基于观测器的编解码网络周期分段系统控制

IF 11.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2025-03-12 DOI:10.1109/TCYB.2025.3543878
Yun Liu;Wen Yang;Chun-Yi Su;Yue Luo;Xiaofan Wang
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引用次数: 0

摘要

研究了编解码框架下网络化周期分段系统基于观测器的控制问题。编码器具有均匀量化器,可以压缩和加密数据,以处理来自传感器的测量。处理后的数据通过网络传输到解码器恢复原始数据,再传输到远程控制站,从而减少了通信负担,保证了数据的安全性。然后,通过构造具有线性插值项的周期Lyapunov函数,利用奇异值分解这一有效技术,导出了具有线性矩阵不等式(LMI)约束的观测器和控制器参数选择的充分条件,实现了闭环系统的指数极限有界性。此外,为了消除由编解码机制(edm)引起的额外稳态误差,设计了一个动态量化因子,使其渐近上界趋于零。最后,通过数值算例验证了所得理论结果的有效性。
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Observer-Based Control of Networked Periodic Piecewise Systems With Encoding–Decoding Mechanism
This article deals with the observer-based control problem of networked periodic piecewise systems under encoding-decoding frameworks. An encoder with a uniform quantizer, which can compress and encrypt data, is provided to process the measurements from the sensors. The processed data is transmitted over the network to the decoder to recover the original data and then to the remote control station, thereby reducing the communication burden and ensuring data security. Then, by constructing the periodic Lyapunov function with linear interpolation terms, exploiting an effective technique—singular value decomposition—sufficient conditions with linear matrix inequality (LMI) constraints for selecting the observer and controller parameters are derived to achieve the exponentially ultimate boundedness of closed-loop systems. Moreover, to eliminate extra steady-state errors caused by encoding-decoding mechanisms (EDMs), a dynamic quantization factor that can make the asymptotic upper bound tend to zero is designed. Finally, numerical examples are provided to illustrate the effectiveness of the derived theoretical results.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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