Wei Wang received her B.Eng degree in Electrical Engineering and Automation from Beihang University (China) in 2005, M.Sc degree in Radio Frequency Communication Systems with Distinction from University of Southampton (UK) in 2006 and Ph.D degree from Nanyang Technological University (Singapore) in 2011. From January 2012 to June 2015, she was a Lecturer with the Department of Automation at Tsinghua University, China. Since July 2015, she has been with the School of Automation Science and Electrical Engineering, Beihang University, China, where she is currently a Full Professor. Her research interests include adaptive control of uncertain systems, distributed cooperative control of multi-agent systems, secure control of cyber-physical systems, fault tolerant control, and robotic control systems. Prof. Wang received Zhang Si-Ying Outstanding Youth Paper Award in the 25th Chinese Control and Decision Conference (2013), the First Prize of Science and Technology Progress Award by Chinese Institute of Command and Control (CICC) in 2018, and the Second Prize of National Teaching Achievement Award (Higher Education). She has been serving as the Principle Investigator for a number of research projects including the National Science Fund for Excellent Young Scholars of China (2021-2023) and Associate Editors for IEEE Transactions on Industrial Electronics, ISA Transactions, IEEE Open Journal of Circuits and Systems, Journal of Control and Decision.
Safety critical control has gained considerable popularity in recent years due to its widespread applications. Most of currently available safety critical control protocols are developed assuming precise knowledge of system dynamics. This talk is mainly focused on safety critical control of uncertain nonlinear systems with applications to mobile robots.
To address potential conflicts between control objectives and safety constraints, the standard Control Barrier Functions (CBFs) often relax constraints on system states to ensure the forward invariant property for the safe set only, rather than for all of its subsets. Nonetheless, the robustness of the closed-loop systems is often reduced, which typically poses significant challenges to the design of disturbance rejection. Modified CBFs are proposed to address this problem, as they exhibit improved robustness close to the boundary of the safe set. The modified CBFs coupled with disturbance observers are employed to jointly resist the external disturbances, which can be seen as a combination of feedback and feedforward schemes.
Since Control Lyapunov Functions (CLFs) do not inherently possess the positive-definite property, the boundedness of parameter estimates cannot be guaranteed by simply following Lyapunov-based adaptive control. The parameter estimation errors may lead to misunderstanding regarding potential conflicts, further causing conservative control performance. To handle this issue, two distinct data-driven adaptive control schemes, Concurrent Learning and Safety-Triggered Batch Least-Squares Identifier, are employed. The excited component involved in the initial estimation error can thus be rapidly eliminated, thereby effectively reducing the conservativeness of safety-critical systems.