-tuning based on generalized high-order SCKF algorithm

In order to restrain the influence of the sudden change of moment of inertia on the speed response characteristics of the motor, the system should have the functions of moment of inertia identification and controller parameter self-tuning. Aiming at the problems of slow convergence time, low accuracy and poor robustness of traditional parameter identification methods, this paper proposes a generalized high-order square-root cubature Kalman filter (SCKF) algorithm. Compared with SCKF algorithm, this algorithm has more sampling points, faster convergence time and better numerical stability. The simulation results of moment of inertia identification show that the generalized high-order SCKF algorithm is superior to the traditional extended Kalman filter (EKF) and SCKF algorithm in convergence time, identification accuracy and stability in the face of moment of inertia mutation. The speed loop PI control parameter self-tuning method based on inertia identification accelerates the response speed of the motor and improves the control performance of the motor.