Abstract
An adaptive interconnected state observer is proposed for the permanent magnet linear synchronous motor (PMLSM) with both unmeasurable state variables and external disturbances. The tracking controller without speed measurement is given via backstepping control method. The stator resistance that varies with the environment is taken as the extended state of one subsystem, and the total disturbance of the mechanical subsystem is as another extended state of the other. The system is transformed into the form of interconnected subsystem. An adaptive interconnected state observer based on Kalman filter is designed to estimate the unmeasurable state and the external disturbance of the system. The controller is designed to realize the sensorless speed tracking control. The simulation results shows the effectiveness of the proposed control scheme.
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This work was supported by the National Natural Science Foundation of China under grant number 62003148, Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University), Ministry of Education under grant number MCCSE2021A05 and China Postdoctoral Science Foundation under grant number 2021M691277.
Kang Wu received his B.S. degree in electrical engineering from Kwangwoon University, Korea, in 2013, an M.S. degree form Newcastle University, United Kingdom, in 2014, and a Ph.D. degree from Southeast University, in 2019. Currently, he is a lecturer at the School of Internet of Things Engineering, Jiangnan University. His research interests include nonlinear control, adaptive control, and motion control.
Ying Lin received her B.S. degree from Liaoning Technical University, China, in 2019, an M.S. degree from the College of Engineering from Qufu Normal University, China, in 2021. Currently, she works at Huaneng Jinling power plant. Her reserch interests include nonlinear control and permanent magnet synchronous motor system control.
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Wu, K., Lin, Y. Sensorless Speed Control of PMLSM via Adaptive Interconnected State Observer. Int. J. Control Autom. Syst. 20, 3822–3831 (2022). https://doi.org/10.1007/s12555-021-0684-x
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DOI: https://doi.org/10.1007/s12555-021-0684-x