Adaptive QSMO-Based Sensorless Drive for IPM Motor with NN-Based Transient Position Error Compensation

In commercial electrical equipment, the popular sensorless drive scheme for the interior permanent magnet synchronous motor, based on the quasi-sliding mode observer (QSMO) and phase-locked loop (PLL), still faces challenges such as position errors and limited applicability across a wide speed range...

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Vydáno v:Electronics (Basel) Ročník 13; číslo 15; s. 3085
Hlavní autoři: Sun, Linfeng, Guo, Jiawei, Jiang, Xiongwen, Kawaguchi, Takahiro, Hashimoto, Seiji, Jiang, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2024
Témata:
Adaptation
Algorithms
Back propagation networks
Bandwidths
Boundary layers
Compensators
Coordinate transformations
Data mining
Design
Electric equipment
Electrical equipment
Electrical equipment and supplies
Electrical machinery
Error analysis
Error compensation
Machine learning
Magnets, Permanent
Methods
Neural networks
Parameters
Permanent magnets
Phase lag
Phase locked loops
Position errors
Position sensing
Sensors
Steady state
Synchronous motors
ISSN:2079-9292, 2079-9292
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Popis
Shrnutí:In commercial electrical equipment, the popular sensorless drive scheme for the interior permanent magnet synchronous motor, based on the quasi-sliding mode observer (QSMO) and phase-locked loop (PLL), still faces challenges such as position errors and limited applicability across a wide speed range. To address these problems, this paper analyzes the frequency domain model of the QSMO. A QSMO-based parameter adaptation method is proposed to adjust the boundary layer and widen the speed operating range, considering the QSMO bandwidth. A QSMO-based phase lag compensation method is proposed to mitigate steady-state position errors, considering the QSMO phase lag. Then, the PLL model is analyzed to select the estimated speed difference for transient position error compensation. Specifically, a transient position error compensator based on a feedback time delay neural network (FB-TDNN) is proposed. Based on the back propagation learning algorithm, the specific structure and optimal parameters of the FB-TDNN are determined during the offline training process. The proposed parameter adaptation method and two position error compensation methods were validated through simulations in simulated wide-speed operation scenarios, including sudden speed changes. Overall, the proposed scheme fully mitigates steady-state position errors, substantially mitigates transient position errors, and exhibits good stability across a wide speed range.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13153085