By Dawei Shi, Ling Shi, Tongwen Chen
This ebook explores event-based estimation difficulties. It indicates how numerous stochastic techniques are built to keep up estimation functionality while sensors practice their updates at slower premiums basically whilst needed.
The self-contained presentation makes this book suitable for readers without greater than a simple wisdom of probability research, matrix algebra and linear platforms. The advent and literature evaluation offer details, whereas the most content material offers with estimation difficulties from 4 unique angles in a stochastic environment, utilizing various illustrative examples and comparisons. The textual content elucidates either theoretical advancements and their purposes, and is rounded out by way of a evaluate of open problems.
This book is a worthy source for researchers and scholars who desire to extend their wisdom and paintings within the region of event-triggered structures. even as, engineers and practitioners in commercial method keep an eye on will enjoy the event-triggering process that reduces conversation charges and improves strength potency in wireless automation applications.
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Extra resources for Event-Based State Estimation: A Stochastic Perspective
IEEE Trans Signal Process 61(6):1520–1530 Zhang J, Liu J (2013) Lyapunov-based mpc with robust moving horizon estimation and its triggered implementation. AIChE J 59(11):4273–4286 Zhang J, Liu J (2014) Distributed moving horizon state estimation with triggered communication. In: American control conference (ACC), pp 5700–5705 Zhang XM, Han QL (2015) Event-based filtering for sampled-data systems. Automatica 51:55–69 Zhang X, Zhang Q, Zhao S, Ferrari R, Polycarpou M, Parisini T (2011) Fault detection and isolation of the wind turbine benchmark: an estimation-based approach.
In: IEEE 51st annual conference on decision and control, pp 6583–6590 Trimpe S, D’Andrea R (2014) Event-based state estimation with variance-based triggering. IEEE Trans Autom Control 59(12):3266–3281 Walters P (1982) An introduction to ergodic theory. Springer, New York Wang B, Fu M (2014) Comparison of periodic and event-based sampling for linear state estimation. In: Proceedings of IFAC world congress Wang J, Mustafa G, Chen T, Chu D, Backstrom J (2012) An efficient quadratic programming implementation for cross directional control of large papermaking processes.
2004; Shi et al. 2011, 2014; Sijs and Lazar 2012; Mo and Sinopoli 2012; Jia et al. 2012; Shi and Chen 2013a, b). A general description of deterministic event-triggering conditions was introduced in Sijs and Lazar (2012) and explored in Sijs and Lazar (2012) and Shi et al. (2014). The deterministic send-on-delta conditions were introduced in Miskowicz (2006) and the corresponding event-based estimation problem was investigated in Nguyen and Suh (2007). The event-based estimation problems for the deterministic innovation-levelbased conditions were investigated in Trimpe and D’Andrea (2011), Trimpe (2012), Wu et al.