A PID self-tuning method and device based on a distributed control system

Abstract

A PID self-tuning method and device based on a distributed control system, the method is executed based on a virtual distributed processing unit or a real distributed processing unit of the distributed control system, comprising: setting a control loop where a controlled object is located to an open loop state, inputting a step signal to the controlled object, collecting a step response for system identification, obtaining a transfer function corresponding to a first-order plus pure lag model including identification parameters, obtaining a calculation expression corresponding to a controller of the controlled object, selecting a PID tuning method, and setting to-be-tuned parameters according to hyperparameters and identification parameters, generating a parameter tuning formula, tuning the to-be-tuned parameters, standardizing the tuned parameters, obtaining standard parameters, improving the accuracy and reliability of the PI / PID controller in the DCS, avoiding the negative influence of bad control parameters on the DCS system, and being able to select a tuning method according to actual conditions, and improving the robustness of the PI / PID controller.

Description

Technical Field

[0001] This article relates to the field of thermal power generation, and in particular to a PID self-tuning method and device based on a distributed control system. Background Technology

[0002] Currently, thermal power generation undertakes the task of flexible peak shaving and provides support for the large-scale grid connection of renewable energy power generation. Therefore, thermal power generating units often operate under deep load variations. Under load variations, thermal power generating unit equipment needs to be adjusted quickly and accurately. PID control is usually used to adjust the controlled object. The factor affecting the speed and accuracy of PID control is whether the selection of PID algorithm parameters is reasonable. Inappropriate PID algorithm parameters will lead to excessively long adjustment time of the controlled object, affecting system response efficiency. Excessive adjustment will lead to excessive overshoot, affecting system safety. Ineffective adjustment will result in system oscillation, and may even cause the output of the controlled object to fail to converge or diverge. Currently, in my country's thermal power generation field, the selection of PID parameters relies on empirical methods for adjustment, which has poor reliability and accuracy, affecting not only production efficiency but also the equipment safety of thermal power generating units. Summary of the Invention

[0003] This application provides a PID self-tuning method and apparatus based on a distributed control system, which improves the accuracy and reliability of the PI controller or PID controller in the DCS, avoids the negative impact of poor control parameters on the DCS system, can select an appropriate tuning method according to the actual situation, and adjust the hyperparameters accordingly to achieve parameter tuning, thereby improving the robustness of the PI controller or PID controller in the DCS.

[0004] On one hand, embodiments of this application provide a PID self-tuning method based on a distributed control system. The method is executed based on a virtual distributed processing unit or a real distributed processing unit of the distributed control system, and includes: Set the control loop containing the controlled object to an open-loop state, input a step signal to the controlled object, and collect the step response of the controlled object; Based on the step response, system identification is performed to obtain the transfer function corresponding to the first-order plus pure time delay model of the control loop. The transfer function includes identification parameters: system gain K, system inertial time T, and time delay τ. Obtain the calculation expression corresponding to the controller of the controlled object, select a PID tuning method, and set the parameter to be tuned in the calculation expression according to the hyperparameter of the PID tuning method and the identification parameter to generate the parameter tuning formula of the controller. Tune the parameter to be tuned according to the parameter tuning formula. The controller includes a PI controller or a PID controller. The tuned parameters are standardized to obtain standard parameters, which are then sent to the controller.

[0005] On the other hand, embodiments of this application also provide a PID self-tuning device based on a distributed control system. The device is deployed in a virtual distributed processing unit or a real distributed processing unit of the distributed control system, and includes: The parameter tuning module is used to set the control loop where the controlled object is located to an open-loop state and input a step signal to the controlled object; A step response acquisition module is used to acquire the step response of the controlled object; The system identification module is used to acquire the step response, perform system identification based on the step response, obtain the transfer function corresponding to the first-order plus pure time delay model of the control loop, and send the identification parameters included in the transfer function to the parameter tuning module. The parameter tuning module is further configured to obtain the calculation expression corresponding to the controller of the controlled object, select a PID tuning method, and set the parameter to be tuned in the calculation expression according to the hyperparameters of the PID tuning method and the identification parameters, generate the parameter tuning formula of the controller, tune the parameter to be tuned according to the parameter tuning formula, standardize the tuned parameters to obtain standard parameters, and send the standard parameters to the controller. The controller includes a PI controller or a PID controller.

