Jun Liu


Canada Research Chair in Hybrid Systems and Control

Tier 2 - 2017-11-01
University of Waterloo
Natural Sciences and Engineering Research Council

519 888 4567 ext. 37550
j.liu@uwaterloo.ca

Coming to Canada From


University of Sheffield, UK

Research involves


Developing validated computational methods for rigorous analysis of and robust control design for cyber-physical systems, with applications in autonomous systems and robotics.

Research relevance


The research will lead to more efficient design methods for complex cyber-physical systems (CPS) and enhance the competitiveness of certain industrial segments involving CPS applications.

Enhancing the capabilities of applications controlled by computer algorithms


Cyber-physical systems (CPS) are physical systems controlled by computer-based algorithms. Such systems are increasingly becoming woven into our daily lives: unmanned air vehicles executing search and rescue missions, self-driving cars navigating through busy traffic, and surgical robots performing live-saving operations. These are just a few examples how CPS can lead to innovative applications.

Dr. Jun Liu, Canada Research Chair in Hybrid Systems and Control, is developing advanced control algorithms to ensure that CPS meet their safety, reliability and performance requirements.

Often, the design of such control algorithms is only made possible through extensive experience, laborious testing and the fine tuning of parameters. Yet, the resulting closed-loop system has no formal guarantee of meeting specifications.

To address this challenge, Liu’s research focuses on developing validated computational methods for rigorous analysis of, and provably correct control design for, CPS to meet their safety and performance specifications, efficiently. He draws on control theory from applied mathematics, as well as formal methods from computer science, to design hybrid control algorithms for CPS. He uses realistic case studies in multi-robot systems to validate his design methodologies.

The results from the research by Liu’s team will lead to more efficient control design paradigms for CPS that have the potential to significantly reduce their development time and cost, and safely improve their run-time performance. These design methodologies will also enhance the competitiveness of industrial segments that require a tight integration between hardware and advanced control algorithms, such as in the automotive, aerospace, energy, medical, and robotics fields.