Elastic prediction control method and device based on interval target self-optimization

Abstract

The invention relates to an elastic prediction control method and device based on interval target self-optimization. The method comprises the steps that process output at the current moment and a process output sequence in a past preset time period are acquired; determining a process stability index for representing process stability according to the process output sequence; judging whether the process stability index is smaller than an index threshold value or not; if it is determined that the process stability index is not smaller than the index threshold value, a first objective function of a set value control strategy is solved; if it is determined that the process stability index is smaller than the index threshold value, whether process output at the current moment is within the range of the target interval or not is further judged; if it is determined that the process output at the current moment is within the range of the target interval, solving a second target function of the reference trajectory self-optimization strategy; and if it is determined that the process output at the current moment is out of the range of the target interval, the first target function is solved, so that the system robustness and the device operation benefit are both considered.

Description

Technical Field

[0001] The present invention relates to the field of advanced control technology, and in particular to a method and device for elastic predictive control based on interval target self-optimization. Background Art

[0002] In the production process of process industry enterprises, there are many uncertain factors, such as fluctuations in raw material composition, switching of production conditions, changes in catalyst activity, chemical exothermic reactions, and seasonal temperature changes, which make it very difficult to ensure the long-term stability of the production process. In addition, the fluctuations of key variables in the upstream process section are often transmitted to the downstream production process section with the flow of production materials, making it impossible to ensure the stability of the production of the entire plant.

[0003] At present, the known methods to improve control stability can be roughly divided into the following categories: The first type, from the perspective of process control, can introduce disturbance variables as a feedforward control method to compensate and reduce production fluctuations. However, this type of method cannot solve the negative impact of unmeasurable disturbances. For unmeasurable disturbances, state observation methods can also be used for estimation, but the estimation accuracy is often not ideal. This is because the design of the state observer generally makes certain assumptions about the data itself, such as noise conforming to Gaussian distribution, etc. The second type is to use more robust control methods to compensate for external disturbances, such as various methods mentioned in robust control theory books. Generally, the improvement of control performance robustness is based on the premise of sacrificing control accuracy. The third method, from a process perspective, blocks the continuous transmission of production fluctuations through equipment modification, such as introducing buffer tanks, but this type of technology often adopts a single control strategy and ignores the optimal operation efficiency of the device. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method and device for elastic predictive control based on interval target self-optimization, which solves the technical problems of low estimation accuracy and poor robustness existing in the prior art.

[0005] (II) Technical solution In order to achieve the above object, the main technical solutions adopted by the present invention include: In the first aspect, an embodiment of the present invention provides a flexible predictive control method based on interval target self-optimization, including: obtaining the process output at the current moment and the process output sequence within a preset time period in the past; determining a process stability index used to represent the process stability according to the process output sequence; judging whether the process stability index is less than an index threshold; if it is determined that the process stability index is not less than the index threshold, solving the first objective function of the set value control strategy to obtain a first optimal control sequence; if it is determined that the process stability index is less than the index threshold, further judging whether the process output at the current moment is within the range of the target interval; if it is determined that the process output at the current moment is within the range of the target interval, solving the second objective function of the reference trajectory self-optimization strategy to obtain a second optimal control sequence; if it is determined that the process output at the current moment is outside the range of the target interval, solving the first objective function to obtain a first optimal control sequence.

[0006] In a possible embodiment, a process stationarity index for representing process stationarity is determined based on a process output sequence, including: calculating a standard deviation of the process output sequence based on an average value of the process output sequence; calculating a deviation between an average value of the process output sequence and a set value; calculating a frequency of the process output sequence exceeding a preset interval; and performing a weighted summation of the standard deviation, the deviation, and the frequency to obtain a process stationarity index.

[0007] In a possible embodiment, the expression of the first objective function is as follows: ; Where, J1 represents the first objective function; represents the output variable predicted by future input; Represents the process output at the current moment The reference trajectory of Q 1 and Q 2 is the corresponding diagonal matrix of weight coefficients; represents the first optimal control sequence.

