Unified predictive control parameter setting method and related products

Abstract

The present invention provides a unified predictive control parameter setting method and related products. The above method includes: obtaining a unified predictive control model and its expression, then simplifying the polynomials in the unified predictive control model, and finally obtaining a unified predictive control model that only needs to identify three parameters to be used. The identification process of the static gain, time constant, and delay time includes: recording multiple equilibrium points of the controlled object, estimating the static gain based on the multiple equilibrium points; changing the set value of the controlled object, recording the transition process of the controlled object entering the closed loop, and estimating the time constant and delay time based on the relationship between the change in feedback value and the time change during the transition process. Through this method, a simplified design algorithm can be obtained under the framework of unified predictive control, thereby reducing the difficulty of using the predictive control algorithm, and providing a method for closed-loop identification of the parameters required, thereby improving the availability of the predictive control algorithm.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a unified predictive control parameter setting method and related products. Background Art

[0002] PID algorithm has a long history and is widely used. It has the advantages of few parameters, simple debugging, simple execution (simulation operation is sufficient). However, it has the following disadvantages: 1. The control quality is poor, the overshoot of the transition process is large, the oscillation is obvious and the decay is slow, and it is more difficult to apply it to the large delay object system. 2. The PID parameter setting is difficult. Many parameter setting methods are implemented under abnormal working conditions and the setting time is long.

[0003] In recent years, many advanced algorithms have been developed, such as fuzzy control and predictive control, but these algorithms have the following disadvantages: 1. The calculation is complex, the amount of calculation is large, and it is difficult to implement; 2. There are many parameters and it is difficult to determine. Therefore, these advanced algorithms are all scientific research projects and can only be used by technical experts, and have not been industrialized. Summary of the invention

[0004] In view of the above problems, the present invention proposes a parameter setting method for unified predictive control and related products that overcome the above problems or at least partially solve the above problems.

[0005] An object of the present invention is to reduce the difficulty of using predictive control algorithms.

[0006] A further object of the present invention is to provide a method for closed-loop identification of the required parameters.

[0007] A further object of the present invention is to improve the usability of predictive control algorithms.

[0008] In particular, the present invention provides a parameter setting method for unified predictive control, which comprises:

[0009] The unified predictive control model is obtained. The expression of the unified predictive control model is formula 1:

[0010]

[0011] In formula 1, They are q -1 The first, second and third polynomials of , q represents the displacement operator, y(k) is the object output, u(k) is the control amount, w(k) is the reference trajectory, H P is the prediction time domain;

[0012] Solve the first polynomial according to equation 2, equation 3, and equation 4 respectively The second polynomial The third polynomial

[0013]

[0014] in, is the estimated value of the static gain k of the controlled object, η=κ(1-a)(1-μ), α=μ(1-κ(1-a)), G i (q -1 )=g0+g1q -1 +g2q -2 +…+g i-1 q -(i-1) , a=e -1 / θ , And θ, H l And μ is the preset value, θ is the ratio of the time constant to the sampling period, H l The value range of is 0.5θ~2θ, the value range of μ is 0~1, It is the estimated value of the object delay, T s represents the sampling period, represents the time constant, Indicates the delay time.

[0015] Optionally, the controlled object is approximated as first-order inertia plus pure delay, which is θ, H l And μ assignment: θ = 10, H l =10, μ=0.1,

[0016] Formula 2, Formula 3, and Formula 4 are simplified to Formula 5, Formula 6, and Formula 7:

[0017]

[0018] Optionally, the identification process of the static gain, the time constant, and the delay time includes:

[0019] Record multiple equilibrium points of the controlled object and estimate the static gain based on the multiple equilibrium points;

[0020] Change the set value of the controlled object, record the transition process of the controlled object entering the closed loop, and estimate the time constant and delay time based on the relationship between the change in feedback value and time during the transition process.

[0021] Optionally, the step of estimating the static gain according to the multiple balance points includes: substituting the multiple balance points into a preset formula to estimate the static gain;

[0022] The default formula is:

[0023] in, represents the static gain, y represents the feedback value of the controlled object under the set value, and u represents the control amount of the controlled object under the set value.

[0024] Optionally, the step of estimating the time constant and the delay time according to the relationship between the feedback value change and the time change during the transition process includes:

[0025] Record the feedback value change as 0.1Δ w The time is τ 0.1 , Δ w Indicates the change in the set value;

[0026] Record the feedback value change as 0.5Δ w The time is τ 0.5 ;

[0027] At θ = 10, H l =10, the parameters identified based on the relationship between the feedback value change and time change are:

[0028]

[0029] in, Indicates the identified delay time, represents the identified time constant.

