Staircase first-order fuzzy predictive control method and device

Abstract

The application provides a stepped first-order fuzzy predictive control method and device, wherein the method comprises the following steps: obtaining a plurality of predictive control components according to preset conditions of control action; obtaining process output predictive values of a (t+N)th moment corresponding to the plurality of predictive control components respectively based on a process output value of a tth moment, a control amount output value of the tth moment and the plurality of predictive control components; obtaining a predictive control deviation and a deviation change rate of process output of the (t+N)th moment corresponding to the plurality of predictive control components respectively according to the process output predictive values of the (t+N)th moment corresponding to the plurality of predictive control components respectively; determining a first predictive control component based on the predictive control deviation and the deviation change rate of the process output of the (t+N)th moment corresponding to the plurality of predictive control components respectively; and controlling a thermal first-order inertia system of a coal-fired unit according to the first predictive control component. The application has good adaptability to outer ring disturbance and time-varying characteristics of process control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal-fired unit process control, and particularly relates to a stepwise first-order fuzzy predictive control method and device. BACKGROUND

[0002] In recent years, advanced control theory is increasingly used in coal-fired unit process control. In control links with large thermal inertia and thermal delay, model-based predictive control methods gradually become mainstream, and have good application effects on non-time-varying control objects and control problems. However, the estimation method of future prediction quantity in modern control methods based on predictive control theory mostly adopts constant estimation. Since the actual control object is generally a large inertia system, the process of the control variable basically changes continuously and unidirectionally, and therefore the use of constant estimation method of prediction quantity may have the problem of poor control effect. SUMMARY

[0003] The present application aims to at least partly solve one of the problems in the related art.

[0004] To this end, the first aspect of the present application provides a stepwise first-order fuzzy predictive control method, comprising:

[0005] obtaining a plurality of predictive control components according to preset conditions of control action;

[0006] obtaining process output prediction values of the plurality of predictive control components at the t+N time point corresponding to each of the plurality of predictive control components based on the process output value at the t time point, the control quantity output value at the t time point and the plurality of predictive control components; wherein t and N are positive integers;

[0007] obtaining prediction control deviations and deviation change rates of process outputs of the plurality of predictive control components at the t+N time point corresponding to each of the plurality of predictive control components according to the process output prediction values of the plurality of predictive control components at the t+N time point corresponding to each of the plurality of predictive control components;

[0008] determining a first predictive control component based on the prediction control deviations and deviation change rates of process outputs of the plurality of predictive control components at the t+N time point corresponding to each of the plurality of predictive control components;

[0009] controlling the thermal first-order inertia system of the coal-fired unit according to the first predictive control component.

[0010] The second aspect of the present application provides a stepwise first-order fuzzy predictive control device, comprising:

[0011] a first obtaining module, configured to obtain a plurality of predictive control components according to preset conditions of control action;

[0012] The second obtaining module is configured to obtain a process output prediction value at the t+N moment corresponding to each of the plurality of prediction control components based on the process output value at the t moment, the control amount output value at the t moment, and the plurality of prediction control components; wherein t and N are positive integers.

[0013] The third obtaining module is configured to obtain a prediction control deviation and a deviation change rate of the process output at the t+N moment corresponding to each of the plurality of prediction control components based on the process output prediction value at the t+N moment corresponding to each of the plurality of prediction control components.

[0014] The determining module is configured to determine the first prediction control component based on the prediction control deviation and the deviation change rate of the process output at the t+N moment corresponding to each of the plurality of prediction control components.

[0015] The control module is configured to control the thermal first-order inertia system of the coal-fired unit based on the first prediction control component.

[0016] The third aspect of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, the method of the first aspect is implemented.

[0017] The fourth aspect of the present application provides a non-transitory computer readable storage medium having a computer program stored thereon, characterized in that the program is executed by a processor to implement the method of the first aspect.

[0018] The stepwise first-order fuzzy predictive control method according to the embodiments of the present application proposes an explicit step change method for future control sequences on the basis of first-order predictive control theory, and then makes a fuzzy decision on the predictive control output to control the first-order inertia system. The present application has good adaptability to the outer loop disturbance and time-varying characteristics of process control, and has obvious effects on tracking and suppressing overshoot in the control process, and optimizes the control process.

