Air conditioner temperature and humidity self-adaptive nonlinear control method

Abstract

This invention relates to an adaptive nonlinear control method for air conditioning temperature and humidity, comprising the following steps: Step 1: Establishing a nonlinear dynamic model of the air handling unit's temperature and humidity control system using humidity source intensity, heat load, outdoor humidity ratio, and outdoor temperature as unknowns, and then performing system transformation; Step 2: Constructing a serial-parallel estimation model to obtain estimated values ​​for humidity source intensity and heat load; Step 3: Establishing a humidity control signal to control indoor humidity to reach the desired value, and designing a first humidity source intensity adaptive law to handle the estimated humidity source intensity; Step 4: Constructing a virtual control signal and a heat load adaptive law to handle the heat load, and designing a first Young's inequality and a second humidity source intensity adaptive law to handle the estimated humidity source intensity; Step 5: Designing a second Young's inequality to handle unknown changing humidity ratio and unknown changing temperature; Step 6: Establishing a temperature control signal to control indoor temperature to reach the desired value; This technical solution offers high control accuracy.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning temperature and humidity control technology, specifically relating to an adaptive nonlinear control method for air conditioning temperature and humidity. Background Technology

[0002] A typical HVAC system consists of multiple loops, each with multiple inter-variable variables. Each component exhibits high nonlinearity, and numerous uncertainties such as time-varying characteristics, coupling, time delay, and disturbances make the entire HVAC system a typical complex nonlinear multivariable system.

[0003] Chinese invention patent publication CN116336617A discloses a nonlinear control method for air handling units. It is designed directly for the nonlinear mathematical model of the air handling unit and uses near-disturbance decoupling technology to attenuate the influence of disturbances on the system's output to a given level.

[0004] The nonlinear mathematical model of the air handling unit proposed in the aforementioned patent disclosure has the following shortcomings: It treats humidity source intensity, heat load, outdoor humidity ratio, and outdoor temperature as disturbances, and then divides these disturbances into known constants and bounded functions for near-disturbance decoupling. This approach has limitations. Because when humidity source intensity, heat load, outdoor humidity ratio, and outdoor temperature are treated as unknowns, the system equilibrium point inevitably shifts. This is because near-disturbance decoupling requires ensuring that the fluctuations of the bounded function are relatively small to prevent system equilibrium point shift; if the fluctuations of the bounded function are large, the system equilibrium point will shift, thus failing to fundamentally solve the problem. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an adaptive nonlinear control method for air conditioning temperature and humidity.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] An adaptive nonlinear control method for air conditioning temperature and humidity includes the following steps:

[0008] Step 1: Establish a nonlinear dynamic model of the air handling unit temperature and humidity control system with humidity source intensity, heat load, outdoor humidity ratio, and outdoor temperature as unknowns. The outdoor humidity ratio includes known humidity ratio and unknown variable humidity ratio, and the outdoor temperature includes known temperature and unknown variable temperature. Then, perform a system transformation on the nonlinear dynamic model of the air handling unit temperature and humidity control system.

[0009] Step 2: Construct a serial-parallel estimation model and use it to estimate the unknown humidity source intensity and heat load, obtaining estimated values ​​for humidity source intensity and heat load.

[0010] Step 3: Establish a humidity control signal to control the indoor humidity to reach the desired value, and design the first humidity source intensity adaptive law to handle the humidity source intensity estimate in Step 2;

[0011] Step 4: Construct a virtual control signal and a heat load adaptive law to handle the heat load in Step 1, and design the first Young's inequality and the second humidity source intensity adaptive law to handle the humidity source intensity estimate in Step 2;

[0012] Step 5: Design a second Young's inequality to handle the unknown changing humidity ratio and unknown changing temperature from Step 1;

[0013] Step 6: Establish a temperature control signal to control the indoor temperature to reach the desired value.

[0014] Furthermore, in step 1, the nonlinear dynamic model of the air handling unit's temperature and humidity control system is as follows:

[0015] (1)

[0016] in, Indoor humidity ratio, The supply air humidity ratio, Indoor temperature, For supply air temperature, For the temperature gradient of the heat exchanger, The intensity of the humidity source is unknown. For unknown heat load, For the volume of the interior space, For the volume of the cooling device, This refers to the airflow rate of the blower. To control the cooling water flow rate of the valve, Outdoor temperature Given the temperature, For unknown temperature changes, The outdoor humidity ratio, Given the humidity ratio, For unknown humidity ratios, For the specific heat of air, For water's specific heat, For saturated water enthalpy, It is the enthalpy of vaporization. air density, For the density of water, The percentage of fresh air in the air supply. The proportion of return air in the supply air, and .