[0030] Optionally, before the step of estimating the time constant and the delay time according to the relationship between the feedback value change and the time change during the transition process, the step further includes:

[0031] Determine whether the controlled object meets the preset rules, the preset rules include: the static gain is accurate, there is no constraint condition on the control quantity, and the originally used time constant is greater than the actual time constant;

[0032] If yes, then continue to execute the step of estimating the time constant and the delay time according to the relationship between the feedback value change and the time change during the transition process.

[0033] Optionally, the step of recording a plurality of equilibrium points of the controlled object includes:

[0034] Identify whether the controlled object has entered the set equilibrium state;

[0035] If so, calculate the control amount and feedback value of the controlled object within a certain period of time under the set equilibrium state;

[0036] Calculate the average value of the control amount and feedback value to get a balance point;

[0037] Change the set value of the controlled object and re-identify whether the controlled object enters the set equilibrium state, so as to obtain another equilibrium point.

[0038] Optionally, the step of identifying whether the controlled object enters a set equilibrium state includes:

[0039] Record the maximum and minimum values ​​of the control quantity within a set number of sampling cycles;

[0040] Calculate the difference between the maximum and minimum values;

[0041] Determine whether the difference is less than a preset threshold;

[0042] If so, it is determined that the controlled object enters the set equilibrium state.

[0043] According to another aspect of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, which implements the steps of any one of the above-mentioned unified predictive control parameter setting methods when the computer program is executed by a processor.

[0044] According to yet another aspect of the present invention, there is provided a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned unified predictive control parameter setting methods are implemented.

[0045] The parameter setting method of the unified predictive control of the present invention provides a unified predictive control model, and the expression of the unified predictive control model is formula 1:

[0046]

[0047] In formula 1, They are q -1 The first, second and third polynomials of , q represents the displacement operator, y(k) is the object output, u(k) is the control amount, w(k) is the reference trajectory, H P is the prediction time domain;

[0048] Solve the first polynomial according to equation 2, equation 3, and equation 4 respectively The second polynomial The third polynomial

[0049]

[0050] in, is the estimated value of the static gain k of the controlled object, η=κ(1-a)(1-μ), α=μ(1-κ(1-a)), G i (q -1 )=g0+g1q -1 +g2q -2 +…+g i-1 q -(i-1) , And θ, H l And μ is the preset value, θ is the ratio of the time constant to the sampling period, H l The value range of is 0.5θ~2θ, the value range of μ is 0~1, It is the estimated value of the object delay, T s represents the sampling period, represents the time constant, Indicates the delay time.

[0051] Furthermore, equation 2, equation 3, and equation 4 are simplified to equation 5, equation 6, and equation 7:

[0052]

[0053] The subsequent identification process of static gain, time constant and delay time includes: recording multiple equilibrium points of the controlled object, estimating static gain based on multiple equilibrium points; changing the set value of the controlled object, recording the transition process of the controlled object entering the closed loop, and estimating the time constant and delay time based on the relationship between the change in feedback value and the change in time during the transition process. Through this method, a simplified design algorithm can be obtained under the framework of unified predictive control, thereby reducing the difficulty of using the predictive control algorithm, and providing a method for closed-loop identification of the parameters required, thereby improving the usability of the predictive control algorithm.

[0054] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0056] Figure 1 is a flow chart of a parameter setting method for unified predictive control according to an embodiment of the present invention;

[0057] Figure 2 A control system block diagram showing a parameter setting method for unified predictive control according to an embodiment of the present invention is shown;

[0058] Figure 3 is a schematic diagram of a transition process of a controlled object entering a closed loop according to a unified predictive control parameter tuning method according to an embodiment of the present invention;

[0059] Figure 4A is a schematic diagram of the change of the feedback value of the controlled object over time according to the unified predictive control parameter setting method according to one embodiment of the present invention;

[0060] Figure 4B is a schematic diagram showing changes in the controlled quantity of a controlled object over time according to a unified predictive control parameter setting method according to an embodiment of the present invention;

[0061] Figure 5 is a schematic diagram of a computer program product of a unified predictive control parameter tuning method according to an embodiment of the present invention;

[0062] Figure 6 is a schematic diagram of a computer-readable storage medium according to a method for parameter tuning of unified predictive control according to an embodiment of the present invention; and

[0063] Figure 7 4 is a schematic diagram of a computer device for a unified predictive control parameter tuning method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] It should be understood by those skilled in the art that the embodiments described below are only some embodiments of the present invention, rather than all embodiments of the present invention, and these embodiments are intended to explain the technical principles of the present invention, rather than to limit the protection scope of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present invention.

