IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1823-d118419.html
   My bibliography  Save this article

A Design of a Hybrid Non-Linear Control Algorithm

Author

Listed:
  • Farinaz Behrooz

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Centre for Advanced Power and Energy Research, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Norman Mariun

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Centre for Advanced Power and Energy Research, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Mohammad Hamiruce Marhaban

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Mohd Amran Mohd Radzi

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Abdul Rahman Ramli

    (Department of Computer and Communication Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

Abstract

One of the high energy consuming devices in the buildings is the air-conditioning system. Designing a proper controller to consider the thermal comfort and simultaneously control the energy usage of the device will impact on the system energy efficiency and its performance. The aim of this study was to design a Multiple-Input and Multiple-Output (MIMO), non-linear, and intelligent controller on direct expansion air-conditioning system The control algorithm uses the Fuzzy Cognitive Map method as a main controller and the Generalized Predictive Control method is used for assigning the initial weights of the main controller. The results of the proposed controller shows that the controller was successfully designed and works in set point tracking and under disturbance rejection tests. The obtained results of the Generalized Predictive Control-Fuzzy Cognitive Map controller are compared with the previous MIMO Linear Quadratic Gaussian control design on the same direct expansion air-conditioning system under the same conditions. The comparative results indicate energy savings would be achieved with the proposed controller with long-term usage. Energy efficiency and thermal comfort conditions are achieved by the proposed controller.

Suggested Citation

  • Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2017. "A Design of a Hybrid Non-Linear Control Algorithm," Energies, MDPI, vol. 10(11), pages 1-32, November.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1823-:d:118419
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1823/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/11/1823/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daghigh, R., 2015. "Assessing the thermal comfort and ventilation in Malaysia and the surrounding regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 681-691.
    2. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    2. Cylon Liaw & Vitória Elisa da Silva & Rebecca Maduro & Milena Megrè & Julio Cesar de Souza Inácio Gonçalves & Edmilson Moutinho dos Santos & Dominique Mouette, 2023. "Thermal Comfort Analysis Using System Dynamics Modeling—A Sustainable Scenario Proposition for Low-Income Housing in Brazil," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    3. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    4. Payam Nejat & Fatemeh Jomehzadeh & Hasanen Mohammed Hussen & John Kaiser Calautit & Muhd Zaimi Abd Majid, 2018. "Application of Wind as a Renewable Energy Source for Passive Cooling through Windcatchers Integrated with Wing Walls," Energies, MDPI, vol. 11(10), pages 1-23, September.
    5. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    6. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    7. Han, H.J. & Jeon, Y.I. & Lim, S.H. & Kim, W.W. & Chen, K., 2010. "New developments in illumination, heating and cooling technologies for energy-efficient buildings," Energy, Elsevier, vol. 35(6), pages 2647-2653.
    8. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    9. Jomehzadeh, Fatemeh & Nejat, Payam & Calautit, John Kaiser & Yusof, Mohd Badruddin Mohd & Zaki, Sheikh Ahmad & Hughes, Ben Richard & Yazid, Muhammad Noor Afiq Witri Muhammad, 2017. "A review on windcatcher for passive cooling and natural ventilation in buildings, Part 1: Indoor air quality and thermal comfort assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 736-756.
    10. Homod, Raad Z., 2014. "Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq," Energy, Elsevier, vol. 74(C), pages 762-774.
    11. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2022. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives," Energies, MDPI, vol. 15(4), pages 1-27, February.
    12. Chi, Fang'ai & Pan, Jiajie & Liu, Yang & Guo, Yuang, 2021. "Improvement of thermal comfort by hydraulic-driven ventilation device and space partition arrangement towards building energy saving," Applied Energy, Elsevier, vol. 299(C).
    13. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    14. Blad, C. & Bøgh, S. & Kallesøe, C. & Raftery, Paul, 2023. "A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems," Applied Energy, Elsevier, vol. 337(C).
    15. Piotr Michalak, 2022. "Thermal Network Model for an Assessment of Summer Indoor Comfort in a Naturally Ventilated Residential Building," Energies, MDPI, vol. 15(10), pages 1-19, May.
    16. D’Oca, Simona & Hong, Tianzhen & Langevin, Jared, 2018. "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 731-742.
    17. Liang, Cai-Hang & Zhang, Li-Zhi & Pei, Li-Xia, 2010. "Performance analysis of a direct expansion air dehumidification system combined with membrane-based total heat recovery," Energy, Elsevier, vol. 35(9), pages 3891-3901.
    18. Khan, Muhammad Waqas & Choudhry, Mohammad Ahmad & Zeeshan, Muhammad & Ali, Ahsan, 2015. "Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit," Energy, Elsevier, vol. 81(C), pages 477-488.
    19. Mukhtar, A. & Ng, K.C. & Yusoff, M.Z., 2018. "Design optimization for ventilation shafts of naturally-ventilated underground shelters for improvement of ventilation rate and thermal comfort," Renewable Energy, Elsevier, vol. 115(C), pages 183-198.
    20. Afram, Abdul & Janabi-Sharifi, Farrokh, 2015. "Gray-box modeling and validation of residential HVAC system for control system design," Applied Energy, Elsevier, vol. 137(C), pages 134-150.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1823-:d:118419. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.