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This paper proposes a sliding mode control (SMC) strategy to solve the problem of independent control of temperature and humidity in air conditioners; the complexity increases due to the need to make full use of the wind side for cooling. First, dynamics of indoor temperature and humidity are determined based on mathematical models. Second, in order to reduce the chattering effect of SMC and coordinate indoor conditions to satisfy people’s needs, the independent control of temperature and humidity is realized via a novel fuzzy sliding mode control strategy based on reaching laws. Finally, simulation results are provided to verify the effectiveness of the proposed method.

In traditional central air-conditioning control systems, temperature is typically the controlled parameter and multiloop PID control method is commonly adopted. However, achieving desired control performance is difficult with the aid of traditional PID controllers due to a variety of reasons, for instance, nonlinearity and time-varying effects in temperature variation and complexity in establishing mathematical models [

Compared with the conventional air-conditioning system, the temperature- and humidity-independent control system uses high-temperature cold source, solution humidity control, and other technologies to ensure energy saving. Lazzarin and Castellotti studied a self-regenerating solution dehumidification system driven by a heat pump, which was applied to supermarket to verify its energy efficiency by simulating year-round outdoor working conditions [

During air-conditioning control, the fuzzy control strategy is more efficient than the PID control because the former can adjust the room temperature under partial load operation and flow rate of cooling water in cooling coil shows satisfactory oscillation response; however, given that the fuzzy approach lacks systemicity, the disadvantages of using fuzzy control method alone in air-conditioning systems are instability and poor robustness in the fuzzy process [

Sliding mode control (SMC) was proposed by Emelyanov et al. in the 1950s. Itkin et al. subsequently summarized and developed SMC theory [

Different from the traditional control method, the robust and fast response characteristics of the fuzzy sliding mode control adopted in this paper are more suitable for the air-conditioning system with large lags and numerous interference. Meanwhile, the contribution of this study is to propose fuzzy rules in the design of sliding mode controller for independent control of indoor temperature and humidity to solve the problem of increased complexity due to the configuration of the exhaust fan. Compared with the conventional PID control and fuzzy PID control methods, the proposed fuzzy SMC strategy achieves faster adjustment response, smaller overshoot, higher accuracy, and better control effect. At the same time, it ensures that the indoor carbon dioxide concentration is at a normal level. However, this air-conditioning system is mainly suitable for the case where the room model data is known. The design method of the stochastic delay feedback control and the stability controller for the discrete-time half-Markov jump linear system with bounded residence time used in [

The independent control of temperature and humidity in the air-conditioning system adjusts the volume of fresh air supply of the dehumidification system and the volume of return air supply of the temperature control system to meet people’s requirements for indoor environment comfort [

Air-conditioning system diagram.

This study considers an office building environment with the following assumptions [

Given that the independent control of temperature and humidity in the air-conditioning system uses fresh air in the dehumidification system to remove indoor residual humidity, CO_{2}, and odors such that to ensure indoor air quality, the dynamic response modeling of room temperature must only consider the room’s sensible heat [

The heat energy balance in an air-conditioning system is expressed as follows [

The room sensible heat change can be described as follows [

The relationship between the sensible heat provided by the air-conditioning system to the room and the air output can be expressed as follows [

For rooms that use temperature and humidity as variables to control the air-conditioning system independently, the main factors that affect the indoor temperature response are listed as follows: sensible heat flowing out of the room and heat dissipation of the room [

Heat dissipation of the room

The sensible heat flowing out of the room is primarily the heat taken away by return air. Given that the office building is assumed to be a closed-loop system, the return air volume can be calculated according to the supply air volume of the temperature control system as follows [

Substituting (

The dehumidification system of the air conditioner with independent control of temperature and humidity adopts the full fresh air system. The humidity control system bears all the latent heat load of the building by sending dry fresh air with moisture content lower than the interior designed state. Under the assumptions that the office building is a closed-loop system and the pressure in the building remains unchanged, mass conservation of air humidity can be calculated as follows [

Hence, determining the total indoor wet load

The wet load of personnel in the office building based on the adults is calculated as follows [

Conventional air-conditioning systems only considered the wet load caused by fresh air penetration in winter but ignored the insignificant wet load generated by fresh air penetration in summer. By comparison, dry air is responsible for dehumidification in air-conditioning systems with independent temperature and humidity control although its dehumidification capacity is limited. Therefore, large humidity load caused by outdoor fresh air penetration must be considered. The wet load due to fresh air penetration can be calculated as follows [

Substituting (

The latent heat control system in the air conditioner with independent control of temperature and humidity uses full fresh air input to ensure that the predicted air concentration in the office space will remain fresh. The relationship between the indoor concentration and the air volume of the air conditioner in the absence of people is expressed as follows:

According to the actual situation of the office building, the amount of indoor

Therefore, the indoor

Define that

The state equation of the temperature response in the room can be expressed as follows:

Define

The integral sliding surface can reduce the steady-state error of the system, weaken the chattering, and enhance the stability of the temperature controller. Therefore, the integral sliding mode surface function is defined as follows:

If

When the system state trajectories reached onto the sliding surface,

Given that

Therefore, we can obtain the following formula based on (

Thus, the following sliding mode controller based on the power reaching law is obtained:

Define the following Lyapunov function,

Substituting that (

Equation (

Define

Selecting sliding surface function which is

The selected exponential approach law is

If

Furthermore we can obtain the following formula based on (

Hence,

Select the following Lyapunov function:

Then,

Because

In this paper, approach laws

This study designs a two-dimensional fuzzy controller to meet the requirements for a stable air-conditioning system under the conditions of temperature parameter changes and interference effects during the operation of the air conditioner. The input signal of fuzzy law is the temperature error

According to the principle of SMC, the parameters of the reaching law

Fuzzy rule with parameter

NB | NM | NS | ZO | PS | PM | PB | |
---|---|---|---|---|---|---|---|

NB | PB | PB | PM | PS | PS | ZO | ZO |

NM | PB | PB | PM | PS | PS | ZO | NS |

NS | PM | PM | PM | ZO | ZO | NS | NS |

ZO | PM | PM | PS | NS | NS | NM | NM |

PS | PS | PS | ZO | NS | NS | NM | NM |

PM | PS | ZO | NS | NM | NM | NM | NB |

PB | ZO | ZO | NM | NM | NM | NB | NB |

Fuzzy rule with parameter

NB | NM | NS | ZO | PS | PM | PB | |
---|---|---|---|---|---|---|---|

NB | NB | NB | NM | NM | NS | ZO | ZO |

NM | NB | NB | NM | NS | NS | ZO | ZO |

NS | NB | NM | NS | NS | ZO | PS | PS |

ZO | NM | NM | NS | ZO | PS | PM | PM |

PS | NM | NS | ZO | PS | PS | PM | PB |

PM | ZO | ZO | PS | PS | PM | PB | PB |

PB | ZO | ZO | PS | PM | PM | PB | PB |

Fuzzy rule with parameter

NB | NM | NS | ZO | PS | PM | PB | |
---|---|---|---|---|---|---|---|

NB | NB | NB | NM | NM | NS | ZO | ZO |

NM | NB | NB | NM | NS | NS | ZO | PS |

NS | NB | NM | NS | NS | ZO | PS | PS |

ZO | NM | NS | NS | ZO | PS | PM | PM |

PS | NM | NS | ZO | PS | PM | PM | PB |

PM | NS | ZO | PS | PS | PM | PB | PB |

PB | ZO | ZO | PS | PM | PM | PB | PB |

This paper uses the center of gravity method to complete deblurring. The output result after deblurring is presented as follows:

This study uses MATLAB/Simulink to establish the simulation model of the central air-conditioning system. Fuzzy sliding mode control and traditional PID control methods are used to compare and verify the correctness and feasibility of the proposed strategy.

Assuming that the room has a length, width, and height of 16, 10, and 4 m, respectively, the indoor air density, indoor air specific heat capacity, coefficient of thermal conductivity of wall materials, and thickness of the wall material are

Heat from solar radiation.

The indoor temperature is set to 25°C, and the input of outdoor temperature and humidity changes according to the data collected in Suzhou in July, as shown in Figures

Outdoor temperature change.

Outdoor humidity change.

Assuming that the initial indoor ambient temperature is 26°C and the moisture content is 13 g/kg, the enthalpy chart demonstrates that the air moisture content is 12 g/kg when the temperature is set to 25°C and the optimum indoor relative humidity is 60%. Therefore, the ideal air moisture content is 12 g/kg.

The comparison of indoor temperature and humidity responses under the sliding mode and the PID control strategies are illustrated in Figures

Indoor temperature response.

Indoor humidity response.

The air volume input of indoor temperature and humidity control under the sliding mode and PID control strategies is compared and illustrated in Figures

Temperature control input air volume.

Humidity control input air volume.

Figures

So, you can get the following result.

The temperature error of the fuzzy sliding mode control strategy is only 30% and 24% of the other two methods.

The humidity error of the fuzzy sliding mode control strategy is 50% and 35% of the other two methods.

Compared with the traditional PID control technique, the higher accuracy of the SMC strategy ensures that the indoor environment is maintained at the desired level and meets the comfort requirements of the human body.

Under the above circumstances, assuming that 10 people are inside the office, the fresh air input can be used to estimate the

Figure

This paper proposes a fuzzy sliding mode approach for the independent control of temperature and humidity in air-conditioning systems. By analyzing the indoor environmental effect, mathematical models for room temperature, humidity, and

The data of outdoor temperature and humidity changes in this paper are measured by the author. The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that they have no conflicts of interest.

This work was supported in part by the NSFC under Grant nos. 61803279, 11871366, and 61672371, the Qing Lan Project of Jiangsu, the China Postdoctoral Science Foundation under Grant no. 2020M671596, the Open Project Funding from Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, under Grant no. IBBE2018KX02ZD, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant no. 18KJB460026, the Suzhou Science and Technology Foundation under Grant no. SYG201813, and the Jiangsu Province Graduate Practice Innovation Program under Grant no. SJCX19_0844.