[0065] As the requirements for industrial process control continue to increase, people are trying to find methods that can achieve high-quality process control without high requirements for process model accuracy. Against this background, predictive control has gradually developed. Predictive control algorithms have three essential characteristics: predictive model, rolling optimization, and feedback correction. Its biggest feature is the continuous rolling local optimization, which makes the uncertainty caused by model mismatch, distortion, interference, etc. compensated in time, thereby obtaining better dynamic control performance.

[0066] However, the current algorithms all have the following disadvantages: 1. The calculations are complex, the amount of computation is large, and it is difficult to implement; 2. There are many parameters and it is difficult to determine. Therefore, these advanced algorithms are all scientific research projects and can only be used by technical experts, and have not been industrialized.

[0067] To solve the above problems, the present invention provides a parameter setting method for unified predictive control. Figure 1 1 is a flow chart of a parameter setting method for unified predictive control according to an embodiment of the present invention. In this embodiment, the flow chart generally includes:

[0068] Step S101, obtain and simplify multiple polynomials of the unified predictive control model of the controlled object. Among them, the unified predictive control model generally has three essential characteristics: prediction model, rolling optimization and feedback correction, that is, the unified predictive control model will solve the optimal control action and implement the current control according to the given constraints and performance requirements, and correct the prediction of future dynamic behavior by detecting real-time information at each step of the rolling.

[0069] Specifically, first obtain a unified predictive control model. In some optional embodiments, the system block diagram of the unified predictive control model is as follows: Figure 2 As shown, Figure 2 A control system block diagram of a unified predictive control parameter setting method according to an embodiment of the present invention is shown. Figure 2 The system block diagram shown can determine the expression of the unified predictive control model as Equation 1:

[0070]

[0071] In formula 1, They are q -1 The first, second and third polynomials of , q represents the displacement operator, y(k) is the object output, u(k) is the control amount, w(k) is the reference trajectory, H P is the prediction time domain;

[0072] Solve the first polynomial according to equation 2, equation 3, and equation 4 respectively The second polynomial The third polynomial

[0073]

[0074] in, is the estimated value of the static gain k of the controlled object, η=κ(1-a)(1-μ), α=μ(1-κ(1-a)), G i (q -1 )=g0+g1q -1 +g2q -2 +…+g i-1 q -(i-1) , And θ, H l And μ is the preset value, θ is the ratio of the time constant to the sampling period, H l The value range of is 0.5θ~2θ, the value range of μ is 0~1, Represents the estimated value of the object delay (delay time / sampling time, rounded to an integer), T s represents the sampling period, represents the time constant, Indicates the delay time.

[0075] In order to reduce the difficulty of using the above polynomial, the method of the present invention simplifies it so that the controlled object is approximately the first-order inertia plus pure delay, which is θ, H l And μ assignment: θ = 10, H l =10, μ=0.1, finally equation 2, equation 3, and equation 4 can be simplified to equation 5, equation 6, and equation 7:

[0076]

[0077] Step S102, record multiple equilibrium points of the controlled object, and estimate static gain based on the multiple equilibrium points. In the Unified Predictive Control (UPC) model, static gain usually refers to the proportional relationship between the input and output of the controlled object or the controlled process under steady-state conditions. The equilibrium point is a stable state, that is, the control amount of the controlled object at the equilibrium point no longer changes or changes very little, and accordingly, the feedback value no longer changes or changes very little.

[0078] In some optional embodiments, the step of recording multiple equilibrium points of the controlled object may generally include: identifying whether the controlled object enters a set equilibrium state; if so, calculating the control amount and feedback value of the controlled object within a certain period of time in the set equilibrium state; calculating the average value of the control amount and the feedback value to obtain a equilibrium point; changing the set value of the controlled object, and re-identifying whether the controlled object enters the set equilibrium state, thereby obtaining another equilibrium point.

