Superheat degree control method and system for suppressing superheat degree oscillation

Abstract

The invention discloses a superheat degree control method and system for suppressing superheat degree oscillation. The method comprises the steps that the process identification module identifies dynamic characteristics of the superheat degree control system through a transfer function; the prediction control module constructs a state space model according to the dynamic characteristics, performs state prediction based on a state estimation value and a disturbance estimation value, outputs a state prediction vector and calculates a first objective function; the parameter identification self-tuning module carries out self-tuning on the PID control parameters according to the dynamic characteristics and a second objective function; the PID control module outputs an electronic expansion valve driving signal according to the deviation between a superheat degree predicted value in the state prediction vector and a superheat degree preset target value and the set PID control parameter; and the electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve driving signal so as to adjust the flow of the refrigerant. According to the technical scheme provided by the embodiment of the invention, superheat degree oscillation caused by low superheat degree operation of the refrigerating system is remarkably inhibited, and the system stability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigeration system control, and particularly to a superheat control method and system for suppressing superheat oscillation. Background Art

[0002] When the refrigeration system operates at a low superheat, the random alternation of the refrigerant flow state at the evaporator outlet will cause a drastic change in the heat transfer coefficient, resulting in superheat oscillation. This oscillation will cause superheat fluctuation and data error, which is not conducive to the rapid response and tracking control of the superheat under the dynamic working conditions of the refrigeration system. In severe cases, it may even lead to liquid slugging of the compressor.

[0003] The prior art usually adjusts the opening of the electronic expansion valve according to the deviation value between the measured superheat and the target superheat or the change rate of the deviation value by proportional-integral-derivative (PID) control. The prior art has the problem that it is difficult to adapt to the oscillation characteristics of dynamic changes. Especially under variable frequency working conditions, the oscillation amplitude increases with the increase of the frequency, resulting in poor system stability. Summary of the Invention

[0004] The present invention provides a superheat control method and system for suppressing superheat oscillation, so as to suppress the superheat oscillation caused by the random alternation of the refrigerant flow state at the evaporator outlet during the operation of the variable frequency refrigeration system at a low superheat, and can effectively improve the system stability.

[0005] According to an aspect of the present invention, there is provided a superheat control method for suppressing superheat oscillation, which is executed by a superheat control system for suppressing superheat oscillation. The superheat control system includes: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification self-tuning module, and a predictive control module. The superheat control method includes:

[0006] The evaporator introduces process noise, and the measurement module introduces observation noise. The measurement module measures the parameters at the evaporator outlet in real time. The parameters at the evaporator outlet include the superheat at the evaporator outlet, the refrigerant mass flow rate flowing out of the evaporator, and the flow state identifier.

[0007] After the Kalman filter processes the parameters at the evaporator outlet, it outputs a state estimate value and a disturbance estimate value.

[0008] The process identification module identifies the dynamic characteristics of the superheat control system through a transfer function.

[0009] The predictive control module constructs a state space model according to the dynamic characteristics, and performs state prediction based on the state estimate value and the disturbance estimate value, outputs a state prediction vector, and calculates a first objective function.

[0010] The parameter identification and self-tuning module performs self-tuning on the PID control parameters according to the dynamic characteristics and the second objective function;

[0011] The PID control module outputs an electronic expansion valve drive signal according to the deviation between the superheat prediction value in the state prediction vector and the preset target value of superheat, and the tuned PID control parameters;

[0012] The electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve drive signal to adjust the refrigerant flow rate.

[0013] Optionally, the transfer function of the process identification module satisfies the following relationship:

[0014]

[0015] Among them, G(s) is the transfer function of the superheat control system, K is the gain of the superheat control system, τ is the pure lag time, T1 and T2 are the two time constants of the second-order link respectively, and s is the complex variable.

[0016] Optionally, the first objective function satisfies the following relationship:

[0017]

[0018] Among them, J Kk is the first objective function, is the preset target value of superheat, ΔS is the flow state switching frequency, and λ is the weight coefficient.

[0019] Optionally, the predictive control module outputs a state prediction vector according to the following formula:

[0020] x k|k-1 = F k-1 x k-1|k-1 + B k-1 u k-1 ;

[0021] x k|k-1 is the state prediction vector at time k, F k-1 is the state transition matrix, B k-1 is the control input matrix, u k-1 is the control input vector at time k-1, and x k-1|k-1 is the state estimation vector at time k-1.

