Radial Basis Function Neural Network and Feedforward Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor
Source abstract
Abstract
A composite control strategy is proposed to improve the position-tracking performance and anti-interference capabilities of permanent magnet synchronous motors (PMSMs). This strategy integrates an active disturbance rejection controller (ADRC) and a radial basis function neural network (RBFNN) with feedforward control. Initially, the flexibility and robustness of the ADRC are utilized in the position loop control. Subsequently, the parameters of the extended state observer (ESO) within the ADRC are optimized, benefiting from the fast convergence speed and optimal approximation provided by the RBFNN. To further enhance the dynamic tracking performance, a differential feedforward link is introduced between the desired speed and the output signal. The simulation and experimental results demonstrate that when the expected electrical angle inputs are sinusoidal and pulse signals, the incorporation of the feedforward link and the adjustment of parameters in the ADRC lead to improved position-tracking capabilities and greater adaptability to load disturbances.
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Research context
OpenAlex topics: Iterative Learning Control Systems · Sensorless Control of Electric Motors · Adaptive Control of Nonlinear Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Aeronautical Engineering Institute, Northwestern Polytechnical University, Xi’an 710054, China
Funding
Key Research and Development Program of Shaanxi Province · Degree and postgraduate education reform and research project of XUST
References (24)
Show source-provided references
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