[0079] Optionally, the step of identifying whether the controlled object has entered a set equilibrium state may generally include: recording the maximum and minimum values ​​of the control quantity within a set number of sampling cycles; calculating the difference between the maximum and minimum values; judging whether the difference is less than a preset threshold; if so, judging that the controlled object has entered a set equilibrium state. Since the equilibrium state is a stable state in which both the control quantity and the feedback value change very little, it is possible to record the control quantity within a set number of sampling cycles, and then calculate the difference between the maximum and minimum values ​​in the control quantity. When the difference is less than the preset threshold, it indicates that the controlled object is in an equilibrium state within the set number of sampling cycles.

[0080] Optionally, the step of estimating the static gain according to the multiple balance points may generally include: substituting the multiple balance points into a preset formula to estimate the static gain;

[0081] The default formula is:

[0082] in, represents the static gain, y represents the feedback value of the controlled object under the set value, and u represents the control amount of the controlled object under the set value.

[0083] In order to clearly illustrate the static gain identification in this method, a specific implementation example is proposed: the static gain identification of this method requires recording two equilibrium points. When recording one equilibrium point, 20 T s If the difference between the maximum and minimum values ​​of the control quantity u is less than a certain value, such as 0.05, the object is judged to be in equilibrium. If the criterion is not met, the test is repeated for 20 T s Calculate the average value of the control value u and the feedback value y in this cycle as a balance point (u1, y1). Change the set value and use the same method to get the second balance point (u2, y2), and the static gain can be estimated. Those skilled in the art can determine the set number of sampling periods and the specific number of preset thresholds according to actual conditions.

[0084] Step S103, change the set value of the controlled object, record the transition process of the controlled object entering the closed loop, and estimate the time constant and delay time according to the relationship between the change in feedback value and the time change during the transition process. During the operation of the controlled object, if the set value is changed rapidly and significantly at a certain moment, the dynamic response of the system will be stimulated until the system reaches a stable state again. In this process, the relationship curve between the change in feedback value and the time change can be obtained by recording the transition process.

[0085] In some optional embodiments, before the step of estimating the time constant and the delay time according to the relationship between the change in the feedback value and the time change during the transition process, the step can generally include: judging whether the controlled object meets the preset rules, the preset rules include: the static gain is accurate, there are no constraints on the control amount, and the time constant originally used is greater than the actual time constant; if so, continuing to execute the step of estimating the time constant and the delay time according to the relationship between the change in the feedback value and the time change during the transition process.

[0086] In order to clearly illustrate the time constant and delay time identification steps, a specific implementation example is provided. Figure 3 As shown, Figure 3 1 is a schematic diagram of the transition process of the controlled object entering the closed loop according to the unified predictive control parameter setting method of one embodiment of the present invention. In this embodiment, firstly, the object is confirmed to enter the equilibrium point according to the aforementioned judgment method, and then the set value is changed, and the transition process of the controlled object entering the closed loop is recorded, that is, Figure 3 The steps of estimating the time constant and the delay time according to the relationship between the change in the feedback value and the change in time during the transition process may generally include:

[0087] Record the feedback value change as 0.1Δ w The time is τ 0.1 , Δ w Indicates the change in the set value;

[0088] Record the feedback value change as 0.5Δ w The time is τ 0.5 ;

[0089] At θ = 10, H l =10, the parameters identified based on the relationship between the feedback value change and time change are:

[0090]

[0091] in, Indicates the identified delay time, represents the identified time constant.

[0092] However, it should be noted that usually The identification of the time constant τ is relatively accurate, but there are certain conditions for the identification of the time constant τ. (1) The static constant need to be accurate, (2) the control quantity should not be limited by constraints, (3) the original Should be greater than the actual τ. To meet these conditions, The value is credible. Otherwise, identification should continue.

[0093] In addition, it should be noted that the parameters obtained by the above identification are l =10, for different values, the parameters obtained by identification will have different results, and those skilled in the art can determine the relationship between θ and H in the identification process according to the actual situation. l Parameter value of .

[0094] Through this method, a simplified design algorithm can be obtained under the framework of unified predictive control, thereby reducing the difficulty of using the predictive control algorithm and providing a method for closed-loop identification of the required parameters, thereby improving the usability of the predictive control algorithm.

