Observability of Multi-Agent Network Sensing Systems

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS
I. Raptis, Clark N. Taylor
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引用次数: 0

Abstract

This work addresses the observability properties of linear time-invariant systems that are monitored by a sensor network that provides access only to relative measurements and partial absolute observations of the state-space variables. Graph-theoretical tools are deployed to represent the information sharing links between the sensors of the network. The results are extended to multi-agent coordinated systems that are independently controlled or execute distributed control protocols. Explicit analytical conditions are derived that determine the system’s observability with respect to the spectral characteristics of the information-sharing network. The system’s observability is further investigated for multi-agent systems governed by the agreement dynamics where only a singleton measurement is available. The analysis is disseminated to the design of distributed observers where the agents have only available their relative displacement measurement from their neighbors. The distributed observer’s estimation error is rendered globally asymptotically stable by the addition of an anchor node that has access to the absolute motion of a single agent (node). The theoretical analysis is validated through numerical simulations.
多智能体网络感知系统的可观测性
这项工作解决了线性时不变系统的可观测性,该系统由传感器网络监测,仅提供对状态空间变量的相对测量和部分绝对观测的访问。使用图理论工具来表示网络传感器之间的信息共享链接。结果可扩展到独立控制或执行分布式控制协议的多智能体协调系统。导出了确定系统相对于信息共享网络光谱特征的可观测性的显式分析条件。系统的可观察性进一步研究了由协议动力学控制的多智能体系统,其中只有单一测量可用。该分析被传播到分布式观测器的设计中,其中代理只能从其邻居处获得相对位移测量。通过添加一个可以访问单个代理(节点)的绝对运动的锚节点,使分布式观测器的估计误差全局渐近稳定。通过数值模拟验证了理论分析的正确性。
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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