Position Sensorless Vector Control System for Lawnmower Permanent Magnet Synchronous Motor Based on Extended Kalman Filter

Abstract

In this paper, we describe a position sensorless vector control system for a permanent magnet synchronous motor (PMSM) for a lawnmower in order to solve the problems of inferior dynamic performance and insufficient load resistance in the control process of lawnmower motors. A speed–current double-closed-loop vector control strategy was adopted to control the motor speed; an extended Kalman filter (EKF) was constructed to track the motor rotor position. STM32F407 was selected as the main control chip to establish the hardware experimental platform, and the performance of the control system was evaluated. The experimental results demonstrate that the control system accurately regulates motor speed, has good dynamic response characteristics, and can maintain stability under various loads; therefore, it meets the performance requirements of lawnmower motors in practical operation.

Refined Aircraft Positioning Based on Stochastic Hybrid Estimation with Adaptive Square-Root Unscented Particle Filtering

“…In cooperation with state estimation algorithms, this system can carry out state estimation based on the stochastic hybrid dynamic model with coupled discrete and continuous states. The extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF) based on KF have limited improvement of estimation accuracy for nonlinear systems [17][18][19], while particle filter could effectively solve this problem using sequential Monte Carlo sampling with the appropriate number of particles [20,21]. However, the importance of sampling distribution commonly uses a particle filter, which is generally a prior distribution, instead of using the posterior probability distribution corrected by measurement data, which would cause issues of low efficiency and sensitivity to singular points and would not effectively utilize measurement data [22].…”