University of Groningen
Coordination networks under noisy measurements and sensor biases
Shi, Mingming
DOI:
10.33612/diss.99968844
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date: 2019
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Shi, M. (2019). Coordination networks under noisy measurements and sensor biases. Rijksuniversiteit Groningen. https://doi.org/10.33612/diss.99968844
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
Summary
Large scale network systems have been constructed and utilized to provide ser-vices ranging from energy acquisition and water distribution to health mon-itoring and transportation. The operation of these complex systems relies on sensors and actuators to acquire and control the system states, which are com-monly exchanged among the sub-parts of the systems via communication chan-nels, due to the spatial separation of the systems. Considering the pervasive-ness of these man-made complex systems and the importance of the data ex-traction and exchange, attention should be paid in understanding how large scale systems behave when there are uncertainties in the measurements and communications.
Aside from transmission delays and information missing, noise is also a major issue in data exchange. In addition, when sensors are used to measure vari-ables, the problem that arises commonly is that the read-out may not be exactly equal to real value. In both cases, the data error prevents the systems to get accurate state information. As the current emergence of Internet of Thing, In-dustry 4.0, smart city and 5G, sensors and communication mediums are play-ing more and more important roles in network systems. Considerplay-ing these facts, this thesis focuses on analysing and addressing the issues in network systems caused by the error in state measurement and exchange.
We first consider two algorithms to deal with the data exchange error, with a particular interest in designing robust network coordination algorithms against unknown but bounded communication noise. In chapter 3, we propose a self-triggered consensus algorithm to tackle the state drift problem of consensus dynamics caused by the communication noise. In chapter 4, we refine the res-ult by proposing a different algorithm. Although these two algorithms both can achieve practical consensus and guarantee boundedness of system state, the mechanisms of them are different. The first algorithm relies on an adaptive threshold, which is adjusted based on the node state, to zero the control inputs of the nodes when their disagreements are sufficiently small. The second al-gorithm imposes the bound on the state of each node by saturating the state received from the node neighbours.
128 summary
Lastly, we consider the state measurement error, and focus on estimating the sensor bias from the incorrect measurement. The sensors in the network meas-ure the relative states of their neighbours, and the measmeas-urements may contain biases. We discuss the conditions of the measurement graphs and the number of biased sensors that allow the biases to be reconstructed from the measure-ments. Furthermore, we provide distributed algorithms to compute the value of the biases.
