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I am a researcher as a Ph.D. candidate in Advanced Controls Research Laboratory at University of Illinois at Urbana-Champaign.
My main focus is developing state estimation/machine learning algorithms for autonomous systems.
Outside of the Ph.D. life, I enjoy listening to jazz and !
Short Bio
Wenbin Wan is a Ph.D. candidate in Advanced Controls Research Laboratory with the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UofI).
He received his B.Sc. in Mechanical Engineering from the University of Missouri-Columbia in 2016 and received his first master's degree in Mechanical Engineering in 2017 and the second master's degree in Applied Mathematics in 2020 from UofI.
Research Interests
Machine Learning
Decision Making
Control Theory
Estimation
Cyber-physical Systems
Safe Autonomous Systems
Research Projects
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.
Publications:
- 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.
Publications:
- Robust Vehicle Lane Keeping Control with Networked Proactive Adaptation (accepted at ACC 2021)
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.
Publications:
- Safety Constrained Multi-UAV Time Coordination: A Bi-level Control Framework in GPS Denied Environment (AIAA Aviation 2021)
- A Safety Constrained Control Framework for UAVs in GPS Denied Environment (CDC 2020)
- Towards Resilient UAV: Escape Time in GPS Denied Environment with Sensor Drift (ACA 2019)
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.
Publications:
Publications
Contact
Address:
Room 269, Computing Applications Bldg,
605 East Springfield Avenue,
Champaign, IL 61820, USA