CN111258212B - Iterative learning refrigeration control system and method based on fractional order - Google Patents
Iterative learning refrigeration control system and method based on fractional order Download PDFInfo
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Abstract
The invention provides a refrigeration control system and method based on fractional order iterative learning. The iterative learning refrigeration control system based on the fractional order comprises a fractional order PID controller, a feedback control unit and a feedforward control unit, wherein the fractional order PID controller is used for obtaining a feedback control quantity of the current operation period according to the refrigeration effect deviation of the refrigeration system of the current operation period and compensating the feedback control quantity into the feedforward control quantity to realize the fractional order PID feedback control of the refrigeration system; and the fractional order iterative learning controller is used for estimating the feedforward control quantity of the next operation period according to the fractional order integral sum of the feedforward control quantity of the current operation period and the refrigerating effect deviation of the refrigerating system under the preset learning gain multiple so as to realize the fractional order iterative learning control on the refrigerating system until the preset refrigerating effect is achieved and finish the iterative control.
Description
Technical Field
The invention belongs to the field of refrigeration control, and particularly relates to a fractional order-based iterative learning refrigeration control system and method.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Refrigeration systems are cooling processes that are created to achieve and maintain the temperature of a product or space below ambient. It is used in food preservation, chemical industry, technological industry, metal cold treatment, medicine production, ice making and other fields. With the rapid development of modern technology, vapor compression refrigeration systems are now the most common means of cooling commercial and residential spaces, resulting in a rapid increase in energy consumption, negative impact on energy and economic balance.
In recent years, linear techniques for vapor compression refrigeration system control have been extensively studied, and such methods are all model-based control methods. However, many of the challenges associated with refrigerant system control come from the essential features of the components themselves and the heat transfer process, which characteristics result in high thermal inertia, dead time, high coupling between variables, and strong non-linearity. Thus, the refrigerant system is a complex system with non-linearity, and it is difficult to obtain an accurate model of the process. In past work, model-based feedback controllers were used to control complex single-stage refrigeration processes, and model inaccuracies would result in poor control performance.
The inventors have found that the control of conventional refrigeration systems all use model-based feedback control methods, which are computationally time consuming, require complex model identification efforts, and require compromises between performance and robust stability when feedback control is applied.
Disclosure of Invention
In order to solve the above problems, a first aspect of the present invention provides a fractional order-based iterative learning refrigeration control system, which improves robustness of the system to external interference, and can effectively utilize operating condition operation data to realize accurate control of a preset refrigeration effect by using a fractional order iterative learning controller, thereby compensating for an effect that a system model cannot be accurately known on control performance.
In order to achieve the purpose, the invention adopts the following technical scheme:
a fractional order based iterative learning refrigeration control system, comprising:
a fractional order PID controller for: according to the refrigeration effect deviation of the refrigeration system in the current operation period, obtaining the feedback control quantity of the current operation period and compensating the feedback control quantity into the feedforward control quantity to realize fractional order PID feedback control on the refrigeration system; the refrigerating effect deviation of the refrigerating system comprises the temperature deviation of a secondary outlet of an evaporator of the refrigerating system and the degree deviation of overheating of a refrigerant at an outlet of the evaporator; the feedback control quantity comprises a compressor rotating speed feedback quantity and a valve opening angle feedback quantity; the feedforward control quantity comprises the rotating speed of the compressor and the opening angle of the valve;
and the fractional order iterative learning controller is used for estimating the feedforward control quantity of the next operation period according to the fractional order integral sum of the feedforward control quantity of the current operation period and the refrigerating effect deviation of the refrigerating system under the preset learning gain multiple so as to realize the fractional order iterative learning control on the refrigerating system until the preset refrigerating effect is achieved and finish the iterative control.
As an embodiment, the expression of the fractional order PID controller is:
The technical scheme has the advantages that the fractional order calculus is introduced into the traditional PID refrigeration control system, the setting freedom degree of the controller is increased, and meanwhile the robustness of the system to external interference is improved.