[0095] To clearly illustrate the method of the present invention, the present invention proposes a simplified design algorithm under the framework of unified predictive control, called HUPC, and its specific derivation process is as follows:

[0096] The form of the object model determined under the framework of unified predictive control is as follows:

[0097]

[0098] Among them, y(k) is the object output, u(k) is the control variable, e(k) is the white noise, d is the time lag, q represents the displacement operator, and A, B, C, and D are polynomials describing the process, specifically:

[0099]

[0100] In addition, the performance indicators are defined as follows:

[0101]

[0102] Among them, w(k) is the reference trajectory, H c is the control time domain, H m is the minimum cost time domain, H P is the prediction time domain, φ, P, Q n and Q d Yes -1 polynomial, P(1) is the steady-state value of P.

[0103] After mathematical derivation, the block diagram of the control system is obtained as follows Figure 2 shown.

[0104] according to Figure 2 The control system block diagram shown shows that the control law satisfies:

[0105]

[0106] in, Equal to

[0107]

[0108] in T, V2, Z2, P are all q -1 The polynomial of .

[0109] In actual engineering, the controlled object can be approximated as a first-order inertia plus pure delay model. Therefore, the transfer function of the controlled object is:

[0110]

[0111] Where K is the static gain, τ is the time constant, τ d is the delay time. In the controller design process, these parameters are estimated values, so Replace K with Replace τ with Instead of τ d .

[0112] HUPC is a discrete control system. Generally, the sampling period is required to be 1 / 5 to 1 / 15 of the object time constant. The symbol θ is used to represent the ratio of the time constant to the sampling period: assumed is the sampling period T s An integer multiple of is an integer, then the discrete transfer function of the object is:

[0113]

[0114] Where a = e -1 / θ ,

[0115] According to the characteristics or features of most process objects, taking into account the requirements of rapidity, robustness, stability, etc., the UPC parameters are selected as shown in Table 1:

[0116] Table 1

[0117]

[0118] After substituting the parameters in Table 1 into the above polynomial, the controller result based on the above conditions is obtained, that is, the polynomial to be tuned is as follows:

[0119]

[0120] in

[0121]

[0122] η=κ(1-a)(1-μ)

[0123] α=μ(1-κ(1-a))

[0124] G i (q -1 )=g0+g1q -1 +g2q -2 +…g i-1 q -(i-1)

[0125]

[0126] In this embodiment, θ=10 is selected, and a data table as shown in Table 2 can be obtained.

[0127] Table 2

[0128] <![CDATA[H l ]]> κ 10 2.116976855 12 1.900496977 14 1.74630116 16 1.631602053 18 1.543460787 20 1.473988272

[0129] H land μ can be subjectively selected. Since the adjustment speed of MPC (Model Predictive Control) control is slower, it has a slight disadvantage, but in general, it is comparable. If you want to speed up the adjustment speed of MPC control, you can choose a smaller H l , but this will affect the stability of the system, so it is recommended to use H in actual industrial process applications l The κ value of θ and θ are both 10. Therefore, considering reducing the confusion of users in setting parameters and taking into account the rapidity, accuracy, stability and robustness in most cases, H is fixed. l =10, μ=0.1, according to Table 2, κ=2.116976885 is determined, and the polynomial to be adjusted is as follows:

[0130]

[0131] It should be noted that the above θ=10 and H l =10, μ=0.1 is an optional embodiment, H l The value range of H is generally 0.5θ~2θ. In some optional embodiments, H l The value range of can also be 0.8θ~1.5θ, for example, H l =θ. The value range of μ is generally 0-1, and can also be 0.05-0.15, for example, μ=0.1. Those skilled in the art can select the preset values ​​of the above parameters according to the actual situation of the controlled object to obtain the polynomial to be adjusted corresponding to the controlled object.

[0132] Substituting the polynomial to be tuned into the relationship of the controlled object block diagram, we can further deduce:

[0133]

[0134] Among them, u(ki) represents the controller output at time ki;

[0135] y(ki) represents the object feedback at time ki;

[0136] w(ki) represents the set value at time ki.

[0137] i=0 represents the current time, i=1 represents the time of the previous sampling cycle, and i=N represents the time of the previous N sampling cycles.

[0138] Summary: The dynamic characteristics of a self-stable controlled object are approximately first-order inertia plus pure delay. is the static gain, is the time constant, It is pure delay, that is, delay time, taking the sampling interval get The controller can operate normally.

[0139] It can be seen that the implementation of this control algorithm is extremely simple, without complex iterative calculations or matrix calculations, only simple arithmetic operations are needed. All subjective design parameters are solidified, and only three model parameters are needed, and identification is setting.