[0017] Furthermore, in step 1, the system transforms as follows:

[0018] (2)

[0019] in, This is a humidity control signal. For temperature control signals,

[0020] , , , , ;

[0021] parameter , , The expression is as follows:

[0022] , , ,

[0023] , ,

[0024] , .

[0025] Furthermore, in step 2, the serial-parallel estimation model is:

[0026] (3)

[0027] in, For observation error, and For positive integers, and They are respectively and The estimated value, This is a humidity control signal.

[0028] Furthermore, in step 3, the humidity control signal is:

[0029] (4)

[0030] in, These are positive design parameters. , It is the target humidity.

[0031] Furthermore, in step 3, the adaptive law for the intensity of the first humidity source is:

[0032] (5)

[0033] in, and It is a positive number.

[0034] Furthermore, in step 4, the virtual control signal is ,

[0035] (6)

[0036] in, These are positive design parameters. , The target temperature;

[0037] The adaptive law of heat load is:

[0038] (7)

[0039] in, and It is a positive number.

[0040] Furthermore, in step 4, the first Young's inequality is:

[0041] (8)

[0042] in, It is a positive number. , , yes The estimated value;

[0043] The adaptive law for the intensity of the second humidity source is:

[0044] (9)

[0045] in, and It is a normal number.

[0046] Furthermore, in step 5, the second Yang inequality is:

[0047] (10)

[0048] in, and These are positive design parameters. , , and They are and The upper limit of the value, and it is a positive number.

[0049] Furthermore, in step 6, the temperature control signal is... :

[0050] (11)

[0051] in, These are positive design parameters. .

[0052] The beneficial effects that this invention can achieve are as follows: This invention is the first to apply the serial-parallel estimation model to the existing model, which solves the problem of system equilibrium point shift when humidity source intensity, heat load, outdoor humidity ratio and outdoor temperature are unknown variables, and improves the control accuracy of the system. Attached Figure Description

[0053] Figure 1 This is a flowchart of the control method provided in an embodiment of the present invention.

[0054] Figure 2 This is the indoor humidity change curve of the air handling unit when the target temperature and humidity are constant, provided by an embodiment of the present invention.

[0055] Figure 3 This is the humidity control signal curve of the air handling unit when the target temperature and humidity are constant, provided in the embodiment of the present invention.

[0056] Figure 4 This is the indoor temperature curve of the air handling unit provided in the embodiment of the present invention when the target temperature and humidity are constant.

[0057] Figure 5 This is the temperature control signal curve of the air handling unit when the target temperature and humidity are constant, provided in the embodiment of the present invention.

[0058] Figure 6 This is the air supply temperature curve of the air handling unit when the target temperature and humidity are constant, provided in the embodiment of the present invention.

[0059] Figure 7 This is the indoor humidity change curve of the air handling unit when the target temperature and humidity change, as provided in the embodiments of the present invention.

[0060] Figure 8 This is the indoor humidity error change curve of the air handling unit when the target temperature and humidity change, as provided in the embodiments of the present invention.

[0061] Figure 9 This is the humidity control signal curve of the air handling unit when the target temperature and humidity change, provided in an embodiment of the present invention.

[0062] Figure 10This is the indoor temperature curve of the air handling unit when the target temperature and humidity change, as provided in the embodiments of the present invention.

[0063] Figure 11 This is the indoor temperature error curve of the air handling unit when the target temperature and humidity change, provided by an embodiment of the present invention.

[0064] Figure 12 This is the temperature control signal curve of the air handling unit when the target temperature and humidity change, provided in an embodiment of the present invention.

[0065] Figure 13 This is an adaptive curve of the humidity source intensity of the air handling unit when the target temperature and humidity change, provided by an embodiment of the present invention.

[0066] Figure 14 This is an adaptive curve of the heat load of the air handling unit when the target temperature and humidity change, provided by an embodiment of the present invention.

