When: Thursday 29th of April, 1pm AEST
Where: This seminar will be presented online, RSVP here.
Speakers: Dr Lei Wang, Prof Ian Manchester
Title: Recent work accepted for CDC II
In this seminar will comprise of two talks by Dr Lei Wang and Prof Ian Manchester where they will present their recent work which was accepted for the 60th IEEE Conference on Decision and Control (CDC 2021).
Lei Wang will present on: Initial-Value Privacy of Linear Dynamical Systems
Ian Manchester will present on: On the Equivalence of Contraction and Koopman Approaches for Nonlinear Stability and Control
Initial-Value Privacy of Linear Dynamical Systems
This talk presents the study on initial-value privacy of linear dynamical systems with random process and sensor noises, in the presence of some public initial values. For such a system, eavesdroppers may infer the sensitive initial values from the observed system output trajectories and the public initial values, leading to initial-value privacy risks. We define differential initial-value privacy and complete initial-value privacy (in the observability sense), respectively, for the system as metrics of privacy risks. Firstly, given any set of public initial values, both qualitative and quantitative properties of the differential initial-value privacy are developed on the reduced observability matrix, and the necessary and sufficient condition is established for the complete initial-value privacy on the extended observable subspace. Next, the inherent network nature of the considered linear system is explored, where each individual state corresponds to a node and the state and output matrices induce interaction and sensing graphs, leading to a network system. Under this network perspective, the previously established results are extended to establish the differential and complete initial-value privacy of local nodes. Moreover, it is shown that the complete initial-value privacy is generically determined by the network structure.
On the Equivalence of Contraction and Koopman Approaches for Nonlinear Stability and Control
In this paper we prove new connections between two frameworks for analysis and control of nonlinear systems: the Koopman operator framework and contraction analysis. Each method, in different ways, provides exact and global analyses of nonlinear systems by way of linear systems theory. The main results of this paper show equivalence between contraction and Koopman approaches for a wide class of stability analysis and control design problems. In particular: stability or stablisability in the Koopman framework implies the existence of a contraction metric (resp. control contraction metric) for the nonlinear system. Further in certain cases the converse holds: contraction implies the existence of a set of observables with which stability can verified via the Koopman framework. Furthermore, the converse claims are based on an novel relations between the Koopman method and construction of a Kazantzis-Kravaris-Luenberger observer.
Lei Wang obtained his Ph.D. from Zhejiang University, China in 2016. From 2014 to 2015 he was a visiting student at the University of Bologna, Italy. He worked as Research Fellow at Nanyang Technological University, Singapore from 2016 to 2018, and Senior Research Associate at University of Newcastle from 2018 to 2019. Since October of 2019, he has joined ACFR as a Research Fellow with Guodong. His research interests include nonlinear control and privacy preservation.
Ian R. Manchester received BE and PhD degrees in electrical engineering from the University of New South Wales in 2002 and 2006, respectively. From 2006-2009 he was a post-doctoral researcher at Umea University, Sweden, and from 2009-2012 he was a Research scientist at the Massachusetts Institute of Technology. Since 2012 he has been a faculty member at the University of Sydney, Australia, where he is currently Professor of Mechatronic Engineering, Director of the Australian Centre for Field Robotics, and Co-Director of the Sydney Institute for Robotics and Intelligent Systems. His current research interests are in algorithms for control, estimation, and learning of nonlinear dynamical systems, with applications in robotics, robust machine learning, biomedical engineering, and smart networked systems.