[0022] Optionally, the second objective function satisfies the following relationship:

[0023]

[0024] Among them, J ITAE is the absolute error objective function of the integration time, that is, the second objective function; |e(t)| is the absolute value of the error; t is the time variable.

[0025] Optionally, the state transition matrix and the control input matrix are established through the following model:

[0026]

[0027] In the formula: M is the mass of the refrigerant flowing out of the evaporator, is the mass flow rate of the refrigerant flowing out of the evaporator, T evap,ou is the refrigerant temperature at the outlet of the evaporator, h at is the convective heat transfer coefficient between the evaporator wall and the outside world, A evap is the area of the evaporator wall, c evap,out is the specific heat of the refrigerant at the outlet of the evaporator, T at is the outside ambient temperature, is the change in the mass flow rate of the refrigerant in the evaporator, h evap,in is the specific enthalpy of the refrigerant at the inlet of the evaporator, T sat is the refrigerant saturation temperature corresponding to the refrigerant pressure at the outlet of the evaporator, C d is the flow coefficient of the electronic expansion valve, A eev is the flow area of the electronic expansion valve, ρ is the density of the refrigerant at the inlet of the electronic expansion valve, ΔP is the pressure difference across the electronic expansion valve, h eev is the valve needle opening of the electronic expansion valve, β is the valve needle cone angle of the electronic expansion valve, d eev is the valve needle aperture of the electronic expansion valve.

[0028] Optionally, the parameter identification and self-tuning module self-tunes the PID control parameters according to the dynamic characteristics and the second objective function, including:

[0029] The parameter identification and self-tuning module calculates the objective function value under the initial PID control parameters according to the second objective function, and uses the simplex method to optimize the PID control parameters to make the second objective function the minimum value.

[0030] Optionally, the observation noise covariance is dynamically adjusted according to the flow regime identifier.

[0031] Optionally, the observation noise covariance is dynamically adjusted according to the flow regime identifier and should satisfy the following relationship:

[0032]

[0033] Among them, is the observation noise covariance, FlowState k is the flow regime identifier at the k-th moment, Rstable The value of the observation noise covariance when the vapor flow is in the steady state, R oscillate1 The value of the observation covariance when the flow regime is identified as the first oscillating state, mist flow, R oscillate2 The value of the observation covariance when the flow regime is identified as the second oscillating state, annular flow, R oscillate2 ≥R oscillate1 >R stable 。

[0034] According to another aspect of the present invention, there is provided a superheat control system for suppressing superheat oscillation, which is used to execute the superheat control method for suppressing superheat oscillation described in any embodiment of the present invention. The superheat control system includes: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification self-tuning module, and a predictive control module connected in sequence;

[0035] The evaporator introduces process noise, and the measurement module introduces observation noise. The measurement module is used to measure the parameters at the outlet of the evaporator in real time; the parameters at the outlet of the evaporator include the superheat at the outlet of the evaporator, the refrigerant mass flow rate flowing out of the evaporator, and the flow regime identification;

[0036] The Kalman filter is used to process the parameters at the outlet of the evaporator and then output a state estimate value and a disturbance estimate value;

[0037] The process identification module is used to identify the dynamic characteristics of the superheat control system through a transfer function;

[0038] The predictive control module is used to construct a state space model according to the dynamic characteristics, and perform state prediction based on the state estimate value and the disturbance estimate value, and output a state prediction vector and calculate a first objective function;

[0039] The parameter identification self-tuning module is used to self-tune the PID control parameters according to the dynamic characteristics and a second objective function;

[0040] The PID control module is used to output an electronic expansion valve drive signal according to the deviation between the predicted superheat value in the state prediction vector and the preset target value of superheat, and the tuned PID control parameters;

[0041] The electronic expansion valve driver is used to drive the electronic expansion valve to act according to the electronic expansion valve drive signal to adjust the refrigerant flow rate.