[0140] Specifically, in some optional embodiments, the characteristics of a certain section of an object at the outlet of the fourth-stage superheater of a 500MW unit in a power plant are as follows:

[0141]

[0142] First simplify it to Then take the sampling interval as T s =50 / 10=5, The controller parameters can be directly obtained. The response simulation of the closed-loop system to the set value change is shown in Figure 4. Figure 4A is a schematic diagram of the change of the feedback value of the controlled object over time according to the unified predictive control parameter setting method according to one embodiment of the present invention; Figure 4B It is a schematic diagram of the change of the controlled variable of the controlled object over time according to the unified predictive control parameter setting method according to an embodiment of the present invention.

[0143] pass Figure 4A and Figure 4B It can be seen that the dynamic process has no overshoot and no oscillation, the set value is tracked quickly, and the control quality is good.

[0144] In other optional embodiments, the algorithm of the present invention can also be applied to the temperature and humidity control project of the air conditioning unit of a pharmaceutical laboratory, where the room temperature is controlled by the reheat valve, the set temperature is 22°C, and the cold water valve is used for dehumidification. The temperature and humidity of the external air and the action of the cold water valve are all used as disturbances for temperature control. The static gain is identified by the MPC function block as 0.45, the time constant is 360s, and the delay time is 35s. The fluctuation in the obtained temperature response curve is mainly due to the influence of the disturbance. The actual temperature fluctuation range is ±0.7°C, which fully meets the user's requirements.

[0145] In summary, the present invention proposes a simplified design algorithm under the framework of unified predictive control (abbreviated as UPC), called HUPC, which not only achieves good control quality, but also has the advantages of simple calculation, few parameters, and easy adjustment. All subjective design parameters are solidified, and only three model parameters are needed, and identification is adjustment. The present invention also proposes a closed-loop identification / adjustment parameter method, which is more convenient for on-site technicians to use. The present invention transforms the predictive control algorithm from a scientific research project to an industrial application, and has the conditions to replace the mainstream position of PID.

[0146] The flow chart provided by the present embodiment is not intended to indicate that the operation of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical thinking provided by the present embodiment method, additional changes may be made to the above method.

[0147] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0148] This embodiment also provides a computer program product 10 , a computer readable storage medium 20 , and a computer device 30 . Figure 5 is a schematic diagram of a computer program product 10 according to an embodiment of the present invention, Figure 6 is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention, Figure 7 is a schematic diagram of a computer device 30 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which implements the steps of any of the above-mentioned unified predictive control parameter tuning methods when executed by a processor 32. The computer readable storage medium 20 stores the above-mentioned computer program 11, which implements the steps of any of the above-mentioned unified predictive control parameter tuning methods when executed by the processor 32. The computer device 30 may include a memory 31, a processor 32, and a computer program 11 stored in the memory 31 and running on the processor 32.

[0149] The computer program 11 for performing the operation of the present invention may be an assembly instruction, an instruction set architecture (ISA) instruction, a machine instruction, a machine-related instruction, a microcode, a firmware instruction, a state setting data, a configuration data of an integrated circuit, or a source code or an object code written in any combination of one or more programming languages ​​and process programming languages. The computer program 11 may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider via the Internet). In some embodiments, in order to perform various aspects of the present invention, an electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit.

[0150] For the purposes of the description of the present embodiment, the computer program product 10 is a related product that includes the computer program 11. For the purposes of the description of the present embodiment, the computer readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, which may be any device that can contain, store, communicate, propagate or transmit the program 11 for use with or in conjunction with an instruction execution system, device or apparatus. More specific examples (a non-exhaustive list) of the computer readable storage medium 20 include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[0151] The computer device 30 may be, for example, a server, a desktop computer, a notebook computer, a tablet computer, or a smart phone. In some examples, the computer device 30 may be a cloud computing node. The computer device 30 may be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, a program module may include routines, programs, target programs, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. The computer device 30 may be implemented in a distributed cloud computing environment where remote processing devices linked via a communication network perform tasks. In a distributed cloud computing environment, program modules may be located on a local or remote computing system storage medium including a storage device.

[0152] The computer device 30 may include a processor 32 adapted to execute stored instructions, and a memory 31 providing temporary storage space for the operation of the instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory 31 may include a random access memory (RAM), a read-only memory, a flash memory, or any other suitable storage system.