As an embodiment, the expression of the fractional order iterative learning controller is:
wherein the content of the first and second substances,
the feedforward control quantity is 2 x 1 dimension and represents the degree feedforward control quantity of the j +1 operation period, and comprises the rotating speed of the compressor and the opening angle of the valve;
the feedforward control quantity of the j running period is expressed in 2 x 1 dimension and comprises the rotating speed of the compressor and the opening angle of the valve; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d gamma denotes the fractional order integral of the gamma order, kdIndicating the learning gain.
The technical scheme has the advantages that the operation working condition of the refrigeration control system is periodic, the repetitive noise and interference exist in the operation environment, the precise control on the preset refrigeration effect can be realized by effectively utilizing the working condition operation data by utilizing the fractional order iterative learning controller, and the influence on the control performance caused by the fact that a system model cannot be accurately known is made up.
As an embodiment, ejThe expression of (t) is:
wherein, ref _ Tj out,sec,ePresetting reference temperature, T, for secondary outflow opening of j operation period system evaporatorj out,sec,eActual temperature of secondary flow outlet of evaporator of system for j operation period, ref _ Tj SHPresetting a reference value, T, for the degree of superheat of the refrigerant at the outlet of the evaporator of the refrigeration system for the jth operating cyclej SHThe actual degree of superheat of the refrigerant at the evaporator outlet of the refrigeration system for the j-th operating cycle.
As an embodiment, the fractional order based iterative learning refrigeration control system further comprises:
and the memory is used for storing the refrigerating effect deviation and the feedforward control quantity of each operation period.
In one embodiment, the refrigeration system is a single-stage cycle refrigeration system; the single-stage circulating refrigeration system comprises a variable-speed compressor, an evaporator, an electronic expansion valve and a condenser;
a variable speed compressor for receiving refrigerant in vapor form as a circulating fluid and compressing it at a constant entropy;
a condenser for receiving the compressed refrigerant in vapor form, first exchanged with a secondary stream, and then the vapor condensed into a liquid;
an electronic expansion valve for evaporating liquid refrigerant at low pressure and temperature;
an evaporator for absorbing heat of evaporation of the liquid refrigerant at low pressure and temperature.
In order to solve the above problems, a second aspect of the present invention provides a fractional order-based iterative learning refrigeration control method, which improves robustness of a system to external interference, and can effectively utilize operating condition operation data to realize accurate control of a preset refrigeration effect by using fractional order iterative learning control, thereby compensating for an influence of a system model that is not accurately known on control performance.
In order to achieve the purpose, the invention adopts the following technical scheme:
an iterative learning refrigeration control method based on fractional order comprises the following steps:
according to the refrigeration effect deviation of the refrigeration system in the current operation period, obtaining the feedback control quantity of the current operation period and compensating the feedback control quantity into the feedforward control quantity to realize fractional order PID feedback control on the refrigeration system; inputting the compensated feedforward control quantity into a refrigeration system, outputting a corresponding actual refrigeration effect, further judging whether the actual refrigeration effect reaches a preset refrigeration effect, if so, ending the fractional-order PID control, otherwise, obtaining a feedback control quantity of a corresponding operation period, compensating the feedback control quantity to the feedforward control quantity and continuously inputting the feedback control quantity to the refrigeration system; the refrigerating effect deviation of the refrigerating system comprises the temperature deviation of a secondary outlet of an evaporator of the refrigerating system and the degree deviation of overheating of a refrigerant at an outlet of the evaporator; the feedback control quantity comprises a compressor rotating speed feedback quantity and a valve opening angle feedback quantity; the feedforward control quantity comprises the rotating speed of the compressor and the opening angle of the valve;
and estimating the feedforward control quantity of the next operation period according to the sum of fractional order integrals of the feedforward control quantity of the current operation period and the deviation of the refrigeration effect of the refrigeration system under the preset learning gain multiple during feedback control, so as to realize fractional order iterative learning control on the refrigeration system, and ending the iterative control until the preset refrigeration effect is achieved.
As an embodiment, the process of the fractional order PID control is:
The technical scheme has the advantages that the fractional order calculus is introduced into the traditional PID refrigeration control system, the setting freedom degree of the controller is increased, and meanwhile the robustness of the system to external interference is improved.
