When: Weds 11th of December, 4pm
Where: Rose St Building seminar area
Speaker: He Kong
Title: Estimation of Dynamic Systems under Arbitrary Unknown Inputs
Abstract: The topic of estimation of dynamic systems under arbitrary unknown inputs, also called unknown input decoupled estimation, has received much attention in the last few decades. This is due to its vast applications, especially in fault tolerant estimation/control, security of cyber-physical systems, advanced vehicle applications, etc. In spite of the available extensive literature, there are very stringent requirements that limit the applicability of existing methods. Especially, most results rely on the following three restricted assumptions: (a) for unbiased and minimum variance estimation of the state/unknown input, the initial guess of the state has to be unbiased; (b) for filter existence and stability, the system needs to satisfy the so-called strong detectability criteria (this result was pioneered by Malo Hautus, who is the co-inventor of the well-known PBH lemma, i.e., the observability lemma in control); (c) for the optimal filter design, the noise covariances and the disturbance shaping structures are assumed to be known exactly. In this seminar, we will report our recent work, directly motivated by the above gaps. Especially, we will discuss (almost) complete solutions resolving issues (a) and (b), and partial solutions towards issue (c).
Bio: He Kong was born and grew up in the city of Heze, Shandong Province, China. He received the Bachelor’s, Master’s and PhD degrees from China University of Mining and Technology, Harbin Institute of Technology, and the University of Newcastle, Australia, respectively. Earlier on, his research was mostly influenced by Guang-Ren Duan and Bin Zhou (at Harbin), Graham Goodwin and Maria Seron (at Newcastle), amongst others. He is currently a research fellow at ACFR’s agricultural robotics team. His research interests include estimation and inference of cyber-physical systems, moving horizon estimation/control, field robotics, machine learning, and signal processing applications in agriculture, etc. Recent years’ exposure to field robotics at ACFR has somewhat changed his mindset: while he still has some passion for developing theoretical methods, he has become equally or more interested to verify them in the real world, on the hardware, or at least using real data.