[0067] Figure 15 This is an air handling unit provided in the embodiments of the present invention that responds to changes in target temperature and humidity. The adaptive curve. Detailed Implementation

[0068] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0069] This embodiment is an improved design based on existing technology, specifically referring to a nonlinear control method for air handling units disclosed in Chinese Invention Patent Publication CN116336617A.

[0070] The structural principle of the air handling unit in this embodiment is similar to that in the appendix of Chinese Invention Patent Publication CN116336617A. Figure 1 Exactly the same.

[0071] An adaptive nonlinear control method for air conditioning temperature and humidity includes the following steps:

[0072] Step 1: Establish a nonlinear dynamic model of the air handling unit temperature and humidity control system with humidity source intensity, heat load, outdoor humidity ratio, and outdoor temperature as unknowns. The outdoor humidity ratio includes known humidity ratio and unknown changing humidity ratio, and the outdoor temperature includes known temperature and unknown changing temperature. Then, perform a system transformation on the nonlinear dynamic model of the air handling unit temperature and humidity control system.

[0073] The nonlinear dynamic model of the temperature and humidity control system of the air handling unit is as follows:

[0074] (1)

[0075] in, Indoor humidity ratio, The supply air humidity ratio, Indoor temperature, For supply air temperature, For the temperature gradient of the heat exchanger, The intensity of the humidity source is unknown. For unknown heat load, For the volume of the interior space, For the volume of the cooling device, This refers to the airflow rate of the blower. To control the cooling water flow rate of the valve, Outdoor temperature Given the temperature, For unknown temperature changes, The outdoor humidity ratio, Given the humidity ratio, For unknown humidity ratios, For the specific heat of air, For water's specific heat, For saturated water enthalpy, It is the enthalpy of vaporization. air density, For the density of water, The percentage of fresh air in the air supply. The proportion of return air in the supply air, and In this embodiment : =1:4.

[0076] The system is transformed into:

[0077] (2)

[0078] in, This is a humidity control signal. For temperature control signals,

[0079] , , , , ;

[0080] parameter , , The expression is as follows:

[0081] , , ,

[0082] , ,

[0083] , .

[0084] Step 2: To avoid system equilibrium point shift, a serial-parallel estimation model is constructed. The unknown humidity source intensity and heat load are estimated using the serial-parallel estimation model to obtain the humidity source intensity estimate and heat load estimate.

[0085] The serial-parallel estimation model is as follows:

[0086] (3)

[0087] in, For observation error, and For positive integers, and They are respectively and The estimated value, This is a humidity control signal.

[0088] Step 3: Establish a humidity control signal to control the indoor humidity to reach the desired value, and design the first humidity source intensity adaptive law to handle the humidity source intensity estimate in Step 2.

[0089] The humidity control signal is:

[0090] (4)

[0091] in, These are positive design parameters. , It is the target humidity.

[0092] Note: Due to The volume of the room determines the size of the room. It is a normal number. The premise of this system model is that the actual humidity of the air supply system is always greater than the actual humidity of the room. It is not zero, which guarantees that The denominator is not zero, so That's reasonable.

[0093] The first humidity source intensity adaptive law is:

[0094] (5)

[0095] in, and It is a positive number.

[0096] Step 4: Construct a virtual control signal and a heat load adaptive law to handle the heat load in Step 1, and design the first Young's inequality and the second humidity source intensity adaptive law to handle the humidity source intensity estimate in Step 2.

[0097] Virtual control signals for:

[0098] (6)

[0099] in, These are positive design parameters. , The target temperature.

[0100] Note: Airflow Because of the exchange of humidity between indoors and outdoors, the humidity inside a room cannot remain constant, so air conditioners always blow humid air into the room. Therefore, It is a positive constant, which guarantees The denominator is not zero, so That's reasonable.

[0101] The adaptive law of heat load is:

[0102] (7)

[0103] in, and It is a positive number.

[0104] The first Young's inequality is:

[0105] (8)

[0106] in, It is a positive number. , , yes The estimated value;

[0107] The adaptive law for the intensity of the second humidity source is:

[0108] (9)

[0109] in, and It is a normal number.

[0110] Step 5: Design a second Young's inequality to handle the unknown changes in humidity ratio and temperature in Step 1.

[0111] The second Yang inequality is:

[0112] (10)

[0113] in, and These are positive design parameters. , , and They are and The upper limit of the value, and it is a positive number.

[0114] Step 6: Establish a temperature control signal to control the indoor temperature to reach the desired value.

