A novel class of non-Gaussian system performance assessment and controller parameter tuning methods

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2024-11-01 DOI:10.1016/j.isatra.2024.08.031
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Abstract

Traditional variance-based control performance assessment (CPA) and controller parameter tuning (CPT) methods tend to ignore non-Gaussian external disturbances. To address this limitation, this study proposes a novel class of CPA and CPT methods for non-Gaussian single-input single-output systems, denoted as data Gaussianization (inverse) transformation methods. The idea of quantile transformation is used to transform the non-Gaussian data with the goal of maximizing mutual information into virtual Gaussian data. In addition, optimal system data for the virtual loop are mapped back to the actual non-Gaussian system using quantile inverse transformation. Furthermore, a CARMA model-based recursive extended least square algorithm and a CARMA model-based least absolute deviation iterative algorithm are used to identify virtual Gaussian and non-Gaussian system process models, respectively, while implementing the CPT. Finally, a unified framework is proposed for the CPA and CPT of a non-Gaussian control system. The simulation results demonstrate that the proposed strategy can provide a consistent benchmark judgment criterion (threshold) for different non-Gaussian noises, and the tuned controller parameters have good performance.
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一类新型非高斯系统性能评估和控制器参数调整方法。
传统的基于方差的控制性能评估(CPA)和控制器参数调整(CPT)方法往往会忽略非高斯外部干扰。针对这一局限性,本研究提出了一类适用于非高斯单输入单输出系统的新型 CPA 和 CPT 方法,即数据高斯化(逆)变换方法。量子变换的思想用于将以互信息最大化为目标的非高斯数据变换为虚拟高斯数据。此外,利用量子反变换将虚拟环路的最佳系统数据映射回实际的非高斯系统。此外,基于 CARMA 模型的递归扩展最小平方算法和基于 CARMA 模型的最小绝对偏差迭代算法分别用于识别虚拟高斯和非高斯系统过程模型,同时实施 CPT。最后,提出了非高斯控制系统 CPA 和 CPT 的统一框架。仿真结果表明,所提出的策略能为不同的非高斯噪声提供一致的基准判断标准(阈值),且调整后的控制器参数具有良好的性能。
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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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