Multi-evaporator vapor compression systems (ME-VCS) are inherently multi-input multi-output (MIMO) systems, often with complex, highly coupled dynamics. Thus, they require more sophisticated control schemes than traditional on-off logic, or decentralized proportional-integral controllers. Unfortunately, many MIMO control design techniques are not well suited for this problem since they require complex numerical computations that do not scale gracefully for the high-dimensional dynamics of ME-VCS systems. This paper exploits the observed similarity of the room dynamics to reduce the computational complexity of designing controllers. We use a linear matrix inequality based controller synthesis technique that exploits symmetry for designing controllers for large-scale ME-VCS systems. This controller synthesis technique was applied to an ME-VCS system with 50 rooms. Using tradition control design methods required 41 hours to synthesize a controller, while our technique designed an identical controller in less than 1 second.
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ASME 2017 Dynamic Systems and Control Conference
October 11–13, 2017
Tysons, Virginia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5827-1
PROCEEDINGS PAPER
Symmetric Control Design for Multi-Evaporator Vapor Compression Systems
Claus Danielson
Claus Danielson
Mitsubishi Electric Research Laboratories, Cambridge, MA
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Claus Danielson
Mitsubishi Electric Research Laboratories, Cambridge, MA
Paper No:
DSCC2017-5167, V001T03A002; 10 pages
Published Online:
November 14, 2017
Citation
Danielson, C. "Symmetric Control Design for Multi-Evaporator Vapor Compression Systems." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems. Tysons, Virginia, USA. October 11–13, 2017. V001T03A002. ASME. https://doi.org/10.1115/DSCC2017-5167
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