[0153] The computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows data to be input and output with external devices that may be connected to the computer device. The network adapter / interface may provide communication between the computer device and a network, which is typically shown as a communication network.

[0154] At this point, those skilled in the art should recognize that, although multiple exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived based on the content disclosed in the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and recognized as covering all these other variations or modifications.

Claims

1. A parameter setting method for unified predictive control, comprising: A unified predictive control model is obtained, and the expression of the unified predictive control model is Formula 1: In formula 1, They are q -1 The first, second and third polynomials of , q represents the displacement operator, y(k) is the object output, u(k) is the control amount, w(k) is the reference trajectory, H P is the prediction time domain; Solve the first polynomial according to equation 2, equation 3 and equation 4 respectively The second polynomial The third polynomial in, is the estimated value of the static gain k of the controlled object, η=κ(1-a)(1-μ), α=μ(1-κ(1-a)), G i (q -1 )=g0+g1q -1 +g2q -2 +…+g i-1 q -(i-1) , a=e -1 / θ , And θ, H l and μ is a preset value, θ is the ratio of the time constant to the sampling period, and H l The value range of is 0.5θ~2θ, the value range of μ is 0~1, represents the estimated value of the object delay, T s represents the sampling period, represents the time constant, Indicates the delay time.

2. The parameter setting method of unified predictive control according to claim 1, wherein The controlled object is first-order inertia plus pure delay, and the θ, the H l And the μ assignment: θ = 10, H l =10, μ=0.1, Formula 2, Formula 3, Formula 4 are simplified to Formula 5, Formula 6, Formula 7:

3. The parameter setting method of unified predictive control according to claim 2, wherein: The identification process of the static gain, the time constant, and the delay time includes: Recording a plurality of equilibrium points of the controlled object, and estimating the static gain according to the plurality of equilibrium points; The set value of the controlled object is changed, the transition process of the controlled object entering a closed loop is recorded, and the time constant and the delay time are estimated according to the relationship between the change in the feedback value and the change in time during the transition process.

4. The parameter setting method of unified predictive control according to claim 3, wherein: The step of estimating the static gain according to the plurality of balance points comprises: substituting the plurality of balance points into a preset formula to estimate the static gain; The preset formula is: Among them, the represents the static gain, y1 and y2 represent the feedback value of the controlled object under the set value, and u1 and u2 represent the control amount of the controlled object under the set value.

5. The unified predictive control parameter setting method according to claim 3, wherein: The step of estimating the time constant and the delay time according to the relationship between the feedback value change and the time change during the transition process comprises: Record the feedback value change as 0.1Δ w The time is τ 0.1 , the Δ w Indicates the change in the set value; Record the feedback value change as 0.5Δ w The time is τ 0.5 ; At θ = 10, H l =10, the parameters identified based on the relationship between the feedback value change and the time change are: in, Indicates the identified delay time, represents the identified time constant.

6. The unified predictive control parameter setting method according to claim 3, wherein: Before the step of estimating the time constant and the delay time according to the relationship between the feedback value change and the time change during the transition process, the step further includes: Determining whether the controlled object satisfies preset rules, wherein the preset rules include: the static gain is accurate, there are no constraints on the control amount, and the originally used time constant is greater than the actual time constant; If yes, then continue to execute the step of estimating the time constant and the delay time according to the relationship between the feedback value change and the time change during the transition process.

7. The parameter setting method of unified predictive control according to claim 3, wherein: The step of recording a plurality of equilibrium points of the controlled object comprises: Identifying whether the controlled object has entered a set equilibrium state; If so, calculating the control amount and feedback value of the controlled object within a fixed time period under the set equilibrium state; Calculating an average value of the control amount and the feedback value to obtain a balance point; The set value of the controlled object is changed, and whether the controlled object enters a set equilibrium state is re-identified, so as to obtain another equilibrium point.

8. The unified predictive control parameter setting method according to claim 7, wherein: The step of identifying whether the controlled object has entered a set equilibrium state comprises: Record the maximum and minimum values ​​of the control quantity within a set number of sampling cycles; calculating the difference between the maximum value and the minimum value; Determine whether the difference is less than a preset threshold; If so, it is determined that the controlled object enters the set equilibrium state.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the parameter setting method for unified predictive control according to any one of claims 1 to 8 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the parameter tuning method of unified predictive control described in any one of claims 1 to 8 are implemented.

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