[0115] Temperature control signal is :

[0116] (11)

[0117] in, These are positive design parameters. .

[0118] The following is proof of system stability:

[0119] Constructing the first Lyapunov function candidate Prove the system's stability.

[0120] (12)

[0121] For the first Lyapunov function candidate Differentiating gives

[0122] (13)

[0123] Substituting equation (4) into equation (13) yields

[0124] (14)

[0125] Substituting equation (5) into equation (14) yields

[0126] (15)

[0127] Constructing a second candidate Lyapunov function for:

[0128] (16)

[0129] in, ,right Differentiation yields

[0130] (17)

[0131] Substituting equation (6) into equation (17) yields

[0132] (18)

[0133] Substituting equations (7) and (8) into equation (18) yields

[0134] (19)

[0135] Substituting equation (9) into equation (19) yields

[0136] (20)

[0137] Constructing a third candidate Lyapunov function for:

[0138]

[0139] right Differentiation yields

[0140] (twenty one)

[0141] Substituting equation (10) into equation (21) yields

[0142] (twenty two)

[0143] Substituting equation (11) into equation (22) yields

[0144] (twenty three)

[0145] The third Young's inequality is:

[0146] (twenty four)

[0147] Substituting equation (24) into equation (23) yields

[0148] (25)

[0149] in, ,

[0150]

[0151] Lemma 1: For an air conditioning system (1), under controllers (4), (6), (11), serial-parallel estimation model (3) and adaptive laws (5), (7), (9), the control errors of temperature and humidity are bounded, and the unknown parameters can converge to the actual values.

[0152] By using equation (25), combined with Lyapunov stability theory and Lemma 1, the control method proposed in this invention can guarantee the stability of the closed-loop system.

[0153] The following is a simulation verification:

[0154] To verify the effectiveness of the proposed control method, it was compared with feedback linearization and PID control through Matlab simulation. The following two cases are discussed:

[0155] Option 1 is: This embodiment

[0156] Option 2 is: Feedback linearization technique

[0157] Option 3 is: PID technology

[0158] (1) The target temperature and humidity are constant.

[0159] The initial conditions are chosen as follows: , and The design parameters for the control signal are: , and The target humidity and temperature are set as follows: and .

[0160] Simulation examples Figures 2-6 As shown.

[0161] The output humidity and the ideal humidity curve are as follows: Figure 2 As shown in the figure, it can be seen that all three schemes have obvious overshoot, but compared with the first scheme (this embodiment), the controller proposed has the fastest convergence speed.

[0162] Humidity control signal The curve is as follows Figure 3 As shown, all three schemes vary within a bounded region. By comparison, it can be seen that the humidity control performance of Scheme 1 (this embodiment) is better than that of Scheme 2 (feedback linearization technology) and Scheme 3 (PID technology).

[0163] Figure 4 The indoor temperature curve is shown. In Scheme 1 (this embodiment), the steady-state value is reached in 100 seconds, which is the fastest convergence speed.

[0164] Figure 5 The temperature control signal curves were plotted, showing the control signals for Scheme 1 (this embodiment) and Scheme 3 (PID technology). Relatively stable, while Scheme 2 (feedback linearization technique) controls... It eventually converges to a small region.

[0165] air supply temperature such as Figure 6 As shown in the figure, the temperature first rises to It then dropped further, and finally stabilized at... .

[0166] (2) Changes in target temperature and humidity

[0167] The initial conditions are chosen as follows: (abbreviated as) ), and The input parameters for controllers (7), (12), and (23) are: , and Humidity source intensity, heat load, and The initial value is set to: , and .

[0168] Simulation results are as follows Figures 7-15 As shown.

[0169] Humidity tracking performance such as Figure 7 As shown, regardless of whether the desired humidity is constant or variable, Scheme 1 (this embodiment) has the best tracking performance.

[0170] Figure 8 Humidity error curves for three different schemes are given, and the root mean squares of the three humidity error curves are as follows: , and .

[0171] Figure 9 This is a humidity control signal; the saturation value of the humidity control signal can be seen. The humidity control signals of all three schemes are bounded and greater than zero, which is consistent with reality; the root mean square of the humidity control signals of the three schemes are respectively , and .

[0172] Figure 10 The actual and expected temperature curves were plotted, and it is easy to see that Scheme 1 (this embodiment) has the smallest temperature error.

