Research Interests
My research interests include:
· State Estimation/Machine Learning/Decision Making/Control Theory/Cyber-physical Systems/Safe Autonomous Systems
Research Projects​​​​​​​ (under construction)
Safe Planning for Autonomous Systems under Large Uncertainties
The motivation of this work is to enable the development of the new safety concept for autonomous systems, while the current one has been limited to collision avoidance. For instance, many industries utilize the constrained motion planning on their systems and could benefit from collision-free for safety, such as indoor navigation robots, follow-filming drones, and self-driving cars. But more often than not, only considering the collision avoidance at the planning level is not enough for safety-critical systems since the primary mission may not be feasible under large uncertainties.
 · Backup Plan Constrained Model Predictive Control (submitted to CDC 2021)
Safe Operation of Connected Vehicles in Complex and Unforeseen Environments
Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging, because AVs operate in dynamic environments with unforeseen challenges. This project seeks to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatio-temporal data fusion, machine learning, and robust adaptive control. 
Path Planning and Control for Multi-UAV Systems in GPS Denied Environment
UAVs have been used across the world for commercial, civilian, as well as educational applications over the decades. The most widely used sensor for UAVs is the global positioning system (GPS), which offers accurate and reliable state measurements. However, GPS receivers are vulnerable to various types of attacks, such as blocking, jamming, and spoofing. We present a secure safety constrained control framework that adapts the UAVs at a path re-planning level to support resilient state estimation against GPS spoofing attacks.
Attack-resilient Estimation and Detection for Cyber-physical Systems
Cyber-Physical Systems (CPS) have been of paramount importance in power systems, critical infrastructures, transportation networks and industrial control systems for many decades. CPS attacks have clearly illustrated the vulnerability of CPS and raised awareness of the security challenges in these systems. In this project, we aim to study and develop attack-resilient estimation and detection algorithms for time-varying stochastic systems.
Check Google Scholar for publications ​​
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