[0042] An embodiment of the present invention provides a superheat control method and system for suppressing superheat oscillation. The method includes: introducing process noise into the evaporator, introducing observation noise into the measurement module, and the measurement module measuring the parameters at the outlet of the evaporator in real time; after processing the parameters at the outlet of the evaporator by the Kalman filter, outputting a state estimate value and a disturbance estimate value; the process identification module identifying the dynamic characteristics of the superheat control system through a transfer function; the predictive control module constructing a state space model according to the dynamic characteristics, and performing state prediction based on the state estimate value and the disturbance estimate value, outputting a state prediction vector and calculating a first objective function; the parameter identification self-tuning module self-tuning the PID control parameters according to the dynamic characteristics and a second objective function; the PID control module outputting an electronic expansion valve drive signal according to the deviation between the predicted superheat value in the state prediction vector and the preset target value of the superheat, and the tuned PID control parameters; the electronic expansion valve driver driving the electronic expansion valve to act according to the electronic expansion valve drive signal to adjust the refrigerant flow rate. The technical solution provided by the embodiment of the present invention effectively reduces the control deviation by using the superheat predicted by the improved Kalman filter through flow state identification and noise adaption. By setting a process identification module and using a transfer function to identify the dynamic characteristics of the superheat control system; setting a predictive control module to construct a state space model according to the dynamic characteristics, and performing state prediction based on the state estimate value and the disturbance estimate value, outputting a state prediction vector and calculating a first objective function; setting a parameter identification self-tuning module to self-tune the PID control parameters according to the dynamic characteristics and a second objective function, so as to be applicable to the fast response and tracking control of the superheat under dynamic conditions such as variable frequency compressors and variable loads, significantly suppressing the superheat oscillation caused by the refrigeration system operating at a low superheat, and improving the system stability.

[0043] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is a schematic structural diagram of a superheat control system for suppressing superheat oscillation provided by an embodiment of the present invention;

[0046] Figure 2 It is a flowchart of a superheat control method for suppressing superheat oscillation provided by an embodiment of the present invention;

[0047] Figure 3 Schematic structural diagram of a refrigeration system adopting a superheat control method for suppressing superheat oscillation provided by an embodiment of the present invention;

[0048] Figure 4 Curve comparison diagram of the optimal superheat estimation value of the traditional Kalman filter and the optimal superheat estimation value of the improved Kalman filter provided by an embodiment of the present invention;

[0049] Figure 5 Adaptive observation noise covariance curve diagram controlled by an improved Kalman filter provided by an embodiment of the present invention;

[0050] Figure 6 Superheat oscillation curve diagram under traditional control provided by an embodiment of the present invention;

[0051] Figure 7 Superheat oscillation curve diagram controlled by an improved Kalman filter provided by an embodiment of the present invention. Detailed implementation manners

[0052] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data may be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0054] Figure 1 Schematic structural diagram of a superheat control system for suppressing superheat oscillation provided by an embodiment of the present invention, Figure 2The flowchart of a superheat control method for suppressing superheat oscillation provided by an embodiment of the present invention. This embodiment is applicable to the situation of superheat oscillation caused by the random alternation of the refrigerant flow state at the outlet of the evaporator during the low superheat operation of a variable-frequency refrigeration system. This method can be executed by a superheat control system for suppressing superheat oscillation. Refer to Figure 1 As shown in Figure 2 This method includes:

[0055] S110. The evaporator introduces process noise, and the measurement module introduces observation noise. The measurement module measures the parameters at the outlet of the evaporator in real time.

[0056] Among them, the parameters at the outlet of the evaporator include the superheat at the outlet of the evaporator, the refrigerant mass flow rate flowing out of the evaporator, and the flow state identifier.

[0057] Specifically, Figure 3 As shown in the structural schematic diagram of a refrigeration system adopting a superheat control method for suppressing superheat oscillation provided by an embodiment of the present invention. Refer to Figure 3 This refrigeration system includes but is not limited to the following components: an evaporator, an electronic expansion valve, a flow state identifier, a refrigerant mass flowmeter, and sensors. Temperature sensors and pressure sensors are arranged at the inlet of the electronic expansion valve, the inlet of the evaporator, and the outlet of the evaporator, respectively, for collecting the temperature and pressure at the inlet and outlet of the electronic expansion valve, as well as the temperature and pressure at the inlet and outlet of the evaporator. At the same time, a flow state identifier is arranged at the outlet of the evaporator, and a refrigerant mass flowmeter is arranged at the inlet of the electronic expansion valve. The flow state identifier can identify the refrigerant flow state at the outlet of the evaporator through image processing or thermodynamic characteristics. The refrigerant mass flowmeter is used to detect the refrigerant mass flow rate. It can be seen that the superheat at the outlet of the evaporator can be calculated by subtracting the saturation temperature from the temperature at the outlet of the evaporator. The temperature at the outlet of the evaporator can be collected by the temperature sensor at the outlet of the evaporator. The saturation temperature can be obtained by looking up the corresponding saturation temperature value based on the pressure collected by the pressure sensor at the outlet of the evaporator. The refrigerant mass flow rate can be detected by the mass flowmeter. The flow state identifier can identify the flow state identifier of the refrigerant at the outlet of the evaporator based on image processing or thermodynamic characteristics. The refrigerant flow states include vapor flow, mist flow, and annular flow.

