Figure 2 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
In order to achieve feasible and copacetic low energy consuming building, a robust and efficient air conditioning system is necessary. Since heating ventilation and air conditioning systems are nonlinear and temperature and humidity are coupled, application of conventional control is inappropriate. A multi-input multi-output nonlinear model is pres...
Similar publications
With the development of automatic control in the HVAC industry, more and more buildings have adopted automatic control of air conditioning, HVAC systems, but most of the control results can not be satisfactory. The research object of this paper is the elevator machine room equipped HVAC, through accurate control of that room temperature can greatly...
The Heating, Ventilating and Air Conditioning (HVAC) system has large thermal inertia, pure lag time and nonlinear characteristic. Furthermore the plant is susceptible to uncertain disturbance factors and sensor deterioration, which affect the control performance. The main objective of this proposed study is to examine the development and design pr...
Abstract
This article presents a novel technique for the fast tuning of the parameters of the proportional–integral–derivative (PID) controller of a second-order heat, ventilation, and air conditioning (HVAC) system. The HVAC systems vary greatly in size, control functions and the amount of consumed energy. The optimal design and power efficiency o...
There is a pressing need for energy management with regard to modern rail vehicles, particularly in the case of electric vehicles (EVs), where various forms of energy and loads have to be considered for vehicle running. Air conditioning is an essential factor that ought to be profoundly analysed, as it can account for a significant proportion of el...
Citations
... However, to adjust the humidity, the temperature will change as well (and vice versa) so it is recommended by [44] to introduce two controllers with a self-turning parameter for two feedback control loops of temperature and Humidity with CO 2 as subsystems. Therefore, the decoupling control principal is recommended by the literature when it is difficult to avoid the impact of the temperature on the dehumidification process for complex systems in commercial buildings [46], this concept was validated experimentally by [42]. This section gives the design of u 1 in (3) and u 2 in (4) for the temperature and humidity control, respectively, of the air conditioning system using the output feedback finite time composite control (FTCC) based on a novel robust finite time disturbance observer (RFTDO). ...
... Based on the literature [42][43][44][45][46], the air-conditioning systems consists of one air handling unit (AHU) which supplies air to VAV terminals using the supply and return fans and the VAV system which is cooled by chilled water coming from the AHU coil. Therefore, the desired condition within the system is regulated by local controllers. ...
... However, to adjust the humidity, the temperature will change as well (and vice versa) so it is recommended by [44] to introduce two controllers with a self-turning parameter for two feedback control loops of temperature and Humidity with CO2 as subsystems. Therefore, the decoupling control principal is recommended by the literature when it is difficult to avoid the impact of the temperature on the dehumidification process for complex systems in commercial buildings [46], this concept was validated experimentally by [42]. This section gives the design of 1 in (3) and 2 in (4) for the temperature and humidity control, respectively, of the air conditioning system using the output feedback finite time composite control (FTCC) based on a novel robust finite time disturbance observer (RFTDO). ...
A novel robust finite time disturbance observer (RFTDO) based on an independent output-finite time composite control (FTCC) scheme is proposed for an air conditioning-system temperature and humidity regulation. The variable air volume (VAV) of the system is represented by two first-order mathematical models for the temperature and humidity dynamics. In the temperature loop dynamics, a RFTDO temperature (RFTDO-T) and an FTCC temperature (FTCC-T) are designed to estimate and reject the lumped disturbances of the temperature subsystem. In the humidity loop, a robust output of the FTCC humidity (FTCC-H) and RFTDO humidity (RFTDO-H) are also designed to estimate and reject the lumped disturbances of the humidity subsystem. Based on Lyapunov theory, the stability proof of the two closed-loop controllers and observers is presented. Comparative simulations are carried out to confirm that the proposed controller outperforms conventional methods and offers greater accuracy of temperature, humidity, and carbon dioxide concentration, having superior regulation performance in terms of a rapid finite time convergence, an outstanding disturbance rejection property, and better energy consumption. In addition to presenting the comparative simulation results from the control applications on the VAV system, the quantitative values are provided to further confirm the superiority of the proposed controller. In particular, the proposed method exhibits the shortest settling time of, respectively, 15 and 40 min to reach the expected temperature and humidity, whereas other comparative controllers require a longer time to settle down.
... Figure 1 which presents a schematic view of an air-handling unit having one zone (indoor) in HVAC. HVAC system for single thermal load consist of a heat exchanger, a chiller, which provides chilled water to the heat exchanger; a circulating air fan; the thermal space; connecting ductwork; dampers; and mixing air components [9]. A mixed air of 25% of fresh air with 75% of recirculating air passes through the heat exchanger. ...
... Many assumptions must be considered [9]: 1) Ideal gas behavior; 2) Mixing air should be perfect; 3) Fixed pressure process; 4) Wall and thermal storage must be neglected; 5) thermal losses between components should be negligible; 6) Infiltration and exfiltration effects is neglected; and 7) negligible transient effects in the flow splitter and mixer. ...
