Journal of Process Control ( IF 4.2 ) Pub Date : 2022-06-11 , DOI: 10.1016/j.jprocont.2022.05.013 L. Morales, D. Pozo-Espín, J. Aguilar, M.D. R-Moreno
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.
中文翻译:
基于 LAMDA 控制的方法应用于建筑物 HVAC 系统的调节
HVAC(暖通空调)系统的控制通常很复杂,因为在某些情况下建模很困难,因为这些系统具有大量组件。热交换器、冷却器、阀门、传感器和执行器增加了整个模型的非线性特性,因此有必要提出新的控制策略来处理所有这些元素协同工作产生的不确定性。另一方面,人工智能是一种强大的工具,可以提高具有不精确模型和不确定性的控制系统的性能。本文介绍了基于 LAMDA(多变量数据分析学习算法)的 HVAC 系统的新控制方案。该算法已被用于机器学习领域,然而,我们利用其学习特性提出了不同类型的智能控制器,以提高整体控制系统在调节和参考变化任务中的性能。为了在 HVAC 系统的背景下进行比较分析,将 PID 和 Fuzzy-PID 等常规方法与 LAMDA-PID、LAMDA-基于 Z 数的滑模控制 (ZLSMC) 和自适应 LAMDA 进行了比较。具体来说,通过仿真实现了两个 HVAC 系统来评估提案:一个 MIMO(多输入多输出)HVAC 系统和一个具有死区时间的 HVAC 系统,用于定性和定量比较结果。结果表明,ZLSMC 是最稳健的控制器,它可以在参考值变化和存在干扰的情况下有效地控制 HVAC 系统。