Adaptive PI nonlinear cooperative control for motor cluster

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2025-06-07 DOI:10.1016/j.isatra.2025.05.047

Yiting Chen, Yushen Wu, Kairui Chen, Jianhui Wang, Zian Wang

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引用次数: 0

Abstract

To address the effects of nonlinearities and uncertainties in the speed regulation of permanent magnet synchronous motors (PMSMs), an adaptive PI nonlinear control strategy is introduced. First, a nonlinear system model is developed using the PMSM mathematical model, and an adaptive PI nonlinear control approach is designed. Numerical simulations are conducted to demonstrate that this control method effectively tracks the system’s desired values. Then, through a group of comparative simulation experiments, the comparison effect of the designed adaptive PI nonlinear control method and the traditional PI control method is analyzed and compared. Additionally, four PMSM collaborative control system models, including the speed tracking and speed synchronization control structures, are constructed. Finally, a simulation model for a cooperative PMSM control system is developed to evaluate the system’s speed tracking capability and the synchronization between multiple motors. The results show that in the designed motor cluster cooperative control system, the PMSM motor cluster using adaptive PI nonlinear control method can achieve cooperative control in speed tracking and speed synchronization, and can maintain stable operation against nonlinear problems and unknown disturbances.

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运动集群的自适应PI非线性协同控制。

针对永磁同步电动机调速过程中存在的非线性和不确定性，提出了一种自适应PI非线性控制策略。首先，利用永磁同步电机数学模型建立了非线性系统模型，设计了自适应PI非线性控制方法。数值仿真结果表明，该控制方法能有效地跟踪系统的期望值。然后，通过一组对比仿真实验，对所设计的自适应PI非线性控制方法与传统PI控制方法的对比效果进行了分析比较。在此基础上，建立了包括速度跟踪和速度同步控制结构在内的永磁同步电机协同控制系统模型。最后，建立了协作式永磁同步电机控制系统的仿真模型，以评估系统的速度跟踪能力和多电机之间的同步。结果表明，在所设计的电机群协同控制系统中，采用自适应PI非线性控制方法的永磁同步电机群在速度跟踪和速度同步方面能够实现协同控制，并能在非线性问题和未知干扰下保持稳定运行。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.