A Smith-Like Active Disturbance Rejection Control Design for a Class of Overdamped Processes With Application to Low-Pressure Heater System

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS
Zhenlong Wu;Gengjin Shi;Donghai Li;Yanhong Liu;Zhiqiang Gao;YangQuan Chen
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引用次数: 0

Abstract

This article proposes a Smith-like active disturbance rejection control (SLADRC) for a class of overdamped processes composed of multiple first-order inertial components, where the idea of the SLADRC comes from Smith predictor. The design principle of SLADRC is presented and its stability analysis is carried out theoretically, where the estimation error of the designed extended state observer and the regulation error of the control law are both bound. For the convenience of engineering applications, this article also provides a simple and effective parameter-tuning method. The comparative simulations illustrate its advantages in tracking and disturbance rejection performance with satisfactory robustness. Finally, the proposed SLADRC is applied to a low-pressure heater system of a 660MW power plant successfully, where running data further verify its practical significance in industrial processes.
一类过阻尼过程的smith型自抗扰控制设计及其在低压加热器系统中的应用
针对一类由多个一阶惯性分量组成的过阻尼过程,提出了一种类似史密斯的自抗扰控制(SLADRC),其中SLADRC的思想来源于史密斯预测器。提出了SLADRC的设计原理,并从理论上对其稳定性进行了分析,其中所设计的扩展状态观测器的估计误差和控制律的调节误差都是有界的。为了便于工程应用,本文还提供了一种简单有效的参数整定方法。仿真结果表明,该方法具有良好的跟踪和抗干扰性能,具有较好的鲁棒性。最后,将所提出的SLADRC成功应用于某660MW电厂低压加热器系统,运行数据进一步验证了其在工业过程中的实际意义。
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
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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