[0008] In a possible embodiment, the expression of the second objective function is as follows: ; ; ; ; Where, J2 represents the second objective function; represents the process input increment; T Represented as the transposed symbol of a matrix or vector; Represents the reference trajectory of the self-optimization process output; Q 1 andQ 2 represents the corresponding diagonal matrix of weight coefficients; Indicates the current time k The process status; and Respectively The upper and lower limit constraints; Y l and Y u Respectively The upper and lower limit constraints; C represents the output matrix of the state-space model; A The state transition matrix representing the state space model; B represents the input matrix of the state-space model; H p To predict the time domain length; H c To control the time domain length.

[0009] In the second aspect, an embodiment of the present invention provides an elastic predictive control device based on interval target self-optimization, including: an acquisition module, used to acquire the process output at the current moment and the process output sequence within a preset time period in the past; a determination module, used to determine a process stability index used to represent the process stability according to the process output sequence; a judgment module, used to judge whether the process stability index is less than the index threshold; a first solution module, used to solve the first objective function of the set value control strategy to obtain a first optimal control sequence if it is determined that the process stability index is not less than the index threshold; a second solution module, used to further judge whether the process output at the current moment is within the range of the target interval if it is determined that the process stability index is less than the index threshold; if it is determined that the process output at the current moment is within the range of the target interval, the second objective function of the reference trajectory self-optimization strategy is solved to obtain a second optimal control sequence; if it is determined that the process output at the current moment is outside the range of the target interval, the first objective function is solved to obtain the first optimal control sequence.

[0010] In a possible embodiment, the determination module is specifically used to: calculate the standard deviation of the process output sequence based on the average value of the process output sequence; calculate the deviation between the average value of the process output sequence and a set value; calculate the frequency of the process output sequence exceeding a preset interval; and perform weighted summation of the standard deviation, deviation and frequency to obtain a process stability index.

[0011] In a possible embodiment, the expression of the first objective function is as follows: ; Where, J1 represents the first objective function; represents the output variable predicted by future input; Represents the process output at the current moment The reference trajectory of Q 1 and Q 2 is the corresponding diagonal matrix of weight coefficients; represents the first optimal control sequence.

[0012] In a possible embodiment, the expression of the second objective function is as follows: ; ; ; ; Where, J2 represents the second objective function; represents the process input increment; T Represented as the transposed symbol of a matrix or vector; Represents the reference trajectory of the self-optimization process output; Q 1 and Q 2 represents the corresponding diagonal matrix of weight coefficients; Indicates the current time k The process status; and Respectively The upper and lower limit constraints; Y l and Y u Respectively The upper and lower limit constraints; C represents the output matrix of the state-space model; A The state transition matrix representing the state space model; B represents the input matrix of the state-space model; H p To predict the time domain length; H c To control the time domain length.

[0013] In a third aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to execute the method described in the first aspect or any optional implementation of the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the method described in the first aspect or any optional implementation method of the first aspect is performed.

[0015] In a fifth aspect, the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0016] (III) Beneficial effects The beneficial effects of the present invention are: The embodiment of the present application provides a flexible predictive control method and device based on interval target self-optimization, which automatically selects set value control (i.e., corresponding set value control strategy) and interval control (i.e., corresponding reference trajectory self-optimization strategy and set value control strategy) based on the flexible control strategy, and adopts a self-optimizable reference trajectory as the tracking target during interval control of the controlled variable, taking into account both system robustness and device operation efficiency.