[0058] S120. After the Kalman filter processes the parameters at the outlet of the evaporator, it outputs the state estimation value and the disturbance estimation value.

[0059] Continue to refer to Figure 1, after the measurement module measures the parameters at the evaporator outlet in real time, it transmits the parameters at the evaporator outlet to the Kalman filter. After the Kalman filter performs denoising and filtering processing on the parameters at the evaporator outlet, it outputs the state estimation value and the disturbance estimation value.

[0060] S130. The process identification module identifies the dynamic characteristics of the superheat control system through the transfer function.

[0061] Specifically, the inverse M sequence is a sequence with good pseudo-random characteristics. The inverse M sequence is used to excite the electronic expansion valve to estimate the parameters of the superheat control model at the evaporator outlet, mainly including steps such as designing the inverse M sequence, establishing the superheat control model, conducting experimental excitation, data acquisition and processing, and parameter estimation. According to specific application requirements and system characteristics, determine the generation rules of the inverse M sequence, including the length of the sequence, the value range, the change law, etc. For example, consider whether the frequency characteristics of the sequence can effectively excite the electronic expansion valve to produce appropriate opening changes. Based on the physical principles of the refrigeration system and the working characteristics of the evaporator, construct a mathematical model to describe the relationship between the superheat at the evaporator outlet and factors such as the opening of the electronic expansion valve, the refrigerant mass flow rate, and the refrigerant flow state. Use the designed inverse M sequence as the input signal and input it into the superheat control system, so that the electronic expansion valve changes its opening according to the law of the inverse M sequence, thereby exerting an excitation effect on the superheat at the evaporator outlet and causing the superheat to have corresponding dynamic changes. Use sensors to collect relevant data such as the superheat at the evaporator outlet, the opening of the electronic expansion valve, and the refrigerant mass flow rate under the excitation of the inverse M sequence. The collected data may contain interference information such as noise, and it needs to be preprocessed through data processing methods such as denoising, filtering, and smoothing to improve the data quality and provide a reliable data basis for subsequent parameter estimation. Use specific parameter estimation methods, such as the least squares method, the maximum likelihood estimation method, etc., to calculate the estimated values of each parameter in the superheat control model according to the collected and processed data. By continuously adjusting the parameters, make the output of the model as close as possible to the actually collected data, so as to determine the model parameters that can best reflect the true characteristics of the system.

[0062] Adopt a second-order link mathematical model with pure lag to approximate the superheat control process; optionally, the transfer function of the process identification module satisfies the following relationship:

[0063]

[0064] Among them, G(s) is the transfer function of the superheat control system, representing the input-output relationship of the system in the complex frequency domain s; K is the gain of the superheat control system, indicating the amplification factor of the system for the input signal; τ is the pure time delay, which reflects the time delay existing in the system, that is, the input signal affects the system after τ time; T1 and T2 are the two time constants of the second-order link respectively, which determine the dynamic response characteristics of the system. The larger the time constant, the slower the response speed of the system; s is a complex variable, which is used in Laplace transform to convert the time-domain signal to the complex frequency domain for analysis.

[0065] S140. The predictive control module constructs a state space model based on the dynamic characteristics, performs state prediction based on the state estimation value and the disturbance estimation value, and outputs a state prediction vector and calculates the first objective function.

[0066] Specifically, construct the state space model: Under the condition of a given controlled object, since the characteristic parameters of the evaporator have the greatest influence on the superheat, the first law of thermodynamics and experimental data are applied to establish the characteristic parameters of the evaporator, and a dynamic model is applied to predict the future change trend of the refrigerant state at the outlet of the evaporator:

[0067] State equation: x k =F k x k-1 +B k u k +w k .

[0068] Observation equation: z k =H k x k +v k .

[0069] Among them:

[0070] x k is the state vector, defined as:

[0071] T sh,k is the superheat at the outlet of the evaporator at the kth sampling moment, is the mass flow rate of the refrigerant flowing out of the evaporator at the kth sampling moment, FlowState k is the flow state identifier at the kth sampling moment, discrete values: 1 = vapor flow, 2 = mist flow, 3 = annular flow.

