Approaches based on LAMDA control applied to regulate HVAC systems for buildings
Identificadores
Enlace permanente (URI): http://hdl.handle.net/10017/53392DOI: 10.1016/j.jprocont.2022.05.013
ISSN: 0959-1524
Editor
Elsevier
Fecha de publicación
2022-04-01Patrocinadores
European Commission
Agencia Estatal de Investigación
Junta de Comunidades de Castilla-La Mancha
Cita bibliográfica
Morales, L., Pozo-Espín, D., Aguilar Castro, J.L. & Rodríguez Moreno, M.D. 2022, “Approaches based on LAMDA control applied to regulate HVAC systems for buildings”, Journal of Process Control, vol. 116, pp. 34-52.
Palabras clave
LAMDA
SMC
Intelligent control
Nonlinear systems
HVAC systems
Proyectos
info:eu-repo/grantAgreement/EC/H2020/754382/EU/GOT Energy Talent/GET
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109891RB-I00/ES/MEJORA DE LA GESTION DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCION DE LA DEMANDA CON APRENDIZAJE AUTOMATICO Y PLANIFICACION/
info:eu-repo/grantAgreement/JCCM//SBPLY%2F19%2F180501%2F000024/ES/MEJORA DE LA GESTIÓN DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCIÓN DE LA DEMANDA CON APRENDIZAJE AUTOMÁTICO Y PLANIFICACIÓN
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.1016/j.jprocont.2022.05.013Derechos
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
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.
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