基于特征补偿的复合干扰双通道观测器设计

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Xinyu Wen, Zhihao Wang, Yaling Dong, Ruixian Li
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引用次数: 0

摘要

本文提出了一种非耦合结构的特性补偿双通道观测器(CCDCO),用于处理受到复合干扰的系统。在设计策略中,有一个外部干扰补偿通道(ODCC)和一个内部干扰补偿通道(IDCC)。具体来说,在外部干扰补偿通道中,所提出的频率特性补偿观测器(FCCO)从复合干扰中提取并去除规则的周期成分,而估计误差则保留不规则特性。因此,在 IDCC 中,FCCO 的估计误差和不规则部分被视为通过补偿函数观测器(CFO)估计的整数项。与之前的设计相比,CCDCO 作为复合干扰估计的一种求解技能有两个重要优势。首先,所提出的非耦合结构可根据已知或未知的先验信息明确区分扰动特征。其次,通过结合 CFO 和建议的 FCCO 的优点,可以相应地估计不同类型的干扰,而无需相互耦合。本文提供了机器人机械手的仿真结果,以验证所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dual‐channel observer design for composite disturbances based on characteristic compensation
This paper presents a non‐coupled structure of characteristic compensation dual‐channel observer (CCDCO) for handling systems subjected to composite disturbances. There exist an outer disturbance compensation channel (ODCC) and an inner disturbance compensation channel (IDCC) in the design strategy. Specifically, in the ODCC, the proposed frequency characteristic compensation observer (FCCO) extracts and removes the regular periodic components from the composite disturbances, where the estimation error preserves irregular characteristics. Thus, the estimation error of FCCO and irregular parts are treated as lumped terms estimated via compensation function observer (CFO) in the IDCC. As a solving skill for composite disturbances estimation, CCDCO has two important advantages over previous designs. First, the proposed non‐coupled structure explicitly distinguishes disturbance characteristics according to a priori information known or not. Second, by combining the benefits of the CFO and proposed FCCO, different types of disturbances can be estimated accordingly without coupling of each other. Simulation results on the robotic manipulator are provided to validate the effectiveness of the proposed method.
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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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