[0017] In order to make the above-mentioned objectives, features and advantages to be achieved by the embodiments of the present application more obvious and understandable, the following specifically cites preferred embodiments and describes them in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flowchart of an elastic predictive control method based on interval target self-optimization provided by an embodiment of the present application is shown; Figure 2 A schematic structural diagram of an elastic predictive control device based on interval target self-optimization provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0021] In order to address the shortcomings of the prior art, this application proposes an elastic predictive control method and device based on interval target self-optimization from the perspective of process control strategy optimization, so that it can better absorb process instability caused by disturbances within a controllable range and block the transmission of disturbances to a certain extent; at the same time, it takes into account the optimal efficiency of the device.

[0022] And, this can be described in two aspects: First, because process industry production is highly complex, the upstream and downstream sections are generally highly correlated, and production fluctuations are easily transmitted step by step, so for some non-critical control variables, flexible and variable control strategies should be formulated to automatically select between the pursuit of precise set value control and robust interval control mode; secondly, in the interval control mode, the reference trajectory of the controlled variable should also be automatically optimized according to the device benefits, so as to ensure the robustness of the system while taking into account the optimal device benefits.

[0023] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0024] In order to facilitate understanding of the embodiments of the present application, the terms involved in the present application are explained as follows: "Predictive control": also known as model predictive control, it is an advanced control method. It predicts future behavior based on a system model and determines the best control strategy to meet specific performance indicators through optimization algorithms; “Reference trajectory”: In predictive control, the reference trajectory refers to the ideal sequence of target values ​​that the system output is expected to follow, which is used to guide the system to dynamically reach the predetermined value, ensuring that the actual output is as close to the ideal trajectory as possible; “Rolling Optimization”: It means that predictive control usually recalculates the optimal control sequence at each sampling moment based on the difference between the predicted system output and the reference trajectory to minimize the weighted cumulative sum of the deviations; "Setpoint control": It refers to a control mode in which the control system adjusts its output according to a preset target value (setpoint) so that the state of the controlled object is stable and maintained near the target value; "Interval control": relative to set value control, it means that the controller stabilizes the output of the controlled object according to the preset target interval and keeps it within the target interval.

[0025] See also Figure 1 , Figure 1 A flowchart of an elastic prediction control method based on interval target self-optimization provided by an embodiment of the present application is shown. It should be understood that the elastic prediction control method can be executed by an elastic prediction control device based on interval target self-optimization, and the specific device of the elastic prediction control device can be set according to actual needs, and the embodiment of the present application is not limited thereto. For example, the elastic prediction control device can be a controller, etc. Specifically, the elastic prediction control method includes: Step S110, obtaining the process output at the current moment and the process output sequence within a preset time period in the past.

[0026] Step S120: determining a process stationarity index for indicating process stationarity according to the process output sequence.

[0027] It should be understood that, based on the process output sequence, the specific process of determining the process stationarity index used to represent the process stationarity can be set according to actual needs, and the embodiments of the present application are not limited thereto.

[0028] Optionally, the standard deviation of the process output sequence is calculated based on the average value of the process output sequence; the deviation between the average value of the process output sequence and a set value is calculated; the frequency of the process output sequence exceeding a preset interval is calculated; and the standard deviation, deviation and frequency are weightedly summed to obtain a process stability index.

[0029] For example, a very important part of the elastic control strategy of this application is the evaluation of system stability. The process stability evaluation proposed in this application is divided into the following three dimensions: the standard deviation of the fluctuation of the process output itself, the deviation between the mean of the process output and the set value, and the alarm frequency of the process output exceeding the preset interval. Specifically: Get the process output sequence within a preset time period in the past , and calculate the average value of the process output sequence within the past preset time period , and based on this average Calculate the standard deviation Std( Y p ),Right now The specific time period of the preset time period and the specific value of N can be set according to actual needs, and the embodiment of the present application is not limited thereto; And, the average value is calculated by the following formula and set value y sp The deviation between Y p ), specifically: ; And, the frequency of the process output sequence exceeding the preset interval is calculated by the following formula: Alarm( Y p ), specifically: ; ; In the formula, and are the upper and lower limits of the alarm interval; Furthermore, based on the above three stability evaluation dimensions, this application can comprehensively calculate the current moment k The process stability index Ind s ( k ), specifically: Ind s ( k )= w 1×Std( Y p )+ w 2×Bias( Y p )+ w 3×Alarm( Y p ); In the formula, w 1 is the standard deviation Std( Y p ) corresponding weight; w 2 is the bias ( Y p ) corresponding weight; w 3 is the frequency alarm ( Y p ) corresponding weight.

