Hybrid event – join us in person at M07 Mezzanine Level at Melbourne Connect, Carlton

Title: Efficient optimisation algorithms for Model Predictive Control with limited online resources

Speaker: Dr Ye Pu

Summary:
Model predictive control (MPC), an optimisation-based control technique, has been successfully applied to modern control applications due to its flexibility in defining suitable control objectives and the ability to explicitly take constraints into account. However, when MPC is applied to systems with fast dynamics, it faces the challenge of computing an optimal or stabilizing control law with limited online resources, such as computational power, data storage space, or communication channels. In this work, we investigate the so-called splitting methods (alternating direction methods), whose efficiency results from breaking down a complex convex minimisation problem into simple sub-problems and solving them in an alternating manner, and develop efficient optimisation algorithms for solving real-time MPC problems with limited computational time and communication data-rates. We also derive complexity upper bounds on the number of iterations to achieve a certain sub-optimality guarantee for the proposed algorithms. These upper bounds will be used as Lyapunov functions of the optimisation algorithms in our further stability analysis of the closed-loop controlled systems. Lastly, I introduce a sub-optimal MPC framework and show its theoretical analysis on system gains and an offline determined upper-bound on the number of optimization iterations, i.e., computational or communication cost, at each time step to guarantee asymptotic stability.

Biography:
Dr Ye Pu is a Senior Lecturer in the Department of Electrical and Electronic Engineering at the University of Melbourne. Prior to joining Melbourne, she was a post-doctoral researcher in the Berkeley Artificial Intelligence Research (BAIR) Lab in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, working on distributed optimisation and safe control from 2016 to 2018. She received a BS degree and an MS degree in Electrical Engineering from Shanghai Jiao Tong University, China, in 2008 and the Technical University of Berlin, Germany, in 2011, and a Ph.D. degree from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, for her work “Splitting methods for distributed optimisation and control” in 2016. She received the Swiss National Science Foundation – Early Postdoc Mobility Fellowship from 2016 to 2018 and is the recipient of an ARC Discovery Early Career Researcher Award (DECRA) from 2022 to 2025.

MEETING ID: 873 1557 5255; PASSWORD: 778635

WED 20 MARCH 2024 16:00-17:00 (AEDT, Melbourne Time)