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Model Predictive Control of Multi-zone Vapor Compression Systems

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Intelligent Building Control Systems

Part of the book series: Advances in Industrial Control ((AIC))

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Abstract

While the previous chapter presented modeling and control strategies for vapor compression systems in general, in this chapter, a model predictive controller is designed for a multi-zone vapor compression system. Controller requirements representing desired performance of production-scale equipment are provided and include baseline requirements common in control literature (constraint enforcement, reference tracking, disturbance rejection) and also extended requirements necessary for commercial application (selectively deactivating zones, implementable on embedded processors with limited memory/computation, compatibility with demand response events.). A controller architecture is presented based on model predictive control to meet the requirements. Experiments are presented validating constraint enforcement and automatic deactivation of zones.

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Authors and Affiliations

  1. Mitsubishi Electric Research Laboratories, 201 Broadway, Cambridge, MA, USA

    Daniel J. Burns, Claus Danielson, Stefano Di Cairano, Christopher R. Laughman & Scott A. Bortoff

Authors
  1. Daniel J. Burns
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  2. Claus Danielson
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  3. Stefano Di Cairano
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  4. Christopher R. Laughman
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  5. Scott A. Bortoff
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Corresponding author

Correspondence to Daniel J. Burns .

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Editors and Affiliations

  1. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA

    John T. Wen

  2. Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA

    Sandipan Mishra

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Burns, D.J., Danielson, C., Di Cairano, S., Laughman, C.R., Bortoff, S.A. (2018). Model Predictive Control of Multi-zone Vapor Compression Systems. In: Wen, J., Mishra, S. (eds) Intelligent Building Control Systems. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-68462-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-68462-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68461-1

  • Online ISBN: 978-3-319-68462-8

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