[0173] Temperature error such as Figure 11 As shown, the root mean square of the temperature error for the three schemes are respectively , and .

[0174] Figure 12 Indicates temperature control signal Its saturation value is set to Within a reasonable range, the root mean square (RMS) of the temperature control signals for the three schemes are respectively , and .

[0175] Figure 13 The intensity of the humidity source was drawn. The two adaptive curves have average steady-state values ​​of [the curves]. and It can converge to near the expected value.

[0176] Figure 14 For heat load The adaptive curve has a steady-state average value of It can also converge to the expected value.

[0177] Figure 15 yes The adaptive trajectory has a steady-state average value of .

[0178] from Figures 8-9 , Figures 11-13 As can be seen from the calculated root mean square, Scheme 1 (this embodiment) outperforms Scheme 2 (feedback linearization technology) and Scheme 3 (PID technology).

[0179] from Figure 13 , Figure 14 Based on the calculated steady-state values, Scheme 1 (in this embodiment) not only ensures that the unknown humidity source intensity and heat load converge to the actual values ​​after adopting the serial-parallel estimation model, but also solves the problem of system equilibrium point shift.

Claims

1. An adaptive nonlinear control method for air conditioning temperature and humidity, characterized by: Includes the following steps: Step 1: Establish a nonlinear dynamic model of the air handling unit temperature and humidity control system with humidity source intensity, heat load, outdoor humidity ratio, and outdoor temperature as unknowns. The outdoor humidity ratio includes known humidity ratio and unknown variable humidity ratio, and the outdoor temperature includes known temperature and unknown variable temperature. Then, perform a system transformation on the nonlinear dynamic model of the air handling unit temperature and humidity control system. Step 2: Construct a serial-parallel estimation model and use it to estimate the unknown humidity source intensity and heat load, obtaining estimated values ​​for humidity source intensity and heat load. Step 3: Establish a humidity control signal to control the indoor humidity to reach the desired value, and design the first humidity source intensity adaptive law to handle the humidity source intensity estimate in Step 2; Step 4: Construct a virtual control signal and a heat load adaptive law to handle the heat load in Step 1, and design the first Young's inequality and the second humidity source intensity adaptive law to handle the humidity source intensity estimate in Step 2; Step 5: Design a second Young's inequality to handle the unknown changing humidity ratio and unknown changing temperature from Step 1; Step 6: Establish a temperature control signal to control the indoor temperature to reach the desired value; In step 1, the nonlinear dynamic model of the air handling unit's temperature and humidity control system is as follows: （1）； in, Indoor humidity ratio, The supply air humidity ratio, Indoor temperature, For supply air temperature, For the temperature gradient of the heat exchanger, The intensity of the humidity source is unknown. For unknown heat load, For the volume of the interior space, For the volume of the cooling device, This refers to the airflow rate of the blower. To control the cooling water flow rate of the valve, Outdoor temperature Given the temperature, For unknown temperature changes, The outdoor humidity ratio, Given the humidity ratio, For unknown humidity ratios, For the specific heat of air, For water's specific heat, For saturated water enthalpy, It is the enthalpy of vaporization. air density, For the density of water, The percentage of fresh air in the air supply. The proportion of return air in the supply air, and ; In step 1, the system transforms as follows: （2）； in, This is a humidity control signal. For temperature control signals, ， ， ， ， ； parameter , , The expression is as follows: , , , , , , ； In step 2, the serial-parallel estimation model is as follows: （3）； in, For observation error, and For positive integers, and They are respectively and The estimated value, This is a humidity control signal; In step 3, the humidity control signal is: （4）； in, These are positive design parameters. , The target humidity; In step 3, the adaptive law for the intensity of the first humidity source is: （5）； in, and It is a positive number; In step 4, the virtual control signal is , （6）； in, These are positive design parameters. , The target temperature; The adaptive law of heat load is: （7）； in, and It is a positive number; In step 4, the first Young's inequality is: （8）； in, It is a positive number. , , yes The estimated value; The adaptive law for the intensity of the second humidity source is: （9）； in, and It is a positive number; In step 5, the second Yang inequality is: （10）； in, and These are positive design parameters. , , and They are and The upper limit of the value, and it is a positive number; In step 6, the temperature control signal is : （11）； in, These are positive design parameters. .

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