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Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control

Author

Listed:
  • Chang-Soon Kang

    (Department of Electrical & Electronic Engineering, Yonsei University, Seoul 120-749, Korea)

  • Jong-Il Park

    (Department of Electrical & Electronic Engineering, Yonsei University, Seoul 120-749, Korea)

  • Mignon Park

    (Department of Electrical & Electronic Engineering, Yonsei University, Seoul 120-749, Korea)

  • Jaeho Baek

    (Advanced R&D Team, Samsung Electronics Co. Ltd., Suwon 443-742, Korea)

Abstract

Conventional heating, ventilating, and air conditioning (HVAC) systems have traditionally used the temperature and the humidity ratio as the quantitative indices of comfort in a room. Recently, the carbon dioxide (CO 2 ) concentration has also been recognized as having an important contribution to room comfort. This paper presents the modeling of an augmented HVAC system including CO 2 concentration, and its control strategies. Because the proposed augmented HVAC system is multi-input multi-output (MIMO) and has no relative degree problem, the dynamic extension algorithm can be employed; then, a feedback linearization technique is applied. A linear-quadratic regulator (LQR) is designed to optimize control performance and to stabilize the proposed HVAC system. Simulation results are provided to validate the proposed system model, as well as its linearized control system.

Suggested Citation

  • Chang-Soon Kang & Jong-Il Park & Mignon Park & Jaeho Baek, 2014. "Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control," Energies, MDPI, vol. 7(6), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:6:p:3599-3617:d:36704
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    Citations

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    Cited by:

    1. Thilker, Christian Ankerstjerne & Jørgensen, John Bagterp & Madsen, Henrik, 2022. "Linear quadratic Gaussian control with advanced continuous-time disturbance models for building thermal regulation," Applied Energy, Elsevier, vol. 327(C).
    2. Vepsäläinen, Jari & Otto, Kevin & Lajunen, Antti & Tammi, Kari, 2019. "Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions," Energy, Elsevier, vol. 169(C), pages 433-443.
    3. Awais Shah & Deqing Huang & Tianpeng Huang & Umar Farid, 2018. "Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems," Energies, MDPI, vol. 11(11), pages 1-18, October.
    4. Vaclav Kaczmarczyk & Zdenek Bradac & Petr Fiedler, 2017. "A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas," Energies, MDPI, vol. 10(7), pages 1-28, July.
    5. Lavanya, R. & Murukesh, C. & Shanker, N.R., 2023. "Microclimatic HVAC system for nano painted rooms using PSO based occupancy regression controller," Energy, Elsevier, vol. 278(PA).

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