... The dynamic modeling of the AHU is given in studies [9,11,21], by the following differential equations. ...
... An SMC and PID approach has been designed for optimization of building energy consumption in [34] for a multizone VAV air-conditioning system. Robust SMC and PID have been implemented to ensure the robust performance of the air handling unit in the presence of uncertainties [35]. The authors in [36] demonstrated a super-twisting SMC for the control of an HVAC system evaporator two-phase length and superheat temperature. ...
In critical healthcare units, such as operation theaters and intensive care units, healthcare workers require specific temperature environments at different stages of an operation, which depends upon the condition of the patient and the requirements of the surgical procedures. Therefore, the need for a dynamically controlled temperature environment and the availability of the required heating/cooling electric power is relatively more necessary for the provision of a better healthcare environment as compared to other commercial and residential buildings, where only comfortable room temperature is required. In order to establish a dynamic temperature zone, a setpoint regulator is required that can control the zone temperature with a fast dynamic response, little overshoot, and a low settling time. Thus, two zone temperature regulators have been proposed in this article, including double integral sliding mode control (DISMC) and integral terminal sliding mode control (ITSMC). A realistic scenario of a hospital operation theater is considered for evaluating their responses and performance to desired temperature setpoints. The performance analysis and superiority of the proposed controllers have been established by comparison with an already installed Johnson temperature controller (JTC) for various time spans and specific environmental conditions that require setpoints based on doctors' and patients' desires. The proposed controllers showed minimal overshoot and a fast settling response, making them ideal controllers for operation theater (OT) zone temperature control.
... In the case of reinforcement learning with demand response, energy consumption is increased and decreased by up to 50%, keeping thermal comfort within suitable limits. The SMC scheme is used to demonstrate robustness against uncertainties for temperature and humidity set points in the Air Handling Unit (AHU) [25].Optimal values for temperature and humidity are achieved by regulating the airflow and water flow rate in the AHU. SMC outperformed PID in terms of energy consumption due to its shorter settling time and lower overshoot for air and water flow rates in AHU. ...
Heating, Ventilation and Air Conditioning (HVAC) systems account for 59% of energy consumption in domestic buildings and 36% in non-domestic buildings. According to a study, around 39% of occupants are dissatisfied with indoor temperature in non-domestic buildings. To maintain thermal comfort and indoor air quality, HVAC systems are widely used in non-domestic buildings. This research aims to develop energy-efficient control techniques for HVAC systems while ensuring indoor thermal comfort. Three control strategies, namely EnergyPlus model-based Model Predictive Control (MPC), Sliding Mode Control (SMC), and simple ON/OFF control, are employed and compared at the Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus. Furthermore, a machine learning-based Predicted Mean Vote (PMV)-based temperature setpoint estimator is designed to ensure occupant thermal comfort. The control techniques estimate the temperature setpoints and supply air temperature of the Variable Air Volume (VAV) system to control indoor room temperature. The energy consumption and indoor thermal comfort of the building are compared under different control techniques. The results show that MPC with PMV-based setpoints consumes 17.20% less energy during winters and 14.67% less energy during summers than a simple ON/OFF controller.
... The SMC needs a proper control law such trajectory moves toward the sliding surface and reaches in a finite time and stays on. Sliding mode control is introduced by Shah et al. [17]. The proposed controller performance compared with performance of PID controller to prove the advantage of using SMC to improving the desired tracking was shown in the paper [18]. ...
... Depending on the laws of mass and heat transfer, the dynamic behavior of the HVAC system can be described by the following differential equations. are given by [11,12,14,17]. ...
... In this work, the ACSMD is used to estimate e3 as presented in section (5.4). Remark (6): according to Eq. (17), and by considering that u2 is a positive quantity, then u2 must follow the following rule: ...
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.
... As the pioneers, Mohammed et al. and Betzaida et al. have given the basic nonlinear dynamic models since 1990s, respectively [5,6]. From then on, different types of nonlinear models have been proposed one by one, including the single-input single-output (SISO) [7][8][9], multi-input multi-output (MIMO) [10][11][12], single-zone [13][14][15], and multi-zone [16][17][18], etc. No matter which type of models, the researchers generally assumed that all the parameters of the system are accurately known or precisely measurable, including the control coefficients. ...
Nowadays, comfort and energy efficiency have become two important objectives in building energy management. The effective control strategy for the air-conditioner units is considered as a promising way for achieving the above two targets. In this article, an adaptive backstepping controller is designed for the efficient control of the air-handling units (AHUs) in the heating, ventilating and air-conditioning (HVAC) system. Firstly, the original system is equivalently transformed to the appropriate form which is more convenient for the controller design. Then, a controller is designed via backstepping method which is given in a step by step way. It is proven that the given backstepping control law can assure that all the states of the transformed system are globally exponentially stable, and the original system states exponentially converge to the desired values of the indoor temperature and humidity. Furthermore, considering that the temperature gradient ΔTc is normally unmeasurable, that is, the control coefficient of the system may be unknown. Therefore, an adaptive controller is constructed to efficiently deal with the unknown control coefficient problem. In this new control system, the states of the closed-loop system are bounded, and the original system states converge to the desired values. Finally, four different simulation examples and comparisons are given, and all the simulation and comparative results demonstrate the effectiveness of the two proposed controllers.
