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Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With unreliable communication channel: A Buffer-Aided Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1566-1578页

作者： Yuhan Zhang Zidong Wang Lei Zou Yun Chen Guoping Lu the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China IEEE the Department of Computer Science Brunel UniversityUB83PHUnited Kingdom the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Automation Hangzhou Dianzi UniversityHangzhou 310018China the School of Electrical Engineering Nantong UniversityNantong 226019China

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 *** 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 *** 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 observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

关键词： Buffer-aided strategy neural networks nonlinear control output-feedback control unreliable communication channel

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Reliable Finite-Time Estimation for Nonlinear Systems over unreliable communication channel

Reliable Finite-Time Estimation for Nonlinear Systems over U...

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第40届中国控制会议

作者： Weiqun Lin Chenwei Zhong Yating Guo Zeqi Zhang Hongxia Rao Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of AutomationGuangdong University of Technology

The article gives a nonfragile state estimator design method for a class of nonlinear systems over unreliable *** use a random function to describe the possibility of data loss in the actual communication channel. It is particularly pointed out that both the original system and the measurement system are nonlinear. An error system between the state value and the estimated value is established, and its corresponding finite-time boundedness was studied. By means of Lyapunov stability analysis, we derive a sufficient condition for stochastic finite-time stability（SFTS） of the estimator error system. Then we develop a theorem for determining the estimator gain, which can be solved by LMI toolbox. Finally, we verify the effectiveness of the theoretical results by a numerical simulation example.

关键词： State estimation finite-time nonlinear systems unreliable communication channel

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Online Learning Approaches in Maximizing Weighted Throughput in an unreliable channel

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Tsinghua Science and Technology 2012年第5期17卷 567-574页

作者： Zhi Zhang Fei Li Department of Computer Science George Mason University Fairfax VA 22030 USA

We design online algorithms to schedule unit-length packets with values and deadlines through an unreliable communication channel. In this model, time is discrete. Packets arrive over time; each packet has a non-negative value and an integer deadline. In each time step, at most one packet can be sent. The ratio of successfully delivering a packet depends on the channel＇s quality of reliability. The objective is to maximize the total value gained by delivering packets no later than their respective deadlines. In this paper, we conduct theoretical and empirical studies of online learning approaches for this model and a few of its variants. These online learning algorithms are analyzed in terms of external regret. We conclude that no online learning algorithms have constant regrets. Our online learning algorithms outperform online competitive algorithms in terms of algorithmic simplicity and running complexity. In general, these online learning algorithms work no worse than the best known competitive online algorithm for maximizing weighted throughput in practice.

关键词： online learning algorithm unreliable communication channel competitive online algorithm

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