Modeling, Analysis, and State Feedback Control Design of a Multizone HVAC System

Abdo-Allah, Almahdi; Iqbal, M. Tariq; Pope, Kevin

doi:10.1155/2018/4303580

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Data-driven HVAC can readily identify strategies for system refinement and enhancement. These types of model determination are termed system identification (SI) in the literature (ASHRAE, 2005) [5].…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Analysis of a Large Building at Memorial University

2019

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In this paper, energy consumption analysis and a process to identify appropriate models based on heat dynamics for large structures are presented. The analysis uses data from heating, ventilation, and air-conditioning (HVAC) system sensors, as well as data from the indoor climate and energy software (IDA Indoor Climate and Energy (IDA-ICE) 4.7 simulation program). Energy consumption data (e.g., power and hot water usage) agrees well with the new models. The model is applicable in a variety of applications, such as forecasting energy consumption and controlling indoor climate. In the study, both data-derived models and a grey-box model are tested, producing a complex building model with high accuracy. Also, a case study of the S. J. Carew building at Memorial University, St. John’s, Newfoundland, is presented.

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Section: Introductionmentioning

confidence: 99%

Energy Consumption Analysis of a Large Building at Memorial University

2019

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“…The parameters, variables, state and control are described in Tables 1 and 2 [7]. 𝑊 𝑜𝑎 = 0.018 𝑘𝑔 𝐻 2 𝑂 /𝑘𝑔 𝑑𝑟𝑦 𝑎𝑖𝑟 𝑉 𝑐𝑢 = 1.7198 𝑚 3 𝑊 𝑠𝑎 = 0.007𝑘𝑔 𝐻 2 𝑂/ 𝑘𝑔 𝑑𝑟𝑦 𝑎𝑖𝑟 𝜌 𝑤𝑑 = 1000𝑘𝑔/𝑚 3 𝑊 𝑖𝑎 = 0.00924𝑘𝑔 𝐻 2 𝑂/𝑘𝑔 𝑑𝑟𝑦 𝑎𝑖𝑟 𝜌 𝑎𝑑 = 1.185𝑘𝑔/𝑚 3 𝑇 𝑜𝑎 = 32℃ ℎ 𝑣 = 2500.45𝑘𝐽/𝑘𝑔 𝑇 𝑠𝑎 = 17℃ ℎ 𝑠 = 790.84𝑘𝐽/𝑘𝑔 𝑇 𝑖𝑎 = 21℃ 𝐶 𝑤 = 4.183𝑘𝐽/𝑘𝑔 ℃ 𝛿𝑇 𝑐𝑢 = 6℃ 𝐶 𝑎 = 1.004𝑘𝐽/𝑘𝑔 ℃ 𝑀 ̇𝑙 = 0.021 𝑘𝑔/ 𝑠 𝑓 ̇𝑎𝑟 = 8.02𝑚 3 / 𝑠 𝐻 ̇𝑙 = 84.93 𝑘𝑔/𝑠 𝑓 ̇𝑤𝑟 = 0.00366𝑚 3 / 𝑠 ℓ = 25% 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑚𝑖𝑥𝑒𝑑 𝑎𝑖𝑟…”

Section: System Description and Modelingmentioning

confidence: 99%

“…Data-driven HVAC can identify strategies for system clarification and enhancement which is termed system identification (SI) in the literature [2]. Data was used to create a mathematical-based HVAC system which examine input and output variables to identify and set system parameters [3].…”

Section: Introductionmentioning

confidence: 99%

Robust Nonlinear Control Design for the HVAC System Based on Adaptive Sliding Mode Control

Hassan¹,

Al-Samarraie²

2022

JESA

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Control of Heating, Ventilating, and Air Conditioning (HVAC) aims to provide a comfortable environment for human life in terms of temperature and humidity and improve indoor air quality. The HVAC system is multi-input multi-output, where the control design of this system is challenging due to its strong nonlinearity and the coupled influence of both system controllers on the temperature within the thermal zone. The aim of this study is to design a dual-controller for the HVAC system. The first controller is a non-linear feedback controller which is devoted to control the humidity ratio of the thermal zone with the desired characteristic. While for the second one, a robust controller is designed to maintain the desired thermal zone temperature based on the adaptive sliding mode controller (ASMC). Using the ASMC enabled us to design the second controller without the need to know the uncertainty bound on the HVAC system model. Additionally, the stability of the proposed control system was verified using the Lyapunov theory. To construct the sliding variable for the temperature control, the error state which is the difference between the thermal zoon temperature and the desired value and its derivative is needed. Due to the uncertainty in the error state derivative, a robust differentiator was designed using the approximate classical sliding mode differentiator (ACSMD). Finally, the performance of the control system is confirmed via numerical simulation. The results showed the robust ability of the control system to make the humidity and temperature of the thermal area follow the required values and with high accuracy.

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“…It will investigate a heat model that is dependent on a range of parameters, a three-dimensional (3D) model, and IDA ICE model library components. The present work will also examine results from [16,17], which used real data as a basis for developing whole structures.…”

Section: Journal Of Energymentioning

confidence: 99%

“…A more detailed description of the structure and amenities of the S.J. Carew building can be found in earlier studies [16,17]. As the building's HVAC system is based on the IDA ICE program, good approximation results can be obtained from the model regarding power and hot water data, which can then be compared to real data.…”

Section: Journal Of Energymentioning

confidence: 99%

Modeling, Analysis, and Design of a Fuzzy Logic Controller for an AHU in the S.J. Carew Building at Memorial University

2018

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Proper functioning of heating, ventilation, and air conditioning (HVAC) systems is important for efficient thermal management, as well as operational costs. Most of these systems use nonlinear time variances to handle disturbances, along with controllers that try to balance rise times and stability. The latest generation of fuzzy logic controllers (FLC) is algorithm-based and is used to control indoor temperatures, CO 2 concentrations in air handling units (AHUs), and fan speeds. These types of controllers work through the manipulation of dampers, fans, and valves to adjust flow rates of water and air. In this paper, modulating equal percentage globe valves, fans speed, and dampers position have been modeled according to exact flow rates of hot water and air into the building, and a new approach to adapting FLC through the modification of fuzzy rules surface is presented. The novel system is a redesign of an FLC using MATLAB/Simulink, with the results showing an enhancement in thermal comfort levels.

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Modeling, Analysis, and State Feedback Control Design of a Multizone HVAC System

Cited by 6 publications

References 19 publications

Energy Consumption Analysis of a Large Building at Memorial University

Energy Consumption Analysis of a Large Building at Memorial University

Robust Nonlinear Control Design for the HVAC System Based on Adaptive Sliding Mode Control

Modeling, Analysis, and Design of a Fuzzy Logic Controller for an AHU in the S.J. Carew Building at Memorial University

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