[0019] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings.

[0021] Figure 1 FIG. 1 is a flowchart of a stepwise first-order fuzzy predictive control method according to an embodiment of the present application;

[0022] Figure 2A schematic diagram of a fuzzy performance table for a domain;

[0023] Figure 3 A diagram of a ladder type first-order fuzzy predictive control;

[0024] Figure 4 A flowchart of another ladder type first-order fuzzy predictive control method provided by an embodiment of the present application;

[0025] Figure 5 A schematic diagram of a ladder type first-order fuzzy predictive control device provided by an embodiment of the present application;

[0026] Figure 6 A structural block diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] The embodiments of the present application are described in detail below with reference to the accompanying drawings. The same or similar components are denoted by the same or similar reference numerals throughout the drawings. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present application, and should not be understood as limiting the present application.

[0028] The ladder type first-order fuzzy predictive control method and device of the embodiments of the present application are described below with reference to the accompanying drawings.

[0029] Figure 1 A flowchart of a ladder type first-order fuzzy predictive control method provided by an embodiment of the present application. As shown in FIG. 1, the ladder type first-order fuzzy predictive control method includes the following steps: Figure 1

[0030] Step 101, obtaining a plurality of predictive control components according to preset conditions of a control action.

[0031] It should be noted that the predictive control component is a predicted control component of the next time, i.e., the control component u i of the t+1 time (wherein t is the current time). In some embodiments of the present application, the preset conditions of the control action can include an upper limit value of the control action, a lower limit value of the control action, and a maximum value of the allowed change of the control action in each sampling period. According to the aforementioned preset conditions, the plurality of predictive control components are determined. As an example, according to the upper limit value u H of the control action, the lower limit value u L of the control action, and the maximum value δ max of the allowed change of the control action in each sampling period, the selection range [u t，LL , u t，HL ​], wherein the determination process of the selection range can refer to formula (1) and formula (2). Wherein, u (t-1) is the control amount output at the t-1 time.

[0032] u t，LL = max [u (t-1) - δ max , u L ] (1)

[0033] u t，HL = max [u (t-1) + δ max , u H ] (2)

[0034] Further, as formula (3) will select the range [u t，LL , u t，HL ] M equidistribution, thereby obtaining a plurality of prediction control components u i (i=0, 1,..., M). For example, M=10, δ max =10.0.

[0035] u i = u t，LL +i·(u t，HL -u t，LL ) / M (3)

[0036] Step 102, based on the process output value at the t time, the control amount output value at the t time and a plurality of prediction control components, obtain a plurality of prediction control components each corresponding to the process output prediction value at the t+N time. Wherein, t, N are positive integers.

[0037] In some embodiments of the present application, based on the process output value at the t time, the control amount output value at the t time and a plurality of prediction control components, through the first output prediction model, a plurality of prediction control components u i each corresponding to the process output prediction value at the t+N time can be obtained. Wherein, the first output prediction model is expressed by formula (4).

[0038]

[0039] Wherein, p (t) is the process output value at the t time, u (t) is the control amount output value at the t time, u i is the prediction control component, p (t+n) is the process output prediction value corresponding to the prediction control component u i at the t+N time (wherein, n=1, 2,..., P,..., N), α1 and β1 are first-order discrete transfer function process parameters, σ is a display step factor, P is a step change prediction step length, d (t) is the generalized disturbance error at the t time.

[0040] The first output prediction model of formula (4) can be derived as follows:

[0041] Firstly, according to the classical fuzzy predictive control method, a first-order predictive control theory is restructured according to the future disturbance invariable constraint assumption, as follows.

[0042] p(t) = CSP[p(t-1), p(t-2), …, p(t-n), u(t-1), …, u(t-m)] + d(t) (5)

[0043] Wherein, p is the process output value, u is the control output value, t is the discrete sampling time, 1, …, n is the process output model order, 1, …, m is the control action model order, and d is the generalized disturbance error.

[0044] For the thermal first-order inertia system of the coal-fired unit, the discrete process model is modified from formula (5) to formula (6).

