报告题目：Remote Estimation for Energy Harvesting Systems Under External Noises

报告简介：In this presentation, we talk about the state estimation problem for a class of networked systems with energy harvesting technologies, where the sensor is capable of replenishing energy from the environment. The underlying system is subject to both additive and multiplicative stochastic noises, and the measurement is transmitted to the remote estimator only when the current energy storage is larger than the transmission energy consumption. A binary encoding scheme is utilized in the communication process, under which the measurements are quantized into a bit string, transmitted via memoryless binary symmetric channels with certain probabilistic bit flips, and recovered at the receiver. A min-max robust estimator is designed to minimize the worst-case covariance of the estimation error in terms of the solutions to Riccati-like difference equations. Furthermore, the influence of the length of bit stream on the transmission rate and the estimation performance is discussed, and conditions guaranteeing the boundedness of the proposed estimator are provided.

报告人：王子栋，现任英国伦敦Brunel University讲席教授，欧洲科学院院士，欧洲科学与艺术院院士，IEEE Fellow，International Journal of Systems Science主编，Neurocomputing主编。多年来从事控制理论、机器学习、生物信息学等方面研究，在SCI刊物上发表国际论文六百余篇。现任或曾任十二种国际刊物的主编、副编辑或编委。曾任旅英华人自动化及计算机协会主席、东华大学长江学者讲座教授、清华大学国家级专家。

报告时间：2023年4月6日（星期四）上午 8:00-10:00

报告地点：扬州大学瘦西湖校区数学科学学院208报告厅

主办单位：扬州大学数学科学学院

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