具有不可靠通信通道的网络非线性系统的最终有界输出反馈控制:缓冲辅助策略

IF 15.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yuhan Zhang;Zidong Wang;Lei Zou;Yun Chen;Guoping Lu
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引用次数: 0

摘要

本文涉及具有未知非线性动力学的网络系统的最终有界输出反馈控制问题。传感器到观测器的信号传输是通过具有通信限制的网络进行的。这些传输是通过不可靠的通信信道进行的。为了提高测量数据的利用率,我们采用了一种新颖的缓冲辅助策略,以便在通信网络无法访问时存储历史测量数据。利用神经网络技术,引入了一种基于观测器的新型控制器,以解决信号传输行为和未知非线性动态的影响。通过应用随机分析和 Lyapunov 稳定性,构建了一个联合框架,用于分析引入控制器后的系统性能。随后,描述了所需输出反馈控制器的存在条件。然后,通过解决一些特定的矩阵不等式,确定基于观测器的控制器所需的参数。最后,通过一个仿真实例来证实该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy
This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints. These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics. Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observer-based controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.
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来源期刊
Ieee-Caa Journal of Automatica Sinica
Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering
CiteScore
23.50
自引率
11.00%
发文量
880
期刊介绍: The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.
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