[0045] p(t) = a1·p(t-1) + b1·u(t-1) + d(t) (6)

[0046] Wherein, a1 and b1 are the first-order discrete transfer function process parameters, and the calculation formula of the generalized disturbance error d(t) at a certain time is shown in formula (7).

[0047]

[0048] Wherein, p(t-1), p(t-2), …, p(t-n) are the process output measurement values from the t-1th time to the tth time, u(t-1), u(t-2), …, u(t-m) are the control action measurement values from the t-1th time to the tth time. It should be noted that the calculation idea of the generalized disturbance error in formula (7) is the difference between the true measurement value and the process model prediction value. The difference value not only contains the error of the model itself, but also contains other unmeasurable disturbance errors. In the process of predicting the process output, formula (7) can be rewritten as formula (8) due to the future disturbance invariable constraint assumption.

[0049] d(t) = d(t+i), i = 1, 2, …, N (8)

[0050] According to the control output value u(t) at the tth time and the predictive control component u i , the future N-step process output prediction value is expressed by formula (9):

[0051]

[0052] Furthermore, based on the first-order predictive control theory formed by the above formula (9), an explicit step change method is proposed for the future control sequence, which improves the first-order explicit step predictive control method. The future predictive control component is divided into two parts. In the control time domain with a prediction time domain length of P, the control sequence is an explicit step change, as shown in formula (10):

[0053] Δu(t-i+P)=σ i-1 Δu(t), i=1,2,…,P (10)

[0054] Where Δu(t) is the prediction control deviation between two adjacent steps of the future predicted control air volume, σ is the display step factor, and P is the prediction step size of the step change;

[0055] Within the control time domain with a prediction time domain length of P→N, its magnitude is equal to the control quantity at time t+P-1, as shown in formula (11):

[0056] Δu(t-i+N+P+1)=0,i=P+1,…,N (11)

[0057] That is, in the next N steps, the predictive control components are represented as follows:

[0058] The first step is u i ;

[0059] The second step is u i +σ(u i -u(t));

[0060] The third step is u i +(σ+σ 2 (u) i -u(t));

[0061] …

[0062] Step P is u i +(σ+σ 2 +…+σ P-1 (u) i -u(t));

[0063] The (P+1)th step is u i +(σ+σ 2 +…+σ P-1 (u) i -u(t));

[0064] …

[0065] Step N is u i +(σ+σ 2 +…+σ P-1 (u) i -u(t));

[0066] Therefore, formula (9) can be further rewritten as formula (4).

[0067]

[0068] Optionally, σ = 0 to 1.0; P = 1 to 10; N = 32 to 64.

[0069] Step 103: Based on the predicted values ​​of the process output at time t+N corresponding to each of the multiple predictive control components, obtain the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple predictive control components.

[0070] Based on step 102, multiple predictive control components u can be obtained. i The predicted process output value p(t+N) at time t+N for each component is determined. Based on the predicted process output value p(t+N), the corresponding predictive control deviation and deviation rate of change are further determined, which can be used to judge the performance of the control system in actual operation. As an example, the predicted control deviation and deviation rate of change of the process output at time t+N for each predictive control component can be obtained by formulas (12) and (13) based on the predicted process output values ​​at time t+N for each predictive control component.

[0071] pe(t+N)=p0-p(t+N) (12)

[0072] cpe(t+N)=pe(t+N)-pe(t+N-1) (13)

[0073] Where pe(t+N) is the predictive control deviation of the process output at time t+N, cpe(t+N) is the rate of change of the deviation of the process output at time t+N, pe(t+N-1) is the predictive control deviation of the process output at time t+N-1, and p0 is the setpoint of the control process.

[0074] Step 104: Determine the first predictive control component based on the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple predictive control components.

