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京东 11.11 红包
Set-Based Methods for Hierarchical Model Predictive Control & Beyond by J Koeln
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Set-Based Methods for Hierarchical Model Predictive Control and Beyond by Justin Koeln September 29, 2023 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 ... 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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