As an embodiment, the process of the fractional order iterative learning control is as follows:
wherein the content of the first and second substances,
2 x 1 dimension, degree of the j +1 th operation cycleFeedforward control quantity, including compressor rotation speed and valve opening angle;
the feedforward control quantity of the j running period is expressed in 2 x 1 dimension and comprises the rotating speed of the compressor and the opening angle of the valve; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d gamma denotes the fractional order integral of the gamma order, kdIndicating the learning gain.
The technical scheme has the advantages that the operation working condition of the refrigeration control system is periodic, the repetitive noise and interference exist in the operation environment, the precise control on the preset refrigeration effect can be realized by effectively utilizing the working condition operation data by utilizing the fractional order iterative learning controller, and the influence on the control performance caused by the fact that a system model cannot be accurately known is made up.
As an embodiment, ejThe expression of (t) is:
wherein, ref _ Tj out,sec,ePresetting reference temperature, T, for secondary outflow opening of j operation period system evaporatorj out,sec,eActual temperature of secondary flow outlet of evaporator of system for j operation period, ref _ Tj SHPresetting a reference value, T, for the degree of superheat of the refrigerant at the outlet of the evaporator of the refrigeration system for the jth operating cyclej SHThe actual degree of superheat of the refrigerant at the evaporator outlet of the refrigeration system for the j-th operating cycle.
The invention has the beneficial effects that:
(1) the fractional order PID controller is used as feedback control for adjusting the influence of noise interference on the temperature of the refrigeration system in real time, and the setting freedom degree of the controller is enhanced by a plurality of adjustable parameters contained in the fractional order PID controller;
(2) the fractional order iterative learning controller fully utilizes system operation condition data to construct a feedforward control item so as to improve the accurate tracking control of the next batch operation process of the system on the preset refrigeration effect;
(3) according to the invention, fractional calculus is introduced, so that adjustable parameters of the controller and system setting freedom are increased, and the sensitivity of the controller is improved;
(4) the refrigeration system is difficult to obtain an accurate model due to the strong coupling nonlinear characteristic, and the method can effectively avoid the influence of unknown dynamics of the system on the control performance;
(5) the invention utilizes fractional order iterative learning control to improve the system to deal with the repetitive noise interference, and simultaneously, compared with the traditional iterative learning control, the learning control method has higher convergence rate.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic diagram of a fractional order based iterative learning refrigeration control system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a single-stage cycle refrigeration system in accordance with an embodiment of the present invention.
Fig. 3 is a standard simulation system of a refrigeration system constructed according to an embodiment of the invention.
Fig. 4(a) is the desired output of the evaporator secondary flow outlet temperature of a single-stage cycle refrigeration system of an embodiment of the present invention.
Fig. 4(b) is a temperature desired output for a superheat limit for a single-cycle refrigeration system in accordance with an embodiment of the invention.
Fig. 5(a) is a schematic diagram of the interference signal being the evaporator secondary flux inlet temperature for a single-stage cycle refrigeration system of an embodiment of the present disclosure.
Fig. 5(b) is a schematic diagram showing the interference signal of the single-stage cycle refrigeration system of the embodiment of the present disclosure as the evaporator secondary flux inlet pressure.
Fig. 6(a) is a graph comparing evaporator secondary flow outlet temperature output for a discrete feedback control and fractional order based iterative learning refrigeration control system of an embodiment of the present disclosure.
Fig. 6(b) is a graph comparing the superheat degree limit output for discrete feedback control versus a fractional order based iterative learning refrigeration control system according to an embodiment of the disclosure.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Fig. 1 shows a schematic diagram of the fractional order based iterative learning refrigeration control system of the present embodiment.
In this embodiment, the refrigeration system is a single-stage cycle refrigeration system.
It should be noted that in other embodiments, the refrigeration system may also be a multi-stage cycle refrigeration system.
The following is an example of a single-stage cycle refrigeration system:
as shown in fig. 2, the single-stage cycle refrigeration system comprises a variable speed compressor, an evaporator, an electronic expansion valve and a condenser;
a variable speed compressor for receiving refrigerant in vapor form as a circulating fluid and compressing it at a constant entropy;
a condenser for receiving the compressed refrigerant in vapor form, first exchanged with a secondary stream, and then the vapor condensed into a liquid;
an electronic expansion valve for evaporating liquid refrigerant at low pressure and temperature;
an evaporator for absorbing heat of evaporation of the liquid refrigerant at low pressure and temperature.