[0075] One possible implementation is to determine the first predictive control component based on the predictive control deviation, deviation rate of change, and universe of discourse fuzzy performance table of the process output at time t+N corresponding to each of the multiple predictive control components. As an example, Figure 2 A schematic diagram of the fuzzy performance table of the universe of discourse (where, Figure 2 The illustrated domain fuzziness performance table is for illustrative purposes only. Figure 2 The fuzzy performance table of the universe of discourse has the following fuzzy query rules:

[0076] if pe(t+N) is A m and cpe(t+N) is B n then J p =λ k (k = 1, ..., 25)

[0077] like Figure 2 As shown, based on the predictive control deviation pe and the rate of change of deviation cpe, the predictive control component u can be found in the fuzzy performance table of the universe of discourse. i The corresponding control performance index J i Control performance index |λ i The smaller the value of |, the better the system control quality. As an example, multiple predictive control components u i (i = 0, 1, ..., M) correspond to multiple control performance indices J i (i = 0, 1, ..., M), determine the control performance index J. i The control performance index J with the smallest absolute value min That is, formula (14). The control performance index J... min The corresponding predictive control component is used as the first predictive control component.

[0078]

[0079] Step 105: Control the first-order thermal inertial system of the coal-fired unit according to the first predictive control component.

[0080] Optionally, in some embodiments of this application, the first predictive control component can be used as the control output to control the first-order thermal inertial system of the coal-fired power unit. The first predictive control component can also be processed to further determine the control output, thereby controlling the first-order thermal inertial system of the coal-fired power unit. Taking the main steam pressure control system of a 1000MW ultra-supercritical unit as an example, the main steam pressure is controlled according to the step-type first-order fuzzy predictive control method proposed in the embodiments of this application. Figure 3 The diagram shows the effect of a step-by-step first-order fuzzy predictive control, such as... Figure 3 As shown, the control output predicted by the step-first-order fuzzy predictive control method proposed in this application is used to control the main steam pressure, which can make it have a significant tracking and overshoot suppression effect and more smoothly approach the set value.

[0081] According to the step-type first-order fuzzy predictive control method of the present application embodiment, based on the first-order predictive control theory, an explicit step change method is proposed for the future control sequence, and then fuzzy decision is made on the predictive control output to control the first-order inertial system. The present application can have good adaptability to the outer-loop disturbance and time-varying characteristics of process control, and the control process has a significant effect on tracking and suppressing overshoot, thus optimizing the control process.

[0082] Figure 4 This is a flowchart illustrating another step-by-step first-order fuzzy predictive control method provided in an embodiment of this application. Figure 4 As shown, the step-by-step first-order fuzzy predictive control method includes the following steps:

[0083] Step 401: Determine the selection range of the predictive control component based on the preset conditions of the control action. The preset conditions for the control action include the upper limit of the control action, the lower limit of the control action, and the maximum allowable variation of the control action in each sampling period.

[0084] Step 402: Divide the selected range into equal parts to obtain multiple predictive control components.

[0085] Step 403: Based on the process output value at time t, the control output value at time t, and multiple predictive control components, obtain the process output prediction values ​​at time t+N corresponding to each of the multiple predictive control components through the first output prediction model.

[0086] Step 404: Based on the predicted values ​​of the process output at time t+N corresponding to each of the multiple predictive control components, obtain the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple predictive control components.

[0087] Step 405: Based on the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple predictive control components, determine the control performance index corresponding to each of the multiple predictive control components.

[0088] Step 406: Among the control performance indices corresponding to the multiple predictive control components, determine the minimum control performance index and determine the predictive control component corresponding to the minimum control performance index as the first predictive control component.

[0089] Step 407: Determine the second and third predictive control components that are adjacent to the first predictive control component.

[0090] For example, assuming the first predictive control component is u5, then the second predictive control component adjacent to the first predictive control component u5 is u4, and the third predictive control component adjacent to the first predictive control component u5 is u6.

[0091] Step 408: Obtain the optimal control component based on the first control performance index corresponding to the first predictive control component, the second control performance index corresponding to the second predictive control component, and the third control performance index corresponding to the third predictive control component.

[0092] As one possible implementation, the optimal control component can be determined based on the positive and negative signs of the first control performance index, the second control performance index, and the third control performance index.

[0093] For example, when the first control performance index and the second control performance index have opposite signs, and the first control performance index and the third control performance index have the same sign, the optimal control component u is determined by formula (15). op .

