TR2016-048
A Reconfigurable Plug-and-Play Model Predictive Controller for Multi-Evaporator Vapor Compression Systems
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- "A Reconfigurable Plug-and-Play Model Predictive Controller for Multi-Evaporator Vapor Compression Systems", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525270, July 2016, pp. 2358-2364.BibTeX TR2016-048 PDF
- @inproceedings{Zhou2016jul1,
- author = {Zhou, Junqiang and Burns, Daniel J. and Danielson, Claus and Di Cairano, Stefano},
- title = {A Reconfigurable Plug-and-Play Model Predictive Controller for Multi-Evaporator Vapor Compression Systems},
- booktitle = {American Control Conference (ACC)},
- year = 2016,
- pages = {2358--2364},
- month = jul,
- doi = {10.1109/ACC.2016.7525270},
- url = {https://www.merl.com/publications/TR2016-048}
- }
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- "A Reconfigurable Plug-and-Play Model Predictive Controller for Multi-Evaporator Vapor Compression Systems", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525270, July 2016, pp. 2358-2364.
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Abstract:
This paper presents a reconfigurable Plug-andPlay (PnP) Model Predictive Controller (MPC) for multievaporator vapor compression systems (VCS) where individual evaporators are permitted to turn on or off. This alters the number of performance variables, actuators and constraints. The proposed approach features structural online updates of the closed loop system with stability guarantees, and avoids the need to commission and tune separate controllers for when individual subsystems are turned on or off. To compare the performance of the proposed approach, a more conventional switched MPC is also developed in order to provide a benchmark design, wherein separate model representations are developed and controllers with numerous tuning parameters are synthesized and deployed depending on the VCS operation mode. Simulations are provided comparing the performance of the proposed reconfigurable PnP MPC to the traditionally-designed switched MPC. Results confirm that the reconfigurable PnP MPC maintains the same performance as the switched MPC approach in terms of room temperature reference tracking after zones are switched on, enforcement of critical machine constraints, and disturbance rejection. However, reconfigurable PnP MPC requires no extra tuning or controller design effort, and can be automatically synthesized from a single master controller design for any VCS operating mode.
Related News & Events
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NEWS MERL makes a strong showing at the American Control Conference Date: July 6, 2016 - July 8, 2016
Where: American Control Conference (ACC)
MERL Contacts: Scott A. Bortoff; Petros T. Boufounos; Stefano Di Cairano; Abraham Goldsmith; Christopher R. Laughman; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Machine LearningBrief- The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.