And the actual refrigerating temperature output by the refrigerating system is the temperature of the secondary flow outlet of the evaporator.
The control objective of a refrigeration system is to achieve a desired refrigeration temperature, i.e., the evaporator secondary flow outlet temperature desired temperature (T;)out,sec,e). In addition, in order to ensure a high coefficient of performance (COP), a limit value (T) is introduced with respect to the degree of overheatingSH). The control system is therefore designed by controlling two manipulated variables (compressor speed N and valve opening a)v) The two controlled variables are made to track their desired temperatures as efficiently as possible.
As shown in fig. 1, the fractional order-based iterative learning refrigeration control system of the present embodiment includes:
a fractional order PID controller for: according to the refrigeration effect deviation of the refrigeration system in the current operation period, obtaining the feedback control quantity of the current operation period and compensating the feedback control quantity into the feedforward control quantity to realize fractional order PID feedback control on the refrigeration system; the refrigerating effect deviation of the refrigerating system comprises the temperature deviation of a secondary outlet of an evaporator of the refrigerating system and the degree deviation of overheating of a refrigerant at an outlet of the evaporator; the feedback control quantity comprises a compressor rotating speed feedback quantity and a valve opening angle feedback quantity; the feedforward control quantity comprises the rotating speed of the compressor and the opening angle of the valve;
and the fractional order iterative learning controller is used for estimating the feedforward control quantity of the next operation period according to the fractional order integral sum of the feedforward control quantity of the current operation period and the refrigerating effect deviation of the refrigerating system under the preset learning gain multiple so as to realize the fractional order iterative learning control on the refrigerating system until the preset refrigerating effect is achieved and finish the iterative control.
The method combines the advantages of a fractional order PID controller and a fractional order iterative learning control method, and effectively improves the robustness and the tracking precision of the system. Let ref _ Tj out,sec,ePresetting reference temperature, T, for secondary outflow opening of j operation period system evaporatorj out,sec,eActual temperature of secondary flow outlet of evaporator of system for j operation period, ref _ Tj SHPresetting a reference value, T, for the degree of superheat of the refrigerant at the outlet of the evaporator of the refrigeration system for the jth operating cyclej SHTo determine the actual superheat of the refrigerant at the evaporator outlet of the refrigeration system for the jth run cycle, the fractional order PID controller is given by:
wherein the content of the first and second substances,
2 x 1 dimension, and represents the feedback quantity of the rotating speed of the compressor and the feedback quantity of the opening angle of the valve in the j running period; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the temperature deviation of refrigerant superheat at the outlet of the evaporator in the j operation period; d-αDenotes the fractional integral of the alpha order, Dbeta denotes the fractional differential of the beta order, KP、KI、KDRespectively showing a proportional constant, an integral constant and a differential constant of the controller. ref _ Tj out,sec,eAnd Tj out,sec,eRespectively representing the preset desired temperature and the actual temperature of the secondary flow outlet of the evaporator in the j-th operation period, ref _ Tj SHAnd Tj SHRespectively representing the preset overheating temperature and the actual overheating temperature of the evaporator outlet heating agent in the j operation period.
The technical scheme has the advantages that the fractional order calculus is introduced into the traditional PID refrigeration control system, the setting freedom degree of the controller is increased, and meanwhile the robustness of the system to external interference is improved.
The expression of the fractional order iterative learning controller is as follows:
wherein the content of the first and second substances,
the feedforward control quantity is 2 x 1 dimension and represents the degree feedforward control quantity of the j +1 operation period, and comprises the rotating speed of the compressor and the opening angle of the valve;
the feedforward control quantity of the j running period is expressed in 2 x 1 dimension and comprises the rotating speed of the compressor and the opening angle of the valve; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d gamma denotes the fractional order integral of the gamma order, kdIndicating the learning gain.
