Adaptive Two-Stage Extended Kalman Filter Theory in Application of Sensorless Control for Permanent Magnet Synchronous Motor

Extended Kalman filters (EKF) have been widely used for sensorless field oriented control (FOC) in permanent magnet synchronous motor (PMSM). The first key problem associated with EKF is that the estimator requires all the plant dynamics and noise processes are exactly known. To compensate inaccurate model information and improve tracking ability, adaptive fading extended Kalman filtering algorithms have been proposed for the nonlinear system. The second key problem is that the EKF suffers from computational burden and numerical problems when state dimension is large. The two-stage extended Kalman filter (TSEKF) with respect to this problem has been extensively studied in the past. Combining the advantages of both AFEKF and TSEKF, this paper presents an adaptive two-stage extended Kalman filter (ATEKF) for closed-loop position and speed estimation of a PMSM to achieve sensorless operation. Experimental results demonstrate that the proposed ATEKF algorithm for PMSMs has strong robustness against model uncertainties and very good real-time state tracking ability

Adaptive Two-Stage Extended Kalman Filter Theory in Application of Sensorless Control for Permanent Magnet Synchronous Motor

Extended Kalman filters (EKF) have been widely used for sensorless field oriented control (FOC) in permanent magnet synchronous motor (PMSM). The first key problem associated with EKF is that the estimator requires all the plant dynamics and noise processes are exactly known. To compensate inaccurate model information and improve tracking ability, adaptive fading extended Kalman filtering algorithms have been proposed for the nonlinear system. The second key problem is that the EKF suffers from computational burden and numerical problems when state dimension is large. The two-stage extended Kalman filter (TSEKF) with respect to this problem has been extensively studied in the past. Combining the advantages of both AFEKF and TSEKF, this paper presents an adaptive two-stage extended Kalman filter (ATEKF) for closed-loop position and speed estimation of a PMSM to achieve sensorless operation. Experimental results demonstrate that the proposed ATEKF algorithm for PMSMs has strong robustness against model uncertainties and very good real-time state tracking ability

Experimental and numerical studies on ceiling maximum smoke temperature and longitudinal decay in a horseshoe shaped tunnel fire

The present paper investigates the ceiling maximum smoke temperature and longitudinal decay in tunnel fires using a horseshoe shaped 1:3.7 scale-model tunnel constructed by concrete and a full-scale model tunnel established by SIMTEC for the first time. The maximum smoke temperature beneath the ceiling and the longitudinal temperature profiles were obtained and analyzed. The major conclusions are summarized as follows: The ceiling maximum smoke temperature rise right above the fire source is directly proportional to the terms of Q2/3/Hf5/3 and the ceiling maximum smoke temperature decreases as a sum function of two exponential equations of horizontal distance. Modified equations are proposed for maximum smoke temperature rise beneath the ceiling and longitudinal temperature decay, and the predictions show a good agreement with the values measured by experiments and numerical simulations. The results obtained by numerical simulations agree well with experimental results, and SIMTEC is reasonable to simulate the tunnel fires to predict the temperature profiles. The results are of important significance for tunnel fire safety and personnel evacuation. Keywords: Ceiling maximum temperature, Longitudinal temperature decay, Horseshoe shaped tunnel, Tunnel fire