基于由降阶和全阶扩展状态观测器组成的级联估计器的主动干扰抑制控制

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2024-08-01 DOI:10.1016/j.isatra.2024.05.032
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引用次数: 0

摘要

本文提出了一种开创性的级联估计器 CRESO,它以一种新颖的方式合并了降阶和全阶扩展状态观测器(ESO)。CRESO 旨在解决主动干扰抑制控制(ADRC)方案中固有的鲁棒性、估计精度和噪声放大之间的权衡问题。与单级 ESO 和级联 ESO(CESO)相比,CRESO 的频域分析证实了其性能和鲁棒性能力。这些特性通过实际指标进行量化,如稳定裕度、灵敏度带宽和低频下的估计误差。此外,还讨论了选择 CRESO 级联带宽对噪声抑制的影响。同步降压转换器的实验验证证明了基于 CRESO 的 ADRC 在控制增益不确定性、频率变化的外部干扰和传感器噪声方面的有效性。从跟踪误差和控制信号得出的几个质量指标可以看出,与采用奇异ESO、两级CESO和两个独立ESO的ADRC策略相比,所提出的方法更具优势。
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Active disturbance rejection control based on a cascade estimator composed of reduced-order and full-order extended state observers

This paper presents a pioneering cascade estimator, CRESO, which merges reduced-order and full-order extended state observers (ESO) in a novel manner. CRESO is designed to navigate the trade-off between robustness, estimation accuracy, and noise amplification inherent in active disturbance rejection control (ADRC) schemes. An analysis in the frequency domain substantiates CRESO’s performance and robustness capabilities compared to those of single-level ESO and cascade ESO (CESO). These features are quantified using practical metrics, such as stability margins, sensitivity bandwidth, and estimation error at low frequencies. Additionally, the discussion encompasses the impact of selecting bandwidths for the cascade levels of CRESO on noise suppression. Experimental validation on a synchronous buck converter demonstrates the effectiveness of CRESO-based ADRC against control gain uncertainties, frequency-varying external disturbances, and sensor noise. The results highlight the advantages of the proposed approach over ADRC strategies employing singular ESO, two-level CESO, and two independent ESOs, as evidenced by several quality indices derived from the tracking errors and control signals.

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来源期刊
ISA transactions
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.
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