[0030] Step S130, determine whether the process stability index is less than the index threshold. The index threshold can be T h To represent, and the indicator threshold T h The specific value of can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0031] Step S140: If it is determined that the process stability index is not less than the index threshold, the first objective function of the set value control strategy is solved to obtain a first optimal control sequence.

[0032] It should be understood that the specific function of the first objective function can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0033] Optionally, the control object of the present application is described as follows: As mentioned above, this application considers a production process control problem. Here, we first use mathematical language to describe this type of process. In the process industry, common process objects, such as flow, liquid level, pressure and temperature, can generally be described by the first-order plus time delay model (FOPDT), and its mathematical form is as follows: (1); In the formula, G(s) is the transfer function of the process object; Kp is gain; e is a natural constant; is time lag; s is the Laplace operator; T p is the time constant.

[0034] And, to facilitate the description of the predictive control method, formula (1) is discretized and converted into a state space model: (2); ; In the formula, x ( k+ 1) k+ The process status at time 1; A The state transition matrix representing this state space model; x ( k ) is the current time k The process status; B represents the input matrix of this state-space model; For the current moment k process input; y ( k ) is the current time k The process output; C represents the output matrix of this state-space model; for k- The process input at time 1. The state space model refers to formula (2).

[0035] In addition, the relevant description of the output prediction of this application is as follows: For the future H p The state prediction of the next step, the equation for this future input is: (3); ; ; ; ; In the formula, H p To predict the time domain length, H c To control the time domain length, and H p and H c are all positive integers and generally satisfy H c <H p .

[0036] Furthermore, the following represents the output variable predicted by future input, denoted as , specifically: (4); ; ; ; In the formula, CA Represents the product value of the output matrix C and the state transfer matrix A, Represents the output matrix C and the state transfer matrix A H p The product value of the power.

[0037] In addition, the relevant description of the set value control strategy of this application is as follows: The first optimal control sequence can be obtained by solving the following optimization problem. Here, for simplicity, the reference trajectory of the process input is not considered. Specifically: (5); ; Where, J1 represents the first objective function; Indicates at time k (which can be regarded as the current moment) process output The reference trajectory of T is the transpose symbol of a matrix or vector; Q 1 and Q 2 is the corresponding diagonal matrix of weight coefficients, which is generally related to the benefits of the device such as energy consumption and product quality; to Separately for the moment k +1 for process output The reference trajectory to time k + H p The process output reference trajectories; at time k, these reference trajectories form the future H p The process outputs the reference trajectory vector .

[0038] Furthermore, if we define the operation , then the expression of the first objective function J1 is: (6); Expand , and omit the constantk After that, you can get , written as a quadratic programming (QP) problem in standard form: (7);

[0039] By solving this QP problem, we can get the first optimal control sequence: , that is, the optimal set value control sequence.

[0040] It should be noted here that the " st The full name of " subject to, It represents a commonly used symbol in solving optimization problems, or it can be understood as representing a constraint condition. st " are similar and will not be described one by one. For details, please refer to the relevant description in this paragraph.

