基于仿射算法的不确定参数系统 PID 控制器图形调整方法

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Minghui Chu
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引用次数: 0

摘要

对于参数不确定的系统,找到一个能满足预设鲁棒性的控制器非常重要。传统方法在用边界系统组代替原始系统时,往往会忽略参数耦合对设定边界系统的影响,导致计算出的控制器值区域过于保守。本文基于仿射算法引入噪声信息来描述不确定的系统参数。然后,基于哈里托诺夫定理,提出了一种新的边界系统分组方法。该方法在确定边界系统时考虑了参数耦合信息,避免了区间守恒问题。在此基础上,引入了虚拟相位裕度测试器,以确保获得的控制器参数范围能使系统满足特定的鲁棒性要求。本文得到的结果具有普遍性,并经过严格证明。最后,还提供了一些实例来说明设计过程,并验证了所提方法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graphical tuning method of PID controller for systems with uncertain parameters based on affine algorithm
For systems with uncertain parameters, it is very important to find a controller that can satisfy the preset robustness. The traditional method often ignores the influence of parameter coupling on the set boundary system when substituting the original system with the boundary system group, resulting in the calculated controller value region being too conservative. In this article, noise information is introduced based on affine algorithm to describe uncertain system parameters. Then, based on Kharitonov theorem, a new grouping method for boundary systems is proposed. This method takes the parameter coupling information into account when determining the boundary system, and avoids the problem of interval conservation. On this basis, a virtual phase margin tester is introduced to ensure that the obtained controller parameter range can make the system meet the specific robustness requirements. The results obtained in this article are general and strictly proved. Finally, examples are provided to illustrate the design process and verify the feasibility and efficacy of the proposed approach.
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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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