[0072] u k is the control input vector, defined as: u k =[EVA k .

[0073] EVA kis the pulse opening of the electronic expansion valve (EEV) at the k-th sampling moment.

[0074] F k is the state transition matrix, defined as:

[0075] f ij (i = 1, 2, 3; j = 1, 2, 3) are coefficients that need to be determined according to the physical characteristics of the actual system, experimental data, or theoretical analysis. For example, f 12 represents the influence coefficient of the mass flow rate at the previous moment on the superheat at the current moment.

[0076] B k is the control input matrix, defined as:

[0077] b 11 b 21 b 31 represent the influence coefficients of the opening of the electronic expansion valve on the superheat, mass flow rate, and flow state identifier, respectively, and need to be determined according to the actual situation.

[0078] w k is the process noise vector, assumed to follow a Gaussian distribution with zero mean and covariance matrix Q k .

[0079] H k is the observation matrix. Since the sensors in this system can directly measure the superheat, mass flow rate, and flow state identifier, the observation matrix can be defined as:

[0080]

[0081] v k is the observation noise vector, assumed to follow a Gaussian distribution with zero mean and covariance matrix R k .

[0082] The predictive control module uses the state estimate as the initial condition of the current state, and at the same time considers the influence of the disturbance estimate on the system. Using the constructed state space model, through mathematical calculations and derivations, it predicts the state changes of the system in the future for a period of time to obtain the state prediction value.

[0083] To improve the adaptability of this filter to the dynamic working conditions of the system and improve the stability of the algorithm, objective function optimization is adopted: introducing a flow state switching penalty term, and adjusting the Kalman gain K k to minimize the objective function; optionally, the first objective function satisfies the following relationship:

[0084]

[0085] where JKk is the first objective function, is the preset target value of superheat degree, ΔS is the fluid state switching frequency, and λ is the weight coefficient.

[0086] The algorithm includes two steps: prediction and update:

[0087] Prediction step:

[0088] Predicted state: x k|k-1 = F k-1 x k-1|k-1 + B k-1 u k-1

[0089] Predicted covariance:

[0090] Update step:

[0091] Calculate the Kalman gain:

[0092] Update state: x k|k = x k|k-1 + K k (z k - H k x k|k-1 )

[0093] Update covariance: P k|k = (I - K k H k )P k|k-1

[0094] S150. The parameter identification and self-tuning module self-tunes the PID control parameters according to the dynamic characteristics and the second objective function.

[0095] Specifically, the dynamic characteristics of the system directly determine the initial range and adjustment direction of the PID control parameters. The second objective function is a quantitative index to measure the control effect, which converts the control objective into an optimizable numerical value through a mathematical expression. For example, taking the Integral of Time-multiplied Absolute Error (ITAE) as the second objective function. Connect the switch K in Figure 1 to point a. The parameter identification and self-tuning module generates a set of candidate PID parameter combinations according to the current system dynamic characteristics; for each set of parameters, calculate the value of the second objective function through simulation or actual operation; through an optimization algorithm, such as the simplex method, iteratively adjust the parameters until the objective function value is minimized, that is, find the optimal PID control parameters, and assign the optimal PID control parameters to the PID control module. Figure 1 Connect the switch K in

[0096] S160. The PID control module outputs an electronic expansion valve drive signal based on the deviation between the superheat prediction value in the state prediction vector and the preset superheat target value, as well as the tuned PID control parameters.

[0097] Specifically, an incremental PID controller is adopted, and the system state x estimated by the Kalman filter is used k |k-1 = F k- 1x k -1|k-1 + B k-1 u k-1 to calculate the control quantity. The proportional (P) link outputs the control quantity proportionally according to the error between the currently estimated state and the desired state, and responds quickly to the error; the integral (I) link integrates the error to eliminate the steady-state error and ensure that the system can finally stabilize at the desired state; the derivative (D) link adjusts the control quantity according to the change rate of the error to improve the dynamic performance of the system. By providing accurate state estimation through the Kalman filter, the PID controller can perform more precise control, reduce overshoot, shorten the adjustment time, and improve the system's resistance to interference and model uncertainty. The deviation between the state prediction value and the preset superheat target value is

[0098] S170. The electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve drive signal to adjust the refrigerant flow rate.