The technical scheme has the advantages that the operation condition of the refrigeration control system is periodic, repetitive noise and interference exist in the operation environment, the fractional order iterative learning controller fully utilizes the system operation condition data to construct the feedforward control item, the accurate control on the preset refrigeration effect can be realized by effectively utilizing the operation condition data, and the influence on the control performance caused by the fact that a system model cannot be accurately known is made up.
As shown in fig. 1, the fractional order-based iterative learning refrigeration control system further includes:
and the memory is used for storing the refrigerating effect deviation and the feedforward control quantity of each operation period.
The iterative learning refrigeration control system based on fractional order of the embodiment can realize accurate tracking of preset refrigeration effect by system output along with increase of system operation batches, and the following steps are given:
the refrigeration effect and the refrigeration temperature are taken as examples as follows:
wherein α represents a fractional order of integration;
a refrigeration temperature error function representing a k +1 operating cycle at an alpha order fractional order integral;
representing a desired refrigeration temperature function at an alpha order fractional order integral;
an actual refrigeration temperature function of k +1 operating period under alpha order fractional order integration;
represents an alpha-1 differential operation; w (t-tau) represents a refrigeration system state transition matrix; u. ofk+1(τ) represents the control input for k +1 run cycles; tau represents a time variable in the integral operation and has a value range of 0, T]；
Representing a refrigerant system noise interference signal;
the formula can be arranged by utilizing Gronwall's theorem and lambda norm to obtain
Wherein the content of the first and second substances,
wherein k isdRepresenting a learning gain of a fractional order iterative learning controller; i isnRepresenting an n x n dimensional identity matrix; t represents the maximum operation time of the system; o (-) denotes high order infinitesimal; q is a constant coefficient>1/α。
It is concluded from the above equation that there is a suitable PID control parameter KP、KI、KDAnd an iterative learning gain kdEquation (4) can be made to hold.
The proposed method will be verified with a benchmark test system. The benchmarking system provides a single-stage cycle refrigeration system as shown in fig. 2, which utilizes R404a refrigerant. A standard simulation system for a refrigeration system as shown in fig. 3 was set up, introducing the disturbances shown in table 1. The desired output of the controlled system output and the system disturbance are shown in fig. 4(a) -5 (b). Secondary flow outlet temperature (T) of evaporator of controlled systemsec,evap,out) Limit value of degree of superheat (T)SH) The desired output of the controlled system is shown in fig. 4(a) and fig. 4(b), respectively, the disturbance signal of the controlled system is the evaporator secondary flux inlet temperature, as shown in fig. 5(a), and the disturbance signal of the controlled system is the evaporator secondary flux inlet pressure, as shown in fig. 5(b), a conventional discrete feedback control system is provided in the benchmark test system.
TABLE 1 interference vector
The sampling time of the simulation system is 1s, the simulation duration is 1200s, and the parameters of the given fractional order PID controller are respectively KP＝0.1、KI＝0.05、KDTo make the controlled variable converge faster, the iterative learning gain k is 0.08dNote that the fractional order-based iterative learning refrigeration control method proposed in this embodiment is C1, and the discrete feedback controller provided by the benchmark test system is C2.
Fig. 6(a) and 6(b) show the system output tracking plots for two control methods. Fig. 6(a) presents a graph of discrete feedback control versus evaporator secondary flow outlet temperature output for a fractional order based iterative learning refrigeration control system. Fig. 6(b) presents a graph comparing the superheat limit output for discrete feedback control versus a fractional order based iterative learning refrigeration control system. From fig. 6(a) and 6(b), it can be seen that the tracking performance of the fractional order based iterative learning refrigeration control system on the evaporator secondary flow outlet temperature and superheat degree is superior to that of the discrete feedback control system, especially on disturbances. In order to quantitatively analyze the control effect, the following eight performance indexes and one comprehensive index are used for further comparative evaluation, and the evaluation results are shown in table 2. The 8 performance indexes shown in table 2 further verify the control effect of the fractional order-based iterative learning refrigeration control strategy of the embodiment, and the control effect of the fractional order-based iterative learning refrigeration control method is significantly superior to that of discrete feedback control, and the comprehensive index J of the fractional order-based iterative learning refrigeration control method is improved by 63% compared with that of the comprehensive index J of the discrete feedback control.