[0006] Compared with related technologies, the PID self-tuning method and apparatus based on a distributed control system according to the embodiments of this application can operate online and can be implemented on both virtual and real distributed processing units. This allows field engineers to flexibly apply the method according to actual conditions. After completing the PID self-tuning, the standard parameters of the controller are sent to the PI controller or PID controller, which improves the accuracy and reliability of the PI controller or PID controller in the DCS and avoids the negative impact of poor control parameters on the DCS system, including unexpected failures and equipment downtime. Appropriate tuning methods can be selected according to the actual system conditions, and hyperparameters can be adjusted accordingly to achieve parameter tuning, thereby improving the robustness of the PID controller or PI controller.

[0007] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the embodiments described in the description and the accompanying drawings. Attached Figure Description

[0008] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0009] Figure 1 This is a flowchart of a PID self-tuning method based on a distributed control system according to an embodiment of this application; Figure 2 A control effect diagram using the Lambda method, as a specific example of this application; Figure 3 A control effect diagram using the IMC method as a specific example of this application; Figure 4 A control effect diagram using the ZN method, as a specific example of this application; Figure 5 A control effect diagram using the KT method, as a specific example of this application; Figure 6 The control effect diagrams using the Zeta and Delta methods are specific examples of this application; Figure 7 A control effect diagram using the Beta method, as a specific example of this application; Figure 8 This is a schematic diagram of a PID self-tuning device based on a distributed control system according to an embodiment of this application. Detailed Implementation

[0010] This application describes several embodiments, but these descriptions are exemplary and not limiting, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0011] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application can also be combined with any conventional features or elements to form unique inventive solutions. Any feature or element of any embodiment can also be combined with features or elements from other inventive solutions to form another unique inventive solution. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes can be made within the scope of the appended claims.

[0012] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0013] In actual operation, thermal power generating units are subject to a variety of disturbances, including instrumentation errors and noise interference. The PID control algorithm is an easy-to-understand and easy-to-adjust feedback control algorithm that can adjust the magnitude of the controlled variable based on the real-time output of the controlled object, thereby achieving the purpose of adjusting the output of the controlled object. Therefore, even if there are occasional disturbances, the PID control algorithm can still control the output value of the controlled object well around the set value in the face of system disturbances. The speed and accuracy of PID algorithm control are influenced by the rationality of the selection of PID algorithm parameters. Meanwhile, the control systems currently used in thermal power plants are all distributed control systems (DCS). DCS software typically provides users with a rich array of functional software modules and packages. Control engineers use the configuration software provided by the DCS to appropriately "assemble and connect" (i.e., configure) various functional software to generate application software that meets the requirements of the control system. Therefore, deploying the PID module in the DCS configuration logic can achieve PID control of the controlled objects in thermal power plants. How to achieve PID parameter tuning in the DCS configuration without affecting the normal production of the power plant has become an urgent problem to be solved for thermal power plants.

[0014] This application provides a PID self-tuning method based on a distributed control system. The method is executed by a virtual distributed processing unit or a real distributed processing unit of the distributed control system, and includes steps S100-S400, as follows: Figure 1 As shown: S100: Set the control loop where the controlled object is located to an open-loop state, input a step signal to the controlled object, and collect the step response of the controlled object; S200: Based on the step response, perform system identification to obtain the transfer function corresponding to the first-order plus pure time delay model of the control loop, wherein the transfer function includes identification parameters: system gain K, system inertial time T, and time delay τ; S300: Obtain the calculation expression corresponding to the controller of the controlled object, select a PID tuning method, and set the parameter to be tuned in the calculation expression according to the hyperparameter of the PID tuning method and the identification parameter, generate the parameter tuning formula of the controller, and tune the parameter to be tuned according to the parameter tuning formula, wherein the controller includes a PI controller or a PID controller. S400: Standardize the tuned parameters to obtain standard parameters, and send the standard parameters to the controller.

[0015] In this embodiment, the PID self-tuning method based on the distributed control system can be executed by the virtual distributed processing unit (VDPU) or the real distributed processing unit (RDPU) of the DCS. The control loop includes at least two parts: the controlled object and the controller (PI controller or PID controller). In the VDPU or RDPU, steps S100-S400 are executed to realize the parameter tuning and parameter standardization of the PI controller or PID controller, obtain the standard parameters of the PI controller or PID controller, and then send the standard parameters to the PI controller or PID controller of the DCS to realize PI control or PID control of the controlled object.

