Guaranteed Disturbance Compensation and Robust Fault Detection Based on Zonotopic Evaluation

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-11-25 DOI:10.1002/rnc.7711
Shui Fu, Rui Wang, Wentao Tang, Xi-Ming Sun
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Abstract

In the context of model-based fault detection, it is crucial to achieve strong robustness against disturbance and noise. However, the existing robust fault detection methods typically address disturbance and noise in a centralized manner to enhance robustness, which may cause some conservatism since the dynamic characteristics of disturbance and noise are considerably different. In addition, most of the existing model-based fault detection method is implemented with constant thresholds, which may further introduce conservatism. In this context, this paper proposes a guaranteed disturbance compensation and robust fault detection method based on zonotopic evaluation for the discrete-time systems subject to unknown but bounded disturbance and noise. To this end, a disturbance compensation controller is developed based on L $$ {L}_{\infty } $$ technology to obtain guaranteed control performance. Moreover, the control performances with or without disturbance compensation are analyzed based on zonotopes. By considering the disturbance dynamic characteristics, an extended fault detection observer (EFDO) is created to pursue robustness to disturbance, noise, and sensitivity to fault simultaneously. Meanwhile, a multi-objective EFDO is devised by exploiting the L $$ {L}_{\infty } $$ index and H $$ {H}_{-} $$ index as the criteria within the finite-frequency domain. Furthermore, the zonotopic residual evaluation is further deployed to generate the residual boundary, which helps to reduce the conservatism of fault detection. The superiority of the proposed method is theoretically analyzed. Simulation results also validate the effectiveness and superiority of the proposed method in disturbance compensation fault detection.

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基于分区评价的保扰动补偿与鲁棒故障检测
在基于模型的故障检测中,实现对干扰和噪声的强鲁棒性至关重要。然而,现有的鲁棒故障检测方法通常集中处理干扰和噪声以增强鲁棒性,由于干扰和噪声的动态特性差异较大,可能会造成一定的保守性。此外,现有的基于模型的故障检测方法大多采用恒定阈值实现,这可能会进一步引入保守性。在此背景下,针对存在未知有界扰动和噪声的离散系统,提出了一种基于分区评价的保证扰动补偿和鲁棒故障检测方法。为此,开发了一种基于L∞$$ {L}_{\infty } $$技术的扰动补偿控制器,以保证控制性能。在此基础上,分析了带扰动补偿和不带扰动补偿的控制性能。在考虑扰动动态特性的基础上,建立了扩展故障检测观测器(EFDO),同时实现了对扰动、噪声的鲁棒性和对故障的灵敏度。同时,利用L∞$$ {L}_{\infty } $$指数和H−,设计了一个多目标EFDO在有限频域内以$$ {H}_{-} $$ index为准则。进一步利用分众残差评价生成残差边界,降低了故障检测的保守性。从理论上分析了该方法的优越性。仿真结果也验证了该方法在干扰补偿故障检测中的有效性和优越性。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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