Set-Based Methods for Hierarchical Model Predictive Control and Beyond
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Abstract: Model Predictive Control (MPC) is a leading approach for the control of constrained systems, where input and state constraints are directly imposed in the underlying optimization problem. Guaranteed constraint satisfaction and stability of the closed-loop system are well understood for the case of a single centralized controller. When the complexity of a system prohibits a centralized control approach, hierarchical MPC can be used to decompose control decisions across multiple levels of controllers. However, with a complex network of interacting MPC controllers operating at different timescales, it becomes very challenging to design each individual controller such that constraint satisfaction and stability of the overall closed-loop system can be guaranteed.
This talk presents how set-based coordination mechanisms can be used within a hierarchical MPC framework to provide guaranteed feasibility of each controller in the hierarchy and the satisfaction of state and input constraints for the closed-loop system. In particular, it is shown how the unique features of zonotope and constrained zonotope set representations are key enablers of the proposed set-based coordination mechanisms. Recently developed hybrid zonotope set representations are also presented as a new tool for linear, hybrid, and nonlinear system analysis. Several numerical examples are used to demonstrate the key features, performance, and scalability of set-based approaches to control design and analysis.
Bio: Justin Koeln received his B.S. degree in 2011 from Utah State University in Mechanical and Aerospace Engineering. He received M.S. and Ph.D. degrees in 2013 and 2016, respectively, from the University of Illinois at Urbana–Champaign in Mechanical Science and Engineering. He is an Assistant Professor at the University of Texas at Dallas in the Mechanical Engineering Department. He was a NSF Graduate Research Fellow and a Summer Faculty Fellow with the Air Force Research Laboratory. He was a recipient of the 2022 Office of Naval Research Young Investigator Award. His research interests include dynamic modeling and control of thermal management systems, model predictive control, set-based methods, and hierarchical and distributed control for electro-thermal systems.
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