[0099] Specifically, Figure 4 This is a curve comparison diagram of the optimal superheat estimation value of the traditional Kalman filter and the optimal superheat estimation value of the improved Kalman filter provided by the embodiment of the present invention. It can be seen from Figure 4 that the superheat prediction structure of the improved Kalman filter is more accurate. Figure 5 This is an adaptive observation noise covariance curve diagram of the improved Kalman filter control provided by the embodiment of the present invention. It can be seen from Figure 5 that the oscillation amplitude of the adaptive observation noise covariance of the improved Kalman filter control is relatively stable at the fluid state switching point. Figure 6 This is a superheat oscillation curve diagram under traditional control provided by the embodiment of the present invention. Figure 7 This is a superheat oscillation curve diagram of the improved Kalman filter control provided by the embodiment of the present invention. After Figure 6 comparing with Figure 7 it can be known that the oscillation amplitude of the control method for suppressing superheat oscillation provided by the embodiment of the present invention is reduced by 70%.

[0100] An embodiment of the present invention provides a superheat control method and system for suppressing superheat oscillation. The method includes: introducing process noise into the evaporator and observation noise into the measurement module, and the measurement module measures the parameters at the outlet of the evaporator in real time; after processing the parameters at the outlet of the evaporator by the Kalman filter, the state estimate value and the disturbance estimate value are output; the process identification module identifies the dynamic characteristics of the superheat control system through the transfer function; the predictive control module constructs a state space model based on the dynamic characteristics, and performs state prediction based on the state estimate value and the disturbance estimate value, outputs the state prediction vector and calculates the first objective function; the parameter identification self-tuning module self-tunes the PID control parameters according to the dynamic characteristics and the second objective function; the PID control module outputs the electronic expansion valve drive signal according to the deviation between the predicted superheat value in the state prediction vector and the preset superheat target value, and the tuned PID control parameters; the electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve drive signal to adjust the refrigerant flow rate. The technical solution provided by the embodiment of the present invention effectively reduces the control deviation by predicting the superheat using an improved Kalman filter through flow state identification and noise adaptation, and by setting a process identification module and using the transfer function to identify the dynamic characteristics of the superheat control system; setting a predictive control module to construct a state space model based on the dynamic characteristics, and performing state prediction based on the state estimate value and the disturbance estimate value, outputting the state prediction vector and calculating the first objective function; setting a parameter identification self-tuning module to self-tune the PID control parameters according to the dynamic characteristics and the second objective function, so as to be applicable to the fast response and tracking control of the superheat in dynamic working conditions such as variable frequency compressors and variable loads, significantly suppressing the superheat oscillation caused by the refrigeration system operating at low superheat, and improving the system stability.

[0101] Optionally, the predictive control module outputs the state prediction vector according to the following formula:

[0102] x k |k-1=F k-1 x k-1 |k-1+B k-1 u k-1 ; where, x k|k-1 is the state prediction vector at time k, F k-1 is the state transition matrix, B k-1 is the control input matrix, u k-1 is the control input vector at time k-1, x k-1|k-1 is the state estimate vector at time k-1.

[0103] Optionally, the second objective function satisfies the following relationship:

[0104] where, J ITAEis the absolute error objective function of the integration time, that is, the second objective function; |e(t)| is the absolute value of the error; t is the time variable.

[0105] Optionally, the state transition matrix and the control input matrix are established through the following model:

[0106]

[0107] In the formula: M is the mass of the refrigerant flowing out of the evaporator, is the mass flow rate of the refrigerant flowing out of the evaporator, T evap,ou is the refrigerant temperature at the outlet of the evaporator, h at is the convective heat transfer coefficient between the evaporator wall and the outside, A evap is the area of the evaporator wall, c evap,out is the specific heat of the refrigerant at the outlet of the evaporator, T at is the outside ambient temperature, is the change in the mass flow rate of the refrigerant in the evaporator, h evap,in is the specific enthalpy of the refrigerant at the inlet of the evaporator, T sat is the refrigerant saturation temperature corresponding to the refrigerant pressure at the outlet of the evaporator, C d is the flow coefficient of the electronic expansion valve, A eev is the flow area of the electronic expansion valve, ρ is the density of the refrigerant at the inlet of the electronic expansion valve, ΔP is the pressure difference across the electronic expansion valve, h eev is the valve needle opening of the electronic expansion valve, β is the valve needle cone angle of the electronic expansion valve, d eev is the valve needle aperture of the electronic expansion valve.