TABLE 2 quantitative comparison of fractional order based iterative learning refrigeration control systems with discrete feedback control systems
| Index (I) | C2vsC1 |
| RIAE1(C2,C1) | 0.7289 |
| RIAE2(C2,C1) | 0.2678 |
| RITAE1(C2,C1,tc1,ts1) | 0.1137 |
| RITAE2(C2,C1,tc2,ts2) | 0.0439 |
| RITAE2(C2,C1,tc3,ts3) | 0.0377 |
| RITAE2(C2,C1,tc4,ts4) | 0.1653 |
| RIAV U1(C2,C1) | 1.0383 |
| RIAV U2(C2,C1,tc1,ts1) | 1.0514 |
| J(C2,C1,tc1,ts1) | 0.3676 |
Wherein e isi(t) represents an error of the ith iteration control; IAEiRepresenting the ith iteration control absolute error integral; IAVUiAn absolute error integral representing the ith iteration control change; ITAEiRepresenting the ith iteration control absolute error time integral; RIAEiRepresenting the ith iteration control absolute error integral ratio; RITAEiRepresenting the ith iteration control absolute error time integral ratio; RIAvuiAn absolute error integral ratio representing the control change of the ith iteration; t is tck，tskAre respectively used for defining an integration interval, where tck<tsk，k＝1,2,3,4；wiA weight coefficient indicating each index; j (C)2,C1) And the comprehensive index of the iterative learning refrigeration control system based on fractional order compared with the discrete feedback control system is shown.
The beneficial effect of this embodiment is:
the fractional order PID controller is used as feedback control for adjusting the influence of noise interference on the temperature of the refrigeration system in real time, and the setting freedom degree of the controller is enhanced by a plurality of adjustable parameters contained in the fractional order PID controller; the fractional order iterative learning controller fully utilizes system operation condition data to construct a feedforward control item so as to improve the accurate tracking control of the next batch operation process of the system on the preset refrigeration effect; the fractional calculus is introduced, so that the adjustable parameters of the controller and the system setting freedom are increased, and the sensitivity of the controller is improved; the refrigeration system is difficult to obtain an accurate model due to the strong coupling nonlinear characteristic, and the influence of unknown dynamics of the system on the control performance can be effectively avoided; the fractional order iterative learning control is utilized to improve the system to deal with the repetitive noise interference, and meanwhile, compared with the traditional iterative learning control, the learning control method has higher convergence rate.
Example two
The embodiment provides an iterative learning refrigeration control method based on fractional order, which comprises the following steps:
according to the refrigeration effect deviation of the refrigeration system in the current operation period, obtaining the feedback control quantity of the current operation period and compensating the feedback control quantity into the feedforward control quantity to realize fractional order PID feedback control on the refrigeration system; inputting the compensated feedforward control quantity into a refrigeration system, outputting a corresponding actual refrigeration effect, further judging whether the actual refrigeration effect reaches a preset refrigeration effect, if so, ending the fractional-order PID control, otherwise, obtaining a feedback control quantity of a corresponding operation period, compensating the feedback control quantity to the feedforward control quantity and continuously inputting the feedback control quantity to the refrigeration system; the refrigerating effect deviation of the refrigerating system comprises the temperature deviation of a secondary outlet of an evaporator of the refrigerating system and the degree deviation of overheating of a refrigerant at an outlet of the evaporator; the feedback control quantity comprises a compressor rotating speed feedback quantity and a valve opening angle feedback quantity; the feedforward control quantity comprises the rotating speed of the compressor and the opening angle of the valve;
and estimating the feedforward control quantity of the next operation period according to the sum of fractional order integrals of the feedforward control quantity of the current operation period and the deviation of the refrigeration effect of the refrigeration system under the preset learning gain multiple during feedback control, so as to realize fractional order iterative learning control on the refrigeration system, and ending the iterative control until the preset refrigeration effect is achieved.
As an embodiment, the process of the fractional order PID control is:
The technical scheme has the advantages that the fractional order calculus is introduced into the traditional PID refrigeration control system, the setting freedom degree of the controller is increased, and meanwhile the robustness of the system to external interference is improved.
