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| Improved Sensorless Control of Linear Oscillatory Machine Based on Online Parameter Identification and Adaptive Harmonic Extraction Filter |
| Ge Jian1, Gong Yifan1, Xu Wei2,3, Liao Kaiju2,3, Su Shihu4 |
1. State Key Laboratory of Advanced Electromagnetic Technology Huazhong University of Science and Technology Wuhan 430074 China;
2. Key Laboratory of High Density Electromagnetic Power and Systems Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190 China;
3. University of Chinese Academy of Sciences Beijing 100049 China;
4. CRRC Zhuzhou Motor Co. Ltd Zhuzhou 412001 China |
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Abstract Sensorless control is an important control strategy for linear oscillatory machines (LOM). The traditional position observer often has low accuracy and poor anti-interference ability. The deviation of stroke observation affects the closed-loop control of piston stroke and resonance frequency tracking control of LOM. Therefore, this paper provides an improved sensorless control method for LOM based on online parameter identification and adaptive harmonic extraction filters.
Firstly, the amplitude-frequency characteristic is applied to analyze parameter sensitivity for LOM. The impacts of different circuit parameters on stroke observation are analyzed. It is proved that the inductance parameter has a more prominent impact on stroke observation. Therefore, the main object of the parameter identification algorithm is the inductance parameter. Secondly, a parameter identification strategy based on a high-frequency injection method is designed. The influence of high-frequency voltage excitation on LOM is studied by analyzing the forced vibration system, and the feasibility of the algorithm in online parameter identification is demonstrated. Then, a new adaptive harmonic extraction filter is designed to solve the harmonic extraction problem, and the fundamental signal can be effectively decayed. Furthermore, an amplitude calculation method based on an orthogonal generator is designed to accelerate data update speed and improve stability, which can obtain accurate piston amplitude information. Finally, the simulation and experimental results show that this method can accurately identify the motor parameters, thereby improving the accuracy of stroke observation.
The following conclusions can be drawn. (1) The impact of a 5% deviation in inductance parameters on stroke observation is much greater than the 10% deviation in resistance parameters, indicating that the accuracy of the stroke observation algorithm is mainly affected by the inductance parameter. (2) Injecting high-frequency voltage signals during LOM operation has little impact on the movement of the piston. (3) The designed adaptive harmonic extraction filter can extract harmonic signals with smaller amplitudes. The extraction of harmonic signals is more accurate and stable than second-order generalized integrators. (4) Compared with the traditional stroke observation method, the accuracy of the stroke observation after parameter identification and correction is significantly improved.
In conclusion, the proposed stroke observation algorithm solves the traditional algorithm's high parameter sensitivity problem. It can obtain accurate stroke observation results even in the case of inaccurate parameters and dynamic changes in parameters.
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Received: 06 October 2023
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2024, Vol. 39 