[0108] Specifically, a scatter plot is drawn through experimental data, the distribution trend of the data is observed, and a suitable functional form is selected to fit the data to obtain the specific functional relationship of the flow regime identifier By drawing a scatter plot through experimental data and using polynomial fitting, the specific functional relationship between the valve needle opening h eev of the electronic expansion valve and the pulse opening EVA k is determined as h eev = a0 + a1EVA k + a2EVA k 2 + a3EVA k 3 +…+ a n EVA k n .

[0109] Optionally, step S150 specifically includes:

[0110] The parameter identification and self-tuning module calculates the objective function value under the initial PID control parameters according to the second objective function, and uses the simplex method to optimize the PID control parameters to minimize the second objective function.

[0111] Optionally, the observation noise covariance is dynamically adjusted according to the flow regime identifier.

[0112] Optionally, the observation noise covariance is dynamically adjusted according to the flow regime identifier and should satisfy the following relationship:

[0113]

[0114] where is the observation noise covariance, and FlowState k is the flow regime identifier at the k-th moment, and R stable is the value of the observation noise covariance when the vapor flow is in the stable state, and R oscillate1 is the value of the observation covariance when the flow regime identifier is the first oscillating state, mist flow, and R oscillate2 is the value of the observation covariance when the flow regime identifier is the second oscillating state, annular flow, and R oscillate2 ≥R oscillate1 >R stable .

[0115] Continue to refer to Figure 1 , the superheat control system includes: a PID control module 11, an electronic expansion valve driver 12, an electronic expansion valve 13, an evaporator 14, a measurement module 15, a Kalman filter 16, a process identification module 17, a parameter identification and self-tuning module 18, and a predictive control module 19, which are connected in sequence.

[0116] The evaporator 14 introduces process noise, and the measurement module 15 introduces observation noise. The measurement module 15 is used to measure the parameters at the evaporator outlet in real time; the parameters at the evaporator outlet include the superheat at the evaporator outlet, the refrigerant mass flow rate flowing out of the evaporator, and the flow regime identifier.

[0117] The Kalman filter 16 is used to process the parameters at the evaporator outlet and then output the state estimate value and the disturbance estimate value.

[0118] The process identification module 17 is used to identify the dynamic characteristics of the superheat control system through the transfer function.

[0119] The predictive control module 18 is used to construct a state space model based on the dynamic characteristics, and perform state prediction based on the state estimate value and the disturbance estimate value, and output the state prediction vector and calculate the first objective function.

[0120] The parameter identification and self-tuning module 19 is used to self-tune the PID control parameters according to the dynamic characteristics and the second objective function.

[0121] The PID control module 11 is configured to output an electronic expansion valve drive signal according to the deviation between the predicted superheat value in the state prediction vector and the preset target value of the superheat, as well as the tuned PID control parameters.

[0122] The electronic expansion valve driver 12 is configured to drive the electronic expansion valve 13 to act according to the electronic expansion valve drive signal, so as to adjust the refrigerant flow rate.

[0123] The superheat control system for suppressing superheat oscillation provided by the embodiments of the present invention can execute the superheat control method for suppressing superheat oscillation provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0124] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0125] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A superheat control method for suppressing superheat oscillation, characterized in that Executed by a superheat control system for suppressing superheat oscillation, the superheat control system comprising: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification self-tuning module, and a predictive control module; the superheat control method comprising: The evaporator introduces process noise, and the measurement module introduces observation noise. The measurement module measures the parameters at the outlet of the evaporator in real time; the parameters at the outlet of the evaporator include the superheat at the outlet of the evaporator, the refrigerant mass flow rate flowing out of the evaporator, and the flow state identifier. After the Kalman filter processes the parameters at the outlet of the evaporator, it outputs a state estimate value and a disturbance estimate value. The process identification module identifies the dynamic characteristics of the superheat control system through a transfer function. The predictive control module constructs a state space model based on the dynamic characteristics, and performs state prediction based on the state estimate value and the disturbance estimate value, outputs a state prediction vector, and calculates a first objective function. The parameter identification self-tuning module self-tunes the PID control parameters according to the dynamic characteristics and a second objective function. The PID control module outputs an electronic expansion valve drive signal according to the deviation between the predicted superheat value in the state prediction vector and the preset target value of superheat, and the tuned PID control parameters. The electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve drive signal to adjust the refrigerant flow rate.

2. The superheat control method according to claim 1, characterized in that, The transfer function of the process identification module satisfies the following relationship: Where, G(s) is the transfer function of the superheat control system, K is the gain of the superheat control system, τ is the pure dead time, T1 and T2 are the two time constants of the second-order link respectively, and s is the complex variable.