As an embodiment, the process of the fractional order iterative learning control is as follows:
wherein the content of the first and second substances,
the feedforward control quantity is 2 x 1 dimension and represents the degree feedforward control quantity of the j +1 operation period, and comprises the rotating speed of the compressor and the opening angle of the valve;
the feedforward control quantity of the j running period is expressed in 2 x 1 dimension and comprises the rotating speed of the compressor and the opening angle of the valve; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d gamma denotes the fractional order integral of the gamma order, kdIndicating the learning gain.
The technical scheme has the advantages that the operation working condition of the refrigeration control system is periodic, the repetitive noise and interference exist in the operation environment, the precise control on the preset refrigeration effect can be realized by effectively utilizing the working condition operation data by utilizing the fractional order iterative learning controller, and the influence on the control performance caused by the fact that a system model cannot be accurately known is made up.
The beneficial effect of this embodiment is:
the fractional order PID controller is used as feedback control for adjusting the influence of noise interference on the temperature of the refrigeration system in real time, and the setting freedom degree of the controller is enhanced by a plurality of adjustable parameters contained in the fractional order PID controller; the fractional order iterative learning controller fully utilizes system operation condition data to construct a feedforward control item so as to improve the accurate tracking control of the next batch operation process of the system on the preset refrigeration effect; the fractional calculus is introduced, so that the adjustable parameters of the controller and the system setting freedom are increased, and the sensitivity of the controller is improved; the refrigeration system is difficult to obtain an accurate model due to the strong coupling nonlinear characteristic, and the influence of unknown dynamics of the system on the control performance can be effectively avoided; the fractional order iterative learning control is utilized to improve the system to deal with the repetitive noise interference, and meanwhile, compared with the traditional iterative learning control, the learning control method has higher convergence rate.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An iterative learning refrigeration control system based on fractional order, comprising:
a fractional order PID controller for: according to the refrigeration effect deviation of the refrigeration system in the current operation period, obtaining the feedback control quantity of the current operation period and compensating the feedback control quantity into the feedforward control quantity to realize fractional order PID feedback control on the refrigeration system; the refrigerating effect deviation of the refrigerating system comprises the temperature deviation of a secondary outlet of an evaporator of the refrigerating system and the degree deviation of overheating of a refrigerant at an outlet of the evaporator; the feedback control quantity comprises a compressor rotating speed feedback quantity and a valve opening angle feedback quantity; the feedforward control quantity comprises the rotating speed of the compressor and the opening angle of the valve;
the fractional order iterative learning controller is used for estimating the feedforward control quantity of the next operation period according to the fractional order integral superposition sum of the feedforward control quantity of the current operation period and the refrigerating effect deviation of the refrigerating system under the preset learning gain multiple so as to realize the fractional order iterative learning control on the refrigerating system until the preset refrigerating effect is achieved and finish the iterative control;
the expression of the fractional order iterative learning controller is as follows:
wherein the content of the first and second substances,
the feedforward control quantity is 2 x 1 dimension and represents the degree feedforward control quantity of the j +1 operation period, and comprises the rotating speed of the compressor and the opening angle of the valve;
the feedforward control quantity of the j running period is expressed in 2 x 1 dimension and comprises the rotating speed of the compressor and the opening angle of the valve; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d gamma denotes the fractional order integral of the gamma order, kdRepresents a learning gain;
the expression of the fractional order PID controller is:
wherein the content of the first and second substances,
2 x 1 dimension, and represents the feedback quantity of the rotating speed of the compressor and the feedback quantity of the opening angle of the valve in the j running period; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d-αDenotes the fractional integral of the alpha order, Dbeta denotes the fractional differential of the beta order, KP、KI、KDRespectively representing a proportional constant, an integral constant and a differential constant of the fractional order PID controller.
2. The fractional order based iterative learning refrigeration control system of claim 1, wherein e isjThe expression of (t) is:
wherein, ref _ Tj out,sec,ePresetting reference temperature, T, for secondary outflow opening of j operation period system evaporatorj out,sec,eActual temperature of secondary flow outlet of evaporator of system for j operation period, ref _ Tj SHPresetting a reference value, T, for the degree of superheat of the refrigerant at the outlet of the evaporator of the refrigeration system for the jth operating cyclej SHThe actual degree of superheat of the refrigerant at the evaporator outlet of the refrigeration system for the j-th operating cycle.