[0041] It should be noted here that the solution method of the QP problem can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0042] Step S150, if it is determined that the process stability index is less than the index threshold, then further determine whether the process output at the current moment is within the range of the target interval; if it is determined that the process output at the current moment is within the range of the target interval, then solve the second objective function of the reference trajectory self-optimization strategy to obtain the second optimal control sequence; if it is determined that the process output at the current moment is outside the range of the target interval, then solve the first objective function to obtain the first optimal control sequence. Among them, the specific value of the index threshold and the specific range of the target interval can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0043] Specifically, if the process stability index Ind is determined s ( k ) is less than the indicator threshold T h , then further determine the process output at the current moment y ( k ) is within the target range If the process output at the current moment is determined y ( k ) is in the target range Within the range of , the second objective function of the reference trajectory self-optimization strategy (i.e., formula (11)) is solved to obtain the second optimal control sequence; if the process output at the current moment is determined y ( k ) is in the target range If it is outside the range of , the first objective function (i.e., formula (6)) is solved to obtain the first optimal control sequence.

[0044] In other words, the proposed flexible control strategy is compared with the fixed control strategy. In simple terms, when the process is stable, it automatically adopts the set value control to achieve precise control of the controlled output. When the system fluctuates, it automatically switches to the interval control strategy described in the following formula (9), and realizes the reference trajectory self-optimization control within the interval.

[0045] It should be understood that the specific function of the second objective function can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0046] Optionally, the relevant description of the interval control and reference trajectory self-optimization strategy of the present application is as follows: In setpoint control, the control output is compared with the reference trajectory of the control output for penalty. As punishment. It is calculated in advance. Interval control, relative to set value control, means that the output of the controlled object is stabilized and maintained within the preset target interval. At this time, the control output can be obtained by the following optimization problem: (8); In the formula, Represents the process output at the current moment y ( k ), and is the lower bound of the target interval, is the upper bound of the target interval.

[0047] Furthermore, the reference trajectory self-optimization proposed in this application refers to the problem of the above formula (8) under the interval control strategy. In this case, the control based on the reference trajectory is still adopted, but It is no longer specified in advance, but is automatically calculated by solving the optimization problem. To distinguish the reference trajectory expression form in the set value control, To indicate that the superscript It means automatic, which is the first letter of the English word auto. Specifically: (9); Expand and omit the constant k , then we can deduce: (10); Where is a simple statement, the process input increment , process output , , and x ( k ) etc. Based on the above formula (10), the control problem can be written as the standard form of the quadratic programming (QP) problem, specifically: (11); ; in, I represents the identity matrix with a diagonal of 1; the constraint part takes into account the process input increment The upper and lower limit constraints , and the process output The upper and lower limit constraints .

[0048] Therefore, compared with the prior art, the present application is based on a flexible control strategy, automatically selects set value control (i.e., corresponding set value control strategy) and interval control (i.e., corresponding reference trajectory self-optimization strategy and set value control strategy), and adopts a self-optimizable reference trajectory as the tracking target during the controlled variable interval control, taking into account both system robustness and device operation efficiency. Its main advantages are: According to the process stability index, it can automatically select the set value control mode for the purpose of precision and the interval control mode for the purpose of robustness, and more flexibly take into account the local and global production process stability; In addition, in the interval control mode, it can automatically generate the optimal reference trajectory sequence as the control target, which can better improve the stability and robustness of the production process of process enterprises. At the same time, in the stable production process, it can also track the set value and achieve precise control; In addition, the self-optimization of the reference trajectory within the interval control can timely predict and prevent the process output from exceeding the interval range in advance, compared with the method of not penalizing the process output within the interval, so it has better dynamic performance.

[0049] It should be understood that the above elastic predictive control method based on interval target self-optimization is only exemplary, and those skilled in the art can make various modifications based on the above method, and the modified scheme also belongs to the protection scope of this application.