[0094]

[0095] Among them, u L As the first predictive control component, u L-1 J is the second predictive control component. L J is the primary control performance indicator. L-1 This is the second control performance indicator;

[0096] When the first control performance index and the second control performance index have the same sign, and the first control performance index and the third control performance index have opposite signs, the optimal control component u is determined by formula (16). op .

[0097]

[0098] Among them, u L As the first predictive control component, u L+1 J is the third predictive control component. L J is the primary control performance indicator. L+1 This is the third control performance indicator;

[0099] When the first control performance index and the second control performance index have the same sign, and the first control performance index and the third control performance index have the same sign, the optimal control component u is determined by formula (17). op .

[0100] u op =u L (17)

[0101] Among them, u L This is the first predictive control component.

[0102] Step 409: Determine the predictive control output based on the optimal control component.

[0103] As an example, based on the optimal control component uop The predictive control output u can be determined using formula (18). out .

[0104] u out =u(t)+λ·(u op -u(t)) (18)

[0105] Where u(t) is the control output value at time t, and λ is the iterative recursion factor. Optionally, λ = 0.00025.

[0106] Step 410: Control the first-order thermal inertial system of the coal-fired unit based on the predictive control output.

[0107] In the embodiments of this application, steps 401-405 can be implemented in any of the embodiments of this application. This application does not make specific limitations on this and will not elaborate further.

[0108] The step-type first-order fuzzy predictive control method according to embodiments of this application proposes an explicit step-change method for the future control sequence based on first-order predictive control theory. Then, fuzzy decision-making is performed on the predictive control output. Based on a first control performance index, a second control performance index, and a third control performance index, the predictive control output is determined to control the first-order inertial system. This application exhibits good adaptability to external loop disturbances and time-varying characteristics of process control, and the control process demonstrates a significant effect in tracking and suppressing overshoot, further optimizing the control process.

[0109] Figure 5 This is a schematic diagram of a stepped first-order fuzzy predictive control device provided in an embodiment of this application. Figure 5 As shown, the stepped first-order fuzzy prediction control device includes: a first acquisition module 501, a second acquisition module 502, a third acquisition module 503, a determination module 504, and a control module 505. Among them,

[0110] The first acquisition module 501 is used to obtain multiple predictive control components based on preset conditions of the control action.

[0111] In some embodiments of this application, the preset conditions for the control action include: an upper limit value for the control action, a lower limit value for the control action, and a maximum allowable change value for the control action in each sampling period. The first acquisition module 501 is specifically used to: determine the selection range of the predicted control components according to the preset conditions for the control action; and divide the selection range into equal parts to obtain the plurality of predicted control components.

[0112] The second acquisition module 502 is used to obtain the predicted process output values ​​at time t+N corresponding to each of the multiple predictive control components based on the process output value at time t, the control output value at time t, and the multiple predictive control components. Here, t and N are positive integers.

[0113] In some embodiments of this application, the second acquisition module 502 is specifically used to: based on the process output value at time t, the control output value at time t, and the plurality of predictive control components, obtain the process output prediction value at time t+N corresponding to each of the plurality of predictive control components through a first output prediction model; wherein, the first output prediction model is expressed by the following formula.

[0114]

[0115] Where p(t) is the process output value at time t, u(t) is the control output value at time t, and u i Let p(t+n) be the predictive control component, and u be the predictive control component. i The corresponding process output prediction value at time t+N (where n = 1, 2, ..., P, ..., N), α1 and β1 are the process parameters of the first-order discrete transfer function, σ is the explicit step factor, P is the step change prediction step size, and d(t) is the generalized disturbance error at time t.

[0116] The third acquisition module 503 is used to obtain the prediction control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple prediction control components based on the predicted values ​​of the process output at time t+N corresponding to each of the multiple prediction control components.

[0117] In some embodiments of this application, the third acquisition module 503 obtains the prediction control deviation and deviation change rate of the process output at time t+N corresponding to each of the plurality of prediction control components according to the predicted value of the process output at time t+N corresponding to each of the plurality of prediction control components through the following formula.