3. The superheat control method according to claim 1, wherein The first objective function satisfies the following relationship: Among them, J Kk is the first objective function, is the preset target value of superheat, ΔS is the flow regime switching frequency, and λ is the weight coefficient.

4. The superheat control method according to claim 1, characterized in that The predictive control module outputs a state prediction vector according to the following formula: x k|k-1 = F k-1 x k-1|k-1 + B k-1 u k-1 ; x k|k-1 is the state prediction vector at time k, F k-1 is the state transition matrix, B k-1 is the control input matrix, u k-1 is the control input vector at time k-1, x k-1|k-1 is the state estimation vector at time k-1.

5. The superheat control method according to claim 1, characterized in that The second objective function satisfies the following relationship: Among them, J ITAE is the integral time absolute error objective function, that is, the second objective function; |e(t)| is the absolute value of the error; t is the time variable.

6. The superheat control method according to claim 4, characterized in that The state transition matrix and the control input matrix are established through the following model: Where: M is the mass of the refrigerant flowing out of the evaporator, is the mass flow rate of the refrigerant flowing out of the evaporator, T evap,out is the refrigerant temperature at the outlet of the evaporator, h at is the convective heat transfer coefficient between the evaporator wall and the outside, A evap is the area of the evaporator wall, c evap,out is the specific heat of the refrigerant at the outlet of the evaporator, T at is the outside ambient temperature, is the change in the mass flow rate of the refrigerant in the evaporator, h evap,in is the specific enthalpy of the refrigerant at the inlet of the evaporator, T sat is the saturation temperature of the refrigerant corresponding to the refrigerant pressure at the outlet of the evaporator, C d is the flow coefficient of the electronic expansion valve, A eev is the flow area of the electronic expansion valve, ρ is the density of the refrigerant at the inlet of the electronic expansion valve, ΔP is the pressure difference across the electronic expansion valve, h eev is the needle opening of the electronic expansion valve, β is the needle cone angle of the electronic expansion valve, d eev is the needle aperture of the electronic expansion valve.

7. The superheat control method according to claim 1, characterized in that, The parameter identification self-tuning module self-tunes the PID control parameters according to the dynamic characteristics and the second objective function, including: The parameter identification self-tuning module calculates the objective function value under the initial PID control parameters according to the second objective function, and uses the simplex method to optimize the PID control parameters to make the second objective function the minimum value.

8. The superheat control method according to claim 1, wherein The observation noise covariance is dynamically adjusted according to the flow state identifier.

9. The superheat control method according to claim 8, wherein The observation noise covariance, dynamically adjusted according to the flow state identifier, should satisfy the following relationship: where is the observation noise covariance, FlowState k is the flow state identifier at the k-th moment, R stable is the value of the observation noise covariance in the case of a steady-state vapor flow, R oscillate1 is the value of the observation covariance when the flow state identifier is the first oscillating state, mist flow, R oscillate2 is the value of the observation covariance when the flow state identifier is the second oscillating state, annular flow, R oscillate2 ≥R oscillate1 >R stable 。 10. A superheat control system for suppressing superheat oscillation, characterized in that, For implementing the superheat control method for suppressing superheat oscillation according to any one of claims 1-9, the superheat control system comprises: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification self-tuning module, and a predictive control module connected in sequence; The evaporator introduces process noise, and the measurement module introduces observation noise. The measurement module is used to measure the parameters at the evaporator outlet in real time; the parameters at the evaporator outlet include the superheat at the evaporator outlet, the refrigerant mass flow rate flowing out of the evaporator, and the flow pattern identifier; The Kalman filter is used to process the parameters at the evaporator outlet and output the state estimate value and the disturbance estimate value; The process identification module is used to identify the dynamic characteristics of the superheat control system through the transfer function; The predictive control module is used to construct a state space model based on the dynamic characteristics, and perform state prediction based on the state estimate value and the disturbance estimate value, output the state prediction vector and calculate the first objective function; The parameter identification and self-tuning module is used to self-tune the PID control parameters according to the dynamic characteristics and the second objective function; The PID control module is used to output the electronic expansion valve drive signal according to the deviation between the predicted superheat value in the state prediction vector and the preset target value of the superheat, and the tuned PID control parameters; The electronic expansion valve driver is used to drive the electronic expansion valve to act according to the electronic expansion valve drive signal to adjust the refrigerant flow rate.

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