[0016] In this embodiment, the distributed control system (DCS) includes a virtual DPU and an RDPU. The VDPU allows PID parameter tuning to be implemented through software simulation on existing hardware. It can be quickly adjusted and configured according to needs, making it suitable for different application scenarios and simple to deploy and maintain. The RDPU is the core computing unit of the DCS, possessing excellent computing performance and supporting efficient PID parameter tuning. Parameter tuning of the PI controller or PID controller can be implemented based on the VDPU or RDPU, improving the accuracy and reliability of the controller. This avoids using controllers containing untuned parameters to control the controlled object, thereby preventing unexpected failures or damages due to improper control and unexpected equipment downtime, achieving accurate control of the controlled object. The tuning process does not rely on additional hardware devices such as servers, gateways, and data centers.

[0017] The PID self-tuning method based on a distributed control system in this embodiment can run online and can be implemented on both virtual and real distributed processing units. This allows field engineers to apply it flexibly according to actual conditions. After completing the PID self-tuning, the standard parameters of the controller are sent to the PI or PID controller, which improves the accuracy and reliability of the PI or PID controller in the DCS and avoids the negative impact of poor control parameters on the DCS system, including unexpected failures and equipment downtime. Appropriate tuning methods can be selected according to the actual system conditions, and hyperparameters can be adjusted accordingly to achieve parameter tuning, thereby improving the robustness of the PID or PI controller.

[0018] In one exemplary embodiment, step S200 may include steps S210-S240: S210: Based on the step response, multiple system identification methods are used to identify the system and obtain the controllable object transfer function corresponding to each system identification method. Each controllable object transfer function includes a set of parameters: system gain K0, system inertial time T0, and lag time τ0. S220: Determine whether there is an anomaly in each group of parameters. If K0=0, or T0≤0, or τ0≤0, then there is an anomaly in the group of parameters; if K0≠0 and T0>0 and τ0>0, then there is no anomaly in the group of parameters. S230: For each of the controlled object transfer functions with no abnormal parameters, perform the following operations: conduct a step experiment based on the step signal and the controlled object transfer function, generate a step test response, and calculate the mean square error corresponding to the controlled object transfer function based on the step test response and the step response. S240: Select the minimum value among all mean square errors, and use the transfer function of the controlled object corresponding to the minimum value as the transfer function.

[0019] In this embodiment, the transfer function is s is a complex frequency domain variable in the Laplace transform; the various system identification methods include the area method, the two-point method, and the inflection point method.

[0020] In this embodiment, the VDPU or RDPU can be equipped with a variety of identification methods, including the area method, the two-point method, and the inflection point method. Step S100 only needs to be executed once, that is, only one complete step response needs to be collected. There is no need to collect the step response multiple times. The system identification can be achieved through the built-in identification methods. Multiple system identifications can be achieved based on a single collected step response, which ensures the accuracy of the system identification results.

[0021] In one exemplary embodiment, step S300, "obtaining the calculation expression corresponding to the controller of the controlled object," may include step S310: S310: Obtain the calculation expression corresponding to the PI controller or the PID controller of the controlled object: .

[0022] In this embodiment, This is the gain coefficient. ρ is the proportional gain coefficient, b is the proportional weight setpoint, SP is the control loop setpoint, and PV is the control loop process value. For integration time, The differential gain coefficient is... is the differential time, and n is the parameter for adjusting the pole values ​​of the differential term.

[0023] In one exemplary embodiment, step S300, "selecting a PID tuning method," may include step S320 or S330: S320: When the controller is the PI controller, one of the following is selected as the PID tuning method: Lambda method, Zeta method, Delta method, ZN method, KT method, and Beta method, wherein the parameters to be tuned for the PI controller include... , , And b, in the calculation expression, =0, =0, n=1; S330: When the controller is the PID controller, one of the following is selected as the PID tuning method: IMC method, ZN method, KT method, and Beta method, wherein the parameters to be tuned for the PID controller include... , , , , n and b.

[0024] In this embodiment, multiple tuning methods can be pre-built into the VDPU or RDPU. The controllers supported by each tuning method are shown in Table 1. The Lambda method is a classic PI controller tuning method. The Beta method is applicable to both PI and PID controllers. The Beta method applicable to PI controllers is an improved Lambda method. The IMC method is an internal model control tuning method. The ZN method is the Ziegler-Nichols method. The KT method is the Kappa-Tau method. The Zeta method is a damping ratio coefficient method developed based on the closed-loop transfer function characteristics of the PI controller and the controlled object. The Delta method is an overshoot method developed based on the closed-loop transfer function characteristics of the PI controller and the controlled object.

