Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems

Ганчев, Иван; Taneva, Albena; Kutryanski, Krum; Petrov, Michail

doi:10.1016/j.ifacol.2019.12.539

Cited by 21 publications

(12 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, the set point could change in any time of day or night and HVAC control system has to follow it such that energy cost optimization could be guaranteed. The present solutions more or less solve this problem, 17 but the designed controllers are complex. Unlike the classical studies, researches on high order nonlinear HVAC models are not well developed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reduced multiple model predictive control of an heating, ventilating, and air conditioning system using gap metric and stability margin

Rikhtehgar

Haeri

2022

Building Services Engineering Research and Technology

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In the second category, tracking is the main purpose to design the controller. 17,18 In this case, use of both equipment will be necessary to cool or heat the indoor temperature in the presence of internal and external disturbances. This strategy would have a higher priority due to rapid changes in disturbance in some circumstances.…”

Section: Introductionmentioning

confidence: 99%

Reduced multiple model predictive control of an heating, ventilating, and air conditioning system using gap metric and stability margin

Rikhtehgar

Haeri

2022

Building Services Engineering Research and Technology

View full text Add to dashboard Cite

show abstract

“…A fuzzy controller was proposed by Shah et al [12] with distinct type and number of membership functions and investigated which shape can give desired performance with energy consumption this controller is compared with variety controllers including self-tuning adaptive fuzzy controller, LQR and nonlinear controllers. A neural fuzzy structure of a parameter self-tuning decoupled fuzzy neural PID controller was proposed by Ganchev et al [13]. A nonlinear model predictive control approach according to an optimization function is used on iterative optimization with a finite horizon and it can generate a control signal regardless of a past error [14].…”

Section: Introductionmentioning

confidence: 99%

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

Hassan¹,

Al-Samarraie²

2022

JESA

View full text Add to dashboard Cite

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.

show abstract

“…Therefore, researchers have carried out a large number of studies on control methods from multiple aspects, such as HVAC equipment control, online monitoring control, model predictive control (MPC) and building energy management system control (MBC), etc. Ganchev et al (2019) saved energy consumption of HVAC systems by improving the efficiency of the cooling equipment and optimizing the operating rate of the equipment. Monitoring of indoor ventilation parameters (CO 2 ) can be conducted in buildings for the diagnostic control of HVAC systems to save energy (Li et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

2020

View full text Add to dashboard Cite

Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO2, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI(T) and CRI(H), the distributions of indoor CO2 concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI(T) and LLHM-based CRI(H), respectively. Finally, according to the self-defined indices (i.e., EV, ET, EH), the optimal balancing between IEQ (indicated by CO2 concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.

show abstract

Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems

Cited by 21 publications

References 7 publications

Reduced multiple model predictive control of an heating, ventilating, and air conditioning system using gap metric and stability margin

Reduced multiple model predictive control of an heating, ventilating, and air conditioning system using gap metric and stability margin

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

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

Contact Info

Product

Resources

About