[0118] pe(t+N)=p0-p(t+N)

[0119] cpe(t+N)=pe(t+N)-pe(t+N-1)

[0120] Where pe(t+N) is the predicted control deviation of the process output at time t+N, cpe(t+N) is the rate of change of the deviation of the process output at time t+N, pe(t+N-1) is the predicted control deviation of the process output at time t+N-1, and p0 is the setpoint of the control process.

[0121] The determination module 504 is used to determine the first predictive control component based on the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple predictive control components.

[0122] In some embodiments of this application, the determining module 504 is specifically used to: determine the control performance index corresponding to each of the multiple predictive control components based on the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple predictive control components; determine the minimum control performance index among the control performance indices corresponding to each of the multiple predictive control components, and determine the predictive control component corresponding to the minimum control performance index as the first predictive control component.

[0123] The control module 505 is used to control the first-order thermal inertial system of the coal-fired unit according to the first predictive control component.

[0124] In some embodiments of this application, the control module 505 is specifically configured to: determine a second predictive control component and a third predictive control component adjacent to the first predictive control component; obtain an optimal control component based on a first control performance index corresponding to the first predictive control component, a second control performance index corresponding to the second predictive control component, and a third control performance index corresponding to the third predictive control component; determine a predictive control output based on the optimal control component; and control the first-order thermal inertial system of the coal-fired power unit based on the predictive control output.

[0125] In some embodiments of this application, the control module 505 is further configured to: in response to the first control performance index and the second control performance index having opposite signs, and the first control performance index and the third control performance index having the same sign, determine the optimal control component u using the following formula. op :

[0126]

[0127] Among them, u L For the first predictive control component, u L-1 J is the second predictive control component. L J is the first control performance index. L-1 This refers to the second control performance indicator;

[0128] Alternatively, in response to the first control performance index having the same sign as the second control performance index, and the first control performance index having the opposite sign as the third control performance index, the optimal control component u is determined using the following formula. op :

[0129]

[0130] Among them, u L For the first predictive control component, u L+1 For the third predictive control component, J L J is the first control performance index. L+1 This refers to the third control performance index;

[0131] Alternatively, in response to the first control performance index having the same sign as the second control performance index, and the first control performance index having the same sign as the third control performance index, the optimal control component u is determined by the following formula. op :

[0132] u op =u L

[0133] Among them, u L This is the first predictive control component.

[0134] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0135] According to the embodiments of this application, the stepped first-order fuzzy predictive control device proposes an explicit stepped change method for the future control sequence based on the first-order predictive control theory, and then makes fuzzy decisions on the predictive control output to control the first-order inertial system. This application can adapt well to the outer-loop disturbances and time-varying characteristics of process control, and the control process has a significant effect on tracking and suppressing overshoot, thus optimizing the control process.

[0136] To implement the above embodiments, this application also provides a computer device. Figure 6 This is a structural block diagram of a computer device provided in an embodiment of this application. Figure 6 As shown, the computer device 600 may include a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and executable on the processor 602. When the processor 602 executes the computer program 603, it executes the step-by-step first-order fuzzy prediction control method described in any of the above embodiments of this application.

[0137] To implement the above embodiments, this application also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the step-by-step first-order fuzzy predictive control method described in any of the above embodiments of this application.

[0138] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0139] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0140] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0141] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0142] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0143] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0144] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0145] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A step-by-step first-order fuzzy predictive control method, characterized in that, Includes the following steps: Based on the preset conditions of the control action, multiple predictive control components are obtained; Based on the process output value at time t, the control output value at time t, and the plurality of predictive control components, the process output prediction value at time t+N corresponding to each of the plurality of predictive control components is obtained; where t and N are positive integers. Based on the predicted values ​​of the process output at time t+N corresponding to each of the multiple predictive control components, the predictive control deviation and the rate of change of deviation of the process output at time t+N corresponding to each of the multiple predictive control components are obtained. Based on the prediction control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple prediction control components, the first prediction control component is determined. The first-order inertial thermal system of the coal-fired unit is controlled based on the first predictive control component.

2. The method according to claim 1, characterized in that, The preset conditions for the control action include: an upper limit for the control action, a lower limit for the control action, and the maximum allowable change of the control action in each sampling period; obtaining multiple predictive control components based on the preset conditions for the control action includes: The selection range of the predictive control component is determined based on the preset conditions of the control action; The selection range is divided into equal parts to obtain the plurality of predictive control components.