[0025] Table 1. Controllers supported by various tuning methods Tuning method Supported controllers Lambda PI Zeta PI Delta PI IMC PID ZN PI, PID KT PI, PID Beta PI, PID In an exemplary embodiment, step S300, "setting the parameter to be tuned in the calculation expression according to the hyperparameters of the PID tuning method and the identification parameters, generating the parameter tuning formula of the controller, and tuning the parameter to be tuned according to the parameter tuning formula," may include step S321 or S322 after step S320: S321: When the controller is the PI controller and the Lambda method is selected as the tuning method, set... =1, b=1, using the system gain K, the system inertia time T, the lag time τ, and the hyperparameter λ to adjust the parameters to be tuned in the calculation expression. and Configure the settings to generate the first parameter tuning formula for the PI controller: ,Will The system inertial time T is set, and the magnitude of the hyperparameter λ is adjusted. Perform the adjustment; S322: When the controller is the PI controller and the Beta method is selected as the tuning method, set... =1, b=1, using the system gain K, the system inertia time T, the lag time τ, and the hyperparameter β to adjust the parameters to be tuned in the calculation expression. and Configure the settings to generate the second parameter tuning formula for the PI controller: ,Will The system inertial time T is set, and the magnitude of the hyperparameter β is adjusted. Adjustments are performed, among which, Let K, T, and τ be the functional relationship between them.

[0026] In this embodiment, when performing step S321, the hyperparameter λ needs to be set to be greater than 0. It can be adjusted by gradually increasing or decreasing the initial value. Alternatively, when it is necessary to quickly adjust the controlled object, the hyperparameter λ can be gradually decreased from the initial value to perform parameter tuning. When it is necessary to slowly and steadily adjust the controlled object, the hyperparameter λ can be gradually increased from the initial value to perform parameter tuning.

[0027] In this embodiment, when performing step S322, the hyperparameter β needs to be set to be greater than 0, which can be adjusted by gradually increasing or decreasing the initial value. It is directly proportional to the hyperparameter β, and by adjusting the hyperparameter β, the tuning can be more intuitive. .

[0028] In this embodiment, when >0 and >0 indicates that the parameters of the PI controller have been successfully tuned, and step S400 can be executed; when ≤0 or If the value is ≤0, it means that the parameter tuning of the PI controller was unsuccessful, and another tuning method can be selected to re-execute step S300 for tuning.

[0029] In another exemplary embodiment, when the controller is the PI controller and there are special requirements for the damping ratio, the Zeta method can be selected for the parameter to be tuned. and When tuning using the Zeta method, the overshoot parameter needs to be set within the range (0, 1). When the controller is a PI controller and there are special requirements for the overshoot, the Delta method can be selected for the parameter to be tuned. and When tuning using the Delta method, the hyperparameter (overshoot) needs to be set in the interval (0, 0.2]. The Zeta and Delta methods are suitable for scenarios where the closed-loop system is a second-order underdamped system.

[0030] In one exemplary embodiment, the step S400 of "standardizing the tuned parameters to obtain standard parameters" may include step S410, which can be performed after step S321 or S322: S410: When the controller is the PI controller, according to the first standardized formula Obtain the standard parameters , .

[0031] In this embodiment, This is the gain coefficient for the standard proportional term. Given the standard integral time, the standard calculation expression for the PI controller obtained based on the standard parameters is: .

[0032] In this embodiment, the tuning obtained It needs to be converted into standard parameters. The result of the adjustment It needs to be converted into standard parameters. Standard parameters are sent to the PI controller to generate a standard calculation expression, which enables PI control of the controlled object.

[0033] In an exemplary embodiment, step S300, which involves "setting the parameter to be tuned in the calculation expression according to the hyperparameters of the PID tuning method and the identification parameters, generating the parameter tuning formula of the controller, and tuning the parameter to be tuned according to the parameter tuning formula," may include step S331 after step S330: S331: When the controller is the PID controller and the Beta method is selected as the tuning method, set b=1, and use the system gain K, the system inertia time T, the lag time τ, and the hyperparameters λ and β to adjust the parameters to be tuned in the calculation expression. , , , , By setting n, the third parameter tuning formula for the PID controller is generated: Adjusting the values ​​of the hyperparameters λ and β to... Perform tuning; adjust the value of the hyperparameter λ to... , , , And n is tuned.

[0034] In this embodiment, the Beta method for PID controllers is based on the Beta method for PI controllers with an added lead element. In actual tuning, when λ=0.5, the tuning results consistently achieve good control performance. Therefore, when using the Beta method to tune a PID controller, λ can be set to 0.5 in the third parameter tuning formula, and control can be achieved solely by adjusting the hyperparameter β. For tuning, the hyperparameter β needs to be set to be greater than 0. It can be adjusted by gradually increasing or decreasing the initial value. That is, the PID controller parameter tuning in step S331 can be the same as the PI controller parameter tuning in step S322, only the hyperparameter β needs to be adjusted.