[0050] See also Figure 2 , Figure 2The structural block diagram of an elastic prediction control device 200 based on interval target self-optimization provided by an embodiment of the present application is shown. It should be understood that the elastic prediction control device 200 is capable of executing each step in the above method embodiment. The specific functions of the elastic prediction control device 200 can be found in the description above. To avoid repetition, the detailed description is appropriately omitted here. The elastic prediction control device 200 includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the elastic prediction control device 200. Specifically, the elastic prediction control device 200 includes: An acquisition module 210 is used to acquire the process output at the current moment and the process output sequence within a preset time period in the past; A determination module 220, for determining a process stability index for indicating process stability according to the process output sequence; A judgment module 230 is used to judge whether the process stability index is less than the index threshold; A first solving module 240 is used to solve a first objective function of the set value control strategy to obtain a first optimal control sequence if it is determined that the process stability index is not less than the index threshold; The second solving module 250 is used to further determine whether the process output at the current moment is within the range of the target interval if it is determined that the process stability index is less than the index threshold; if it is determined that the process output at the current moment is within the range of the target interval, the second objective function of the reference trajectory self-optimization strategy is solved to obtain the second optimal control sequence; if it is determined that the process output at the current moment is outside the range of the target interval, the first objective function is solved to obtain the first optimal control sequence.

[0051] In a possible embodiment, the determination module 220 is specifically used to: calculate the standard deviation of the process output sequence based on the average value of the process output sequence; calculate the deviation between the average value of the process output sequence and a set value; calculate the frequency of the process output sequence exceeding a preset interval; and perform weighted summation of the standard deviation, deviation and frequency to obtain a process stability index.

[0052] In a possible embodiment, the expression of the first objective function is as follows: ; Where, J1 represents the first objective function; represents the output variable predicted by future input; Represents the process output at the current moment The reference trajectory of Q 1 and Q 2 is the corresponding diagonal matrix of weight coefficients; represents the first optimal control sequence.

[0053] In a possible embodiment, the expression of the second objective function is as follows: ; ; ; ; Where, J2 represents the second objective function; represents the process input increment; T Represented as the transposed symbol of a matrix or vector; Represents the reference trajectory of the self-optimization process output; Q 1 and Q 2 represents the corresponding diagonal matrix of weight coefficients; Indicates the current time k The process status; and Respectively The upper and lower limit constraints; Y l and Y u Respectively The upper and lower limit constraints; C represents the output matrix of the state-space model; A The state transition matrix representing the state space model; B represents the input matrix of the state-space model; H p To predict the time domain length; H c To control the time domain length.

[0054] Since the device described in the above embodiment of the present invention is a device used to implement the method of the above embodiment of the present invention, based on the method described in the above embodiment of the present invention, a person skilled in the art can understand the specific structure and deformation of the device, so it is not described here. All devices used in the method of the above embodiment of the present invention belong to the scope of protection of the present invention.

[0055] The present application provides a storage medium having a computer program stored thereon. The computer program is executed by a processor to execute the method described in the embodiment.

[0056] The present application also provides a computer program product, which, when executed on a computer, enables the computer to execute the method described in the method embodiment.

[0057] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0059] It should be noted that the word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising a number of different components and by means of a suitably programmed computer. Among the several means listed, several of these means may be embodied by the same hardware. The use of the words first, second, third, etc., is for convenience of expression only and does not indicate any order. These words may be understood as part of the name of the component.

[0060] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0061] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the technical solution should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0062] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the technical solution of the present invention and its equivalent technology, the present invention should also include these modifications and variations.

Claims

1. A flexible predictive control method based on interval target self-optimization, characterized in that: include: Get the process output at the current moment and the process output sequence within the past preset time period; Determining a process stationarity index for indicating process stationarity according to the process output sequence; Determining whether the process stability index is less than an index threshold; If it is determined that the process stability index is not less than the index threshold, solving the first objective function of the set value control strategy to obtain a first optimal control sequence; If it is determined that the process stability index is less than the index threshold, it is further determined whether the process output at the current moment is within the range of the target interval; if it is determined that the process output at the current moment is within the range of the target interval, the second objective function of the reference trajectory self-optimization strategy is solved to obtain the second optimal control sequence; if it is determined that the process output at the current moment is outside the range of the target interval, the first objective function is solved to obtain the first optimal control sequence.