3. The method according to claim 1, characterized in that, The process output prediction value at time t+N corresponding to each of the multiple predictive control components is obtained based on the process output value at time t, the control output value at time t, and the multiple predictive control components, including: Based on the process output value at time t, the control output value at time t, and the plurality of predictive control components, the process output prediction values ​​at time t+N corresponding to each of the plurality of predictive control components are obtained through a first output prediction model; wherein, the first output prediction model is expressed by the following formula: in, The output value for the process at time t is... The output value of the control quantity at time t is... For the predictive control component, For the predictive control component The corresponding predicted output value at time t+N, where n=1,2,…,P,…N, and These are the process parameters of a first-order discrete transfer function. To display the step factor, P is the step size for predicting step changes. Let be the generalized perturbation error at time t.

4. The method according to claim 1, characterized in that, Based on the predicted process output values ​​at time t+N corresponding to each of the multiple predictive control components, the predictive control deviation and deviation rate of change of the process output at time t+N corresponding to each of the multiple predictive control components are obtained using the following formula: in, The predictive control deviation output at time t+N is the process output at that time. The deviation change rate of the process output at time t+N. The predictive control deviation output at time t+N-1 is the process output. The setpoint for the control process.

5. The method according to claim 1, characterized in that, The determination of the first predictive control component based on the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the plurality of predictive control components includes: Based on the predicted control deviation and deviation change rate of the process output at time t+N corresponding to the multiple predicted control components, the control performance index corresponding to each of the multiple predicted control components is determined. Among the control performance indices corresponding to the plurality of predictive control components, the minimum control performance index is determined, and the predictive control component corresponding to the minimum control performance index is determined as the first predictive control component.

6. The method according to claim 5, characterized in that, The step of controlling the first-order thermal inertial system of the coal-fired unit based on the first predictive control component includes: Determine the second and third predictive control components that are adjacent to the first predictive control component; Based on the first control performance index corresponding to the first predictive control component, the second control performance index corresponding to the second predictive control component, and the third control performance index corresponding to the third predictive control component, the optimal control component is obtained. Based on the optimal control components, determine the predictive control output; Based on the predicted control output, the first-order thermal inertial system of the coal-fired unit is controlled.

7. The method according to claim 6, characterized in that, The step of obtaining the optimal control component based on the first control performance index corresponding to the first predicted control component, the second control performance index corresponding to the second predicted control component, and the third control performance index corresponding to the third predicted control component includes: In response to the first control performance index and the second control performance index having opposite signs, and the first control performance index and the third control performance index having the same sign, the optimal control component is determined by the following formula. : in, This is the first predictive control component. This is the second predictive control component. This is the first control performance index. This refers to the second control performance indicator; Alternatively, in response to the first control performance index having the same sign as the second control performance index, and the first control performance index having the opposite sign as the third control performance index, the optimal control component is determined using the following formula. : in, This is the first predictive control component. The third predictive control component, This is the first control performance index. This refers to the third control performance index; Alternatively, in response to the first control performance index having the same sign as the second control performance index, and the first control performance index having the same sign as the third control performance index, the optimal control component is determined using the following formula. : in, This is the first predictive control component.

8. A stepped first-order fuzzy predictive control device, characterized in that, include: The first acquisition module is used to obtain multiple predictive control components based on the preset conditions of the control action. The second acquisition module is used to obtain the process output prediction value at time t+N corresponding to each of the multiple predictive control components based on the process output value at time t, the control output value at time t, and the multiple predictive control components; where t and N are positive integers. The third acquisition module is used to obtain the prediction control deviation and deviation change rate of the process output at time t+N corresponding to each of the multiple prediction control components based on the predicted value of the process output at time t+N corresponding to each of the multiple prediction control components. The determination module is used to determine the first predictive control component based on the predictive control deviation and deviation change rate of the process output at time t+N corresponding to each of the plurality of predictive control components. The control module is used to control the first-order thermal inertial system of the coal-fired unit according to the first predicted control component.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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