基于自适应参数估计的永磁同步电机非线性自抗扰控制器

IF 9.9 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Honghao Xia;Shengquan Li;Zhengrong Xiang;Donglei Chen;Zhe Li
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引用次数: 0

摘要

考虑参数变化、模型误差和负载突变等全扰动,提出了一种自适应自抗扰控制(adaptive ADRC)方法来调节永磁同步电机系统的转速。首先,利用自适应的惯性和通量参数估计,构建了永磁同步电机的跟踪状态空间模型;其次,设计了一种基于自适应律的非线性扩展状态观测器(ESO)来估计系统的总扰动并进行前馈补偿。建立了有限时间有界稳定性理论来分析所提出的非线性ESO。在此基础上,利用Lyapunov稳定性理论证明了闭环系统的稳定性。最后,在基于dsspf28335和MATLAB/SIMULINK的实验平台上验证了所提出的自适应自抗扰控制器的调速性能和抗干扰性能。仿真和实验结果验证了该控制器的可靠性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Parameter Estimation Based Nonlinear Active Disturbance Rejection Controller for a PMSM System
Considering total disturbances, i.e., parameter variation, model error, and load mutation, an adaptive active disturbance rejection control (adaptive ADRC) method is proposed to regulate the speed of permanent magnet synchronous motor system (PMSMs) in this article. First, a tracking state space model of a PMSMs is structured with adaptive parameter estimation of inertia and flux. Second, a nonlinear extended state observer (ESO) based on an adaptive law is designed to estimate the total disturbances for feedforward compensation. A finite time bounded stability theory is established to analyze the proposed nonlinear ESO. In addition, the stability of the closed-loop system is proved by a Lyapunov stability theory based on a proposed positive definite scalar function. Finally, the speed regulation and antidisturbance performances of the proposed adaptive ADRC controller are verified by an experimental platform based on DSPF28335 and MATLAB/SIMULINK. The reliability and superiority of the proposed controller are verified by simulation and experiment results.
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来源期刊
IEEE Transactions on Industrial Informatics
IEEE Transactions on Industrial Informatics 工程技术-工程:工业
CiteScore
24.10
自引率
8.90%
发文量
1202
审稿时长
5.1 months
期刊介绍: The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.
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