[0035] In this embodiment, when >0 and >0 and ≠0 and >0 indicates that the parameters of the PID controller have been successfully tuned, and step S400 can be executed; when ≤0 or ≤0 or =0 or If the value is ≤0, it means that the parameter tuning of the PID controller was unsuccessful, and another tuning method can be selected to re-execute step S300 for tuning.

[0036] In this embodiment, during the actual tuning process, it is recommended to first use the Beta method for both the PI controller and the PID controller. This is because extensive testing has shown that the Beta method yields better final control performance. Furthermore, the parameter tuning of the PI controller and the PID controller are interrelated, the hyperparameter β is a continuously adjustable parameter, and the hyperparameter β is related to… or It is directly proportional to the hyperparameter β, and can be intuitively tuned by adjusting the hyperparameter β. or .

[0037] In another exemplary embodiment, when the controller is a PID controller and the control loop of the controlled object is a system with a large time delay, the IMC method is better for parameter tuning. When the controller is a PI controller or a PID controller and the control accuracy requirement is not high, the KT method can be used for parameter tuning. The hyperparameter of the KT method is limited to only two values: 2 and 1.4. When the hyperparameter is set to 2, the control rate is faster and the control objective can be achieved more quickly. When the hyperparameter is set to 1.4, the control rate is slower. When the controller is a PI controller or a PID controller and the control loop of the controlled object is a first-order system without pure time delay or the time delay is much smaller than the system inertia time, the ZN method can be used for parameter tuning.

[0038] In one exemplary embodiment, step S400, "standardizing the tuned parameters to obtain standard parameters," may include step S420, which can be performed after step S331. S420: When the controller is the PID controller, according to the second standardized formula Obtain the standard parameters , , , ; In this embodiment, This is the gain coefficient for the standard proportional term. For standard integration time, The standard differential gain coefficient, Given the standard derivative time, the standard calculation expression for the PID controller obtained based on the standard parameters is: .

[0039] In this embodiment, the tuning obtained It needs to be converted into standard parameters. The result of the adjustment It needs to be converted into standard parameters. The result of the adjustment It needs to be converted into standard parameters. The result of the adjustment It needs to be converted into standard parameters. Standard parameters are sent to the PID controller to generate a standard calculation expression, which enables PID control of the controlled object.

[0040] In one exemplary embodiment, step S100 may include steps S110-S120: S110: Set the control loop where the controlled object is located to an open loop state, input the step signal to the controlled object, and collect the step response signal before the control loop reaches a steady state; S120: Perform outlier processing and smoothing on the step response signal to generate the step response.

[0041] In this embodiment, after setting the control loop to an open-loop state, the step signal is input to the controlled object and the step response signal is collected. When the control loop reaches a stable state, the collection stops. When executing step S120, high-frequency fluctuations in the step response signal are reduced and unnecessary aggressive control is avoided through anomaly point processing and smoothing, thereby improving the stability and robustness of the controller.

[0042] To illustrate the technical effectiveness of the PID self-tuning method based on a distributed control system in this application, a specific example is described in detail below: This specific example demonstrates experimental results of different tuning methods in distributed control systems, including Figures 2-7 In this diagram, the horizontal axis represents time, and the vertical axis represents dimensionless numerical values. The red line represents the setpoint SP of the controlled object in the control loop, with each change in SP being 0.5. The blue line represents the control effect curve of the original PI or PID controller before parameter tuning. The green and yellow lines represent the control effect curves after using the PID self-tuning method based on the distributed control system according to this embodiment. System noise was added during the experiment, such as... Figures 2-7As shown by the dashed lines in the figure, continuous disturbances with values ​​of 0.5, 1.0, 0.5, and 0 (dimensionless constants) were added after the first, second, third, and fourth dashed lines from left to right, respectively. Figures 2-7 It can be seen that, regardless of the presence of system noise, the PID self-tuning method based on a distributed control system according to the embodiments of this application can stably control the controlled object near the setpoint SP, and the control effect is better than that of the original PI controller or PID controller shown by the blue line; among which, Figure 2 This is a diagram illustrating the control effect using the Lambda method. Figure 3 This is a diagram illustrating the control effect of the IMC method. Figure 4 The diagram shows the control effect using the ZN method. Figure 5 This is a diagram illustrating the control effect using the KT method. Figure 6 The control effect diagrams using the Zeta and Delta methods are shown. Figure 7 This is a diagram showing the control effect using the Beta method.

[0043] This application also provides a PID self-