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Event-triggered Estimation: Observability, Identifiability and Parameter Learning
The Hong Kong University of Science and Technology Department of Computer Science and Engineering Final Year Thesis Oral Defense Title: "Event-triggered Estimation: Observability, Identifiability and Parameter Learning" by LI Kin Fung Abstract: Event-triggered state estimation is an emerging area in control theory. The extraction of information in event-triggered output measurements is crucial in analyzing system characteristics such as observability and identifiability. Meanwhile, the tools for observability analysis in other types of systems are well-developed. Qualitative tools include the observability matrix for linear systems and the observability Lie algebra for nonlinear systems. Recently, quantitative measures based on the empirical Gramian, such as the unobservability index and the estimation condition number, are defined on more general systems, possibly nonlinear and stochastic. This study proposes a quantitative extension of system observability to event-triggered systems through the empirical Gramian and examines its relevance to qualitative system properties. Practical considerations in the implementation of observability criteria and parameter learning algorithms for event-triggered systems are also discussed. Date : 3 May 2024 (Friday) Time : 09:45 - 10:25 Venue : Room 6602 (near lifts 31/32), HKUST Advisor : Prof. CHAN Gary Shueng-Han 2nd Reader : Prof. SHI Ling