Intelligent MEMS Thermal Mass Flowmeter Based on Modified Sage-Husa Adaptive Robust-Strong Tracking Kalman Filtering
Xinxin Yan, Lei Zhao, Chen Tang, Jie Zou, Wanlei Gao, Jiawen Jian, Qinghui Jin, Xin Zhang
Abstract
Microelectromechanical system (MEMS)-based thermal flow sensors are well known for their fast response and compact size in gas flow measurement. Nevertheless, these sensors exhibit superior performance at low gas flow rates, encountering significant limitations in accuracy and anti-disturbance under high gas flow conditions. This article presents a bypass thermal mass flowmeter (TMF) based on an MEMS sensor core that extended the measurement range by optimizing the length and hole diameter of the restrictor to determine the suitable bypass ratio. Meanwhile, by combining the Sage-Husa adaptive robust KF with the strong tracking KF, we propose a modified Sage-Husa adaptive robust-strong tracking Kalman filter (MSHAR-STKF) to ensure high accuracy and rapid response in flow measurement. The results indicate that the flowmeter achieves a measurement range of 0–15 m2/h with a relative error within 1.5%. By comparing the root mean square error (RMSE) and mean absolute error (MAE) of static signals under various disturbances, the MSHAR-STKF exceeds the Kalman filter (KF), Sage-Husa adaptive KF (SHAKF), strong tracking KF (STKF), and adaptive robust KF (ARKF) in suppressing outliers and biases. In addition, the dynamic response time of MSHAR-STKF is also the fastest among the above algorithms with about 1 s of rise and fall times.