具有传感器/执行器故障的互联系统基于输出的分散自适应事件触发控制

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-11-27 DOI:10.1109/TCYB.2024.3498071
Zhirong Zhang;Changyun Wen;Long Chen;Yongduan Song;Bowen Peng;Gang Feng
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Output-Based Decentralized Adaptive Event-Triggered Control of Interconnected Systems With Sensor/Actuator Failures
This article presents a double-channel (sensor-to-controller channel and controller-to-actuator channel) event triggered control method for nonlinear interconnected systems subject to sensor and actuator faults via the backstepping technique. It should be emphasized that the utilization of triggering mechanism at the sensor side poses a challenge to the design of backstepping control, as it leads to nondifferentiable virtual control signals due to the discontinuous nature of the state/output signals received at the controller side. In contrast to existing methods, the proposed event triggering mechanism eliminates the need for computing virtual control signals at the sensor side before transmitting them to the controller side. By establishing the relationships of the corresponding variables in two communication scenarios (namely, without and with event triggering) and introducing dynamic filtering technique, the problem of nondifferentiable virtual control signals in backstepping design is solved. We present a numerical case study to validate the effectiveness and advantages of the proposed decentralized event triggered control approach.
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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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