Time: 10:00, Jan. 6, 2026
Venue: Room 209, IEI Building
Overview:
In real-time applications such as embedded systems and robotics, the online computation of Model Predictive Control (MPC) is often limited by solving multiparametric quadratic programming (mpQP) instances. Existing methods handle their constraints, incurring high computational cost, while many constraints are actually redundant and their removal does not change the optimal solution. Using solved mpQP instances, we propose a safe and optimality certifiable strategy for removing redundant constraints, thereby substantially reducing the scale of the mpQP instances to be solved. In particular, we prove that the number of constraints of mpQP of MPC reduces to zero for the constrained linear stabilization problem. Furthermore, we explicitly quantify the complexity of a ReLU network-based MPC policy to ensure the closed-loop performance in terms of network depth and width parameters. Both theoretical analysis and experiments confirm that the method maintains control performance while achieving computational speedup, offering an advanced solution for resource-constrained platforms.
Organizer: School of Electrical Engineering
Speaker:
You Keyou is a Tenured Professor and Ph.D. Supervisor at the Department of Automation, Tsinghua University, and a recipient of the National Science Fund for Distinguished Young Scholars. He received his B.S. degree in Statistics from Sun Yat-sen University (SYSU) in 2007. From August 2007 to June 2012, he pursued his Ph.D. degree and conducted postdoctoral research at School of Electrical and Electronic Engineering, Nanyang Technological University. Since July 2012, he has been a faculty member at Department of Automation, Tsinghua University. He has also been invited as a visiting scholar to institutions including Polytechnic University of Turin, University of Melbourne, and Hong Kong University of Science and Technology. Professor You has long been dedicated to research on learning, optimization, and control of complex networked systems. He currently serves as an Associate Editor for international journals including Automatica and IEEE Transactions on Control of Network Systems. He has led numerous research projects, including National Natural Science Foundation of China (NSFC) Distinguished Young Scholar and Key Program, Joint Research Fund, and the Young Scientists Program of the National Key R&D Program of China’s “New Generation Artificial Intelligence” Major Project. He has received the First Prize of the Natural Science Award of the Chinese Association of Automation (CAA), the Temasek Young Educator Award from the Asian Control Association, and the Guan Zhao-Zhi Best Paper Award. He has also contributed to three provincial- and ministerial-level First Prizes awarded by organizations including the Beijing Municipal Government, the Ministry of Education, and the Ministry of Natural Resources. His doctoral students have received honors including the IFAC Young Author Award, CAA Excellent Doctoral Dissertation Award, and the National Postdoctoral Innovative Talent Support Program.