... Closed-loop zone temperature of the HVAC system under two proposed algorithms (constrained MPC) and saturated sliding mode controller reported in ref.32. ...
In this paper, a reduced multiple-model predictive controller based on gap metric and stability margin is presented to control heating, ventilating, and air conditioning (HVAC) systems. To tackle the strong nonlinearity and large number of degrees of freedom in HVAC system, two approaches, called Reduced Order Model Bank-Multiple Model (ROMB-MM) and Multiple Model-Reduced Order Model (MM-ROM), are introduced. In the first approach, the order reduction is performed prior to multiple models selection and in the second one multiple models selection is implemented before the model order reduction. Furthermore, soft switching is employed to enhance the closed-loop performance as well as to gain optimal energy consumption. In order to evaluate the proposed approaches, a sliding mode controller is also simulated to compare the results in terms of energy and cost savings, transient and steady-state responses, and robustness against external disturbances and model uncertainties.
Practical application: HVAC control systems are in charge of control indoor air temperature with energy optimization so that the thermal comfort is maintained. But how to model HVAC systems in each weather conditions is a significant challenge. A simpler and more accurate model will provide more efficient control of indoor air temperature. This paper suggests model order reduction and multiple model to select the simple linear model(s) in extreme weather conditions. The effectiveness of the proposed method can be implemented on nonlinear HVAC system.
... But the third state variable, which is not part of the outputs, is controlled only by the second input. In [12], [13], to produce the sliding surface and design the first control input, the third state variable tracking error has been applied, which is not available to the controller. So, the practical implementation of the proposed method in this investigation [12], [13] will be difficult. ...
... In [12], [13], to produce the sliding surface and design the first control input, the third state variable tracking error has been applied, which is not available to the controller. So, the practical implementation of the proposed method in this investigation [12], [13] will be difficult. Also, the conventional sliding surface has been used in this discussion. ...
... (Afram & Janabi-Sharifi, 2015;Moradi & Vossoughi, 2016;Müller et al., 2014;Shah et al., 2017;Xiong & Wang, 2020;Yan et al., 2020), por lo que su operación eficiente resulta muy importante. También, ha sido de interés el mejoramiento del confort térmico en los edificios y el diagnóstico y detección de fallos en el sistema de climatización. ...
... Las UMAs son el componente fundamental de los sistemas HVAC (Shah et al., 2017). Estas permiten controlar variables como: la temperatura, la humedad y la velocidad del aire, y pueden ser de volumen de aire constante o variable. ...
This article addresses the problem of identifying differential pressure in an air handling unit (AHU), with special emphasis on the validation of the obtained models. Despite of the fact that many authors focus only on the variables: temperature and humidity, due to the proven energy savings of the application of several advanced control strategies for these variables, other researches show energy savings opportunities from air flow control. All those works have as common denominator the use of models, both in the design and in the evaluation of the proposed control strategies. The work presented here highlights the aspects considered during the application of the system identification methodology in the studied process. Some of the main difficulties involved in applying it in a real case of the industry are exposed and variants are proposed to, despite them, obtain models that satisfy the expectation of its future users. In the validation step, in addition to tests typically done in the practice of system identification, other analysis on the obtained models are recommended, given the stochastic variability of differential pressure variable.
... The new air-conditioning and oxygen supply control system for the running airconditioned train in Qinghai-Tibet Railway has been introduced in (Li and Wang, 2015). Sliding mode control (SMC), which is also an integral part of this research, was already applied on HVAC system in buildings energy consumption area (Shah et al., 2017, Shah et al., 2018. ...
There is a pressing need for energy management with regard to modern rail vehicles, particularly in the case of electric vehicles (EVs), where various forms of energy and loads have to be considered for vehicle running. Air conditioning is an essential factor that ought to be profoundly analysed, as it can account for a significant proportion of electricity demand. This paper considers a lumped-parameter type thermal model of the passenger compartment of a tram in the presence of parametric uncertainties and external disturbances with an optimized heating, ventilating and air conditioning (HVAC) system configuration. To overcome the aforementioned problem, a decoupled sliding mode control (SMC) is designed with the purpose of enabling a lower energy usage and providing the thermal comfort conditions in a tram as an efficient and durable control strategy. The temperature and concentration of carbon dioxide (CO2) are regulated in a desirable way. A proportional, integral and derivative (PID) controller is employed for comparison purposes and for validating the proposed control scheme. According to the findings of this paper, the SMC outperforms the PID controller in terms of fast tracking and steady state for the desired tracking targets.