2. The elastic prediction control method according to claim 1, characterized in that: Determining a process stationarity index for indicating process stationarity according to the process output sequence includes: Calculating the standard deviation of the process output sequence based on the average value of the process output sequence; Calculating the deviation between the mean value of the process output sequence and the set value; Calculating the frequency of the process output sequence exceeding a preset interval; The standard deviation, the deviation and the frequency are weightedly summed to obtain the process stability index.

3. The elastic prediction control method according to claim 1, characterized in that: The expression of the first objective function is as follows: ； Wherein, J1 represents the first objective function; represents the output variable predicted by future input; Represents the process output at the current moment The reference trajectory of Q 1 and Q 2 is the corresponding diagonal matrix of weight coefficients; represents the first optimal control sequence.

4. The elastic prediction control method according to claim 1, characterized in that: The expression of the second objective function is as follows: ； ； ； ； Wherein, J2 represents the second objective function; represents the process input increment; T Represented as the transposed symbol of a matrix or vector; Represents the reference trajectory of the self-optimization process output; Q 1 and Q 2 represents the corresponding diagonal matrix of weight coefficients; Indicates the current time k The process status; and Respectively indicate the The upper and lower limit constraints; Y l and Y u Respectively indicate the The upper and lower limit constraints; C represents the output matrix of the state-space model; A a state transition matrix representing the state-space model; B an input matrix representing the state-space model; H p To predict the time domain length; H c To control the time domain length.

5. A flexible predictive control device based on interval target self-optimization, characterized in that: include: An acquisition module is used to acquire the process output at the current moment and the process output sequence within a preset time period in the past; A determination module, used for determining a process stability index for indicating process stability according to the process output sequence; A judgment module, used to judge whether the process stability index is less than an index threshold; A first solving module, configured to solve a first objective function of a set value control strategy to obtain a first optimal control sequence if it is determined that the process stability index is not less than the index threshold; The second solving module is used to further determine whether the process output at the current moment is within the range of the target interval if it is determined that the process stability index is less than the index threshold; if it is determined that the process output at the current moment is within the range of the target interval, solve the second objective function of the reference trajectory self-optimization strategy to obtain the second optimal control sequence; if it is determined that the process output at the current moment is outside the range of the target interval, solve the first objective function to obtain the first optimal control sequence.

6. The elastic prediction control device according to claim 5, characterized in that: The determination module is specifically used to: calculate the standard deviation of the process output sequence according to the average value of the process output sequence; calculate the deviation between the average value of the process output sequence and a set value; calculate the frequency of the process output sequence exceeding a preset interval; The standard deviation, the deviation and the frequency are weightedly summed to obtain the process stability index.

7. The elastic prediction control device according to claim 5, characterized in that: The expression of the first objective function is as follows: ； Wherein, J1 represents the first objective function; represents the output variable predicted by future input; Represents the process output at the current moment The reference trajectory of Q 1 and Q 2 is the corresponding diagonal matrix of weight coefficients; represents the first optimal control sequence.

8. The elastic prediction control device according to claim 5, characterized in that: The expression of the second objective function is as follows: ； ； ； ； Wherein, J2 represents the second objective function; represents the process input increment; T Represented as the transposed symbol of a matrix or vector; Represents the reference trajectory of the self-optimization process output; Q 1 and Q 2 represents the corresponding diagonal matrix of weight coefficients; Indicates the current time k The process status; and Respectively indicate the The upper and lower limit constraints; Y l and Y u Respectively indicate the The upper and lower limit constraints; C represents the output matrix of the state-space model; A a state transition matrix representing the state-space model; B an input matrix representing the state-space model; H p To predict the time domain length; H c To control the time domain length.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the elastic predictive control method based on interval target self-optimization as described in any one of claims 1 to 4 is executed.

10. An electronic device comprising a processor, a memory and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the elastic predictive control method based on interval target self-optimization as described in any one of claims 1-4.

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