基于极限学习机的永磁同步电机无差拍预测电流控制

Zhichao Chen, Haiyan Gao, Ke Lin, Rong Fu, Zhiyong Lin, Weiqiang Tang
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摘要

为了增强永磁同步电机系统的跟踪性能和鲁棒性,提出了一种基于极限学习机的无差拍预测电流控制方法。由于永磁同步电机易受外界干扰和参数变化等不确定因素的影响,在数学模型中引入了不确定因素。通过ELM逼近系统的不确定性,实现了永磁同步电机的速度跟踪,并通过建立Lyapunov函数验证了系统的稳定性。此外,提出了等效于高增益比例控制的永磁同步电机DPCC控制方法,提高了永磁同步电机的性能。最后,分别在标称情况和参数失配情况下进行了仿真实验,通过系统仿真的对比研究,结果表明,与传统控制方法相比,本文提出的ELM-DPCC具有更好的速度跟踪性能和鲁棒性。
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PMSM Deadbeat Predictive Current Control Based on Extreme Learning Machine
In order to strengthen the tracking performance and robustness of permanent magnet synchronous motor (PMSM) system, a deadbeat predictive current control (DPCC) based on extreme learning machine (ELM) is come up. Since PMSM is susceptible to uncertainties such as external disturbances and parameter changes, the uncertainty factors are introduced in the mathematical model. The uncertainty of the system is approximated by the ELM, then the speed tracking of the permanent magnet synchronous motor is realized, and the stability is certificated by establishing the Lyapunov function. In addition, DPCC method of PMSM is proposed, which is equivalent to high-gain proportional control and improves the performance of the PMSM. Finally, the simulation experiments are carried out in nominal case and parameter mismatch case respectively, through the comparative study of system simulation, the results indicate that contrast with the traditional control method, The ELM-DPCC proposed in this paper has better speed tracking performance and robustness.
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