3. The fractional order based iterative learning refrigeration control system of claim 1, wherein the fractional order based iterative learning refrigeration control system further comprises:
and the memory is used for storing the refrigerating effect deviation and the feedforward control quantity of each operation period.
4. The fractional order based iterative learning refrigeration control system of claim 1, wherein said refrigeration system is a single-stage cycle refrigeration system; the single-stage circulating refrigeration system comprises a variable-speed compressor, an evaporator, an electronic expansion valve and a condenser;
a variable speed compressor for receiving refrigerant in vapor form as a circulating fluid and compressing it at a constant entropy;
a condenser for receiving the compressed refrigerant in vapor form, first exchanged with a secondary stream, and then the vapor condensed into a liquid;
an electronic expansion valve for evaporating liquid refrigerant at low pressure and temperature;
an evaporator for absorbing heat of evaporation of the liquid refrigerant at low pressure and temperature.
5. An iterative learning refrigeration control method based on fractional order is characterized by comprising the following steps:
according to the refrigeration effect deviation of the refrigeration system in the current operation period, obtaining the feedback control quantity of the current operation period and compensating the feedback control quantity into the feedforward control quantity to realize fractional order PID feedback control on the refrigeration system; inputting the compensated feedforward control quantity into a refrigeration system, outputting a corresponding actual refrigeration effect, further judging whether the actual refrigeration effect reaches a preset refrigeration effect, if so, ending the fractional-order PID control, otherwise, obtaining a feedback control quantity of a corresponding operation period, compensating the feedback control quantity to the feedforward control quantity and continuously inputting the feedback control quantity to the refrigeration system; the refrigerating effect deviation of the refrigerating system comprises the temperature deviation of a secondary outlet of an evaporator of the refrigerating system and the degree deviation of overheating of a refrigerant at an outlet of the evaporator; the feedback control quantity comprises a compressor rotating speed feedback quantity and a valve opening angle feedback quantity; the feedforward control quantity comprises the rotating speed of the compressor and the opening angle of the valve;
estimating the feedforward control quantity of the next operation period according to the sum of fractional order integrals of the feedforward control quantity of the current operation period and the deviation of the refrigeration effect of the refrigeration system under the preset learning gain multiple during the feedback control so as to realize the fractional order iterative learning control of the refrigeration system until the preset refrigeration effect is achieved, and ending the iterative control;
wherein, the process of the fractional order iterative learning control is as follows:
wherein the content of the first and second substances,
the feedforward control quantity is 2 x 1 dimension and represents the degree feedforward control quantity of the j +1 operation period, and comprises the rotating speed of the compressor and the opening angle of the valve;
2 x 1 dimension, represents the feedforward control quantity of the j running period,including compressor speed and valve opening angle; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d gamma denotes the fractional order integral of the gamma order, kdRepresents a learning gain;
the process of fractional order PID control is as follows:
wherein the content of the first and second substances,
2 x 1 dimension, and represents the feedback quantity of the rotating speed of the compressor and the feedback quantity of the opening angle of the valve in the j running period; e.g. of the typej(t) is 2 x 1 dimension, and the temperature deviation of the secondary outlet of the evaporator of the refrigerating system and the degree deviation of the refrigerant overheating at the outlet of the evaporator in the j operation period; d-αDenotes the fractional integral of the alpha order, Dbeta denotes the fractional differential of the beta order, KP、KI、KDRespectively representing a proportional constant, an integral constant and a differential constant of the fractional order PID controller.
6. The fractional order based iterative learning refrigeration control method of claim 5, wherein e isjThe expression of (t) is:
wherein, ref _ Tj out,sec,ePresetting reference temperature, T, for secondary outflow opening of j operation period system evaporatorj out,sec,eActual temperature of secondary flow outlet of evaporator of system for j operation period, ref _ Tj SHPresetting a reference value, T, for the degree of superheat of the refrigerant at the outlet of the evaporator of the refrigeration system for the jth operating cyclej SHThe actual degree of superheat of the refrigerant at the evaporator outlet of the refrigeration system for the j-th operating cycle.
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