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Approaches based on LAMDA control applied to regulate HVAC systems for buildings.

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Title: Approaches based on LAMDA control applied to regulate HVAC systems for buildings.
Authors: Morales, L.1 (AUTHOR) luis.moralesec@epn.edu.ec, Pozo-Espín, D.2 (AUTHOR) david.pozo@udla.edu.ec, Aguilar, J.1,3,4,5 (AUTHOR) aguilar@ula.ve, R-Moreno, M.D.5,6 (AUTHOR) malola.rmoreno@uah.es
Source: Journal of Process Control. Aug2022, Vol. 116, p34-52. 19p.
Subjects: Machine learning, Valves, Intelligent control systems, Heating & ventilation industry, Heat exchangers, Artificial intelligence, Air conditioning
Abstract: The control of HVAC (Heating Ventilation and Air Conditioning) systems is usually complex because its modeling in certain cases is difficult, since these systems have a large number of components. Heat exchangers, chillers, valves, sensors, and actuators, increase the non-linear characteristics of the complete model, so it is necessary to propose new control strategies that can handle the uncertainty generated by all these elements working together. On the other hand, artificial intelligence is a powerful tool that allows improving the performance of control systems with inexact models and uncertainties. This paper presents new control alternatives for HVAC systems based on LAMDA (Learning Algorithm for Multivariable Data Analysis). This algorithm has been used in the field of machine learning, however, we have taken advantage of its learning characteristics to propose different types of intelligent controllers to improve the performance of the overall control system in tasks of regulation and reference change. In order to perform a comparative analysis in the context of HVAC systems, conventional methods such as PID and Fuzzy-PID are compared with LAMDA-PID, LAMDA-Sliding Mode Control based on Z-numbers (ZLSMC), and Adaptive LAMDA. Specifically, two HVAC systems are implemented by simulations to evaluate the proposals: an MIMO (Multiple-input Multiple-output) HVAC system and an HVAC system with dead time, which are used to compare the results qualitatively and quantitatively. The results show that ZLSMC is the most robust controller, which efficiently controls HVAC systems in cases of reference changes and the presence of disturbances. • New control alternatives for HVAC systems based on fuzzy approaches. • Learning Algorithm for Multivariable Data Analysis (LAMDA) in the definition of intelligent controllers. • MIMO (Multiple-input Multiple-output) HVAC and HVAC with dead time controlled by LAMDA approaches. • Utilization of LAMDA approaches for fuzzy modelling and control of HVAC systems. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Process Control is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 158158017
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Approaches based on LAMDA control applied to regulate HVAC systems for buildings.
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  Data: <searchLink fieldCode="AR" term="%22Morales%2C+L%2E%22">Morales, L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> luis.moralesec@epn.edu.ec</i><br /><searchLink fieldCode="AR" term="%22Pozo-Espín%2C+D%2E%22">Pozo-Espín, D.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> david.pozo@udla.edu.ec</i><br /><searchLink fieldCode="AR" term="%22Aguilar%2C+J%2E%22">Aguilar, J.</searchLink><relatesTo>1,3,4,5</relatesTo> (AUTHOR)<i> aguilar@ula.ve</i><br /><searchLink fieldCode="AR" term="%22R-Moreno%2C+M%2ED%2E%22">R-Moreno, M.D.</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<i> malola.rmoreno@uah.es</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Process+Control%22">Journal of Process Control</searchLink>. Aug2022, Vol. 116, p34-52. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Valves%22">Valves</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+control+systems%22">Intelligent control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Heating+%26+ventilation+industry%22">Heating & ventilation industry</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+exchangers%22">Heat exchangers</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Air+conditioning%22">Air conditioning</searchLink>
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  Data: The control of HVAC (Heating Ventilation and Air Conditioning) systems is usually complex because its modeling in certain cases is difficult, since these systems have a large number of components. Heat exchangers, chillers, valves, sensors, and actuators, increase the non-linear characteristics of the complete model, so it is necessary to propose new control strategies that can handle the uncertainty generated by all these elements working together. On the other hand, artificial intelligence is a powerful tool that allows improving the performance of control systems with inexact models and uncertainties. This paper presents new control alternatives for HVAC systems based on LAMDA (Learning Algorithm for Multivariable Data Analysis). This algorithm has been used in the field of machine learning, however, we have taken advantage of its learning characteristics to propose different types of intelligent controllers to improve the performance of the overall control system in tasks of regulation and reference change. In order to perform a comparative analysis in the context of HVAC systems, conventional methods such as PID and Fuzzy-PID are compared with LAMDA-PID, LAMDA-Sliding Mode Control based on Z-numbers (ZLSMC), and Adaptive LAMDA. Specifically, two HVAC systems are implemented by simulations to evaluate the proposals: an MIMO (Multiple-input Multiple-output) HVAC system and an HVAC system with dead time, which are used to compare the results qualitatively and quantitatively. The results show that ZLSMC is the most robust controller, which efficiently controls HVAC systems in cases of reference changes and the presence of disturbances. • New control alternatives for HVAC systems based on fuzzy approaches. • Learning Algorithm for Multivariable Data Analysis (LAMDA) in the definition of intelligent controllers. • MIMO (Multiple-input Multiple-output) HVAC and HVAC with dead time controlled by LAMDA approaches. • Utilization of LAMDA approaches for fuzzy modelling and control of HVAC systems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Process Control is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.jprocont.2022.05.013
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 34
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Valves
        Type: general
      – SubjectFull: Intelligent control systems
        Type: general
      – SubjectFull: Heating & ventilation industry
        Type: general
      – SubjectFull: Heat exchangers
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Air conditioning
        Type: general
    Titles:
      – TitleFull: Approaches based on LAMDA control applied to regulate HVAC systems for buildings.
        Type: main
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          Name:
            NameFull: Morales, L.
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            NameFull: Pozo-Espín, D.
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            NameFull: Aguilar, J.
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            NameFull: R-Moreno, M.D.
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            – D: 01
              M: 08
              Text: Aug2022
              Type: published
              Y: 2022
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              Value: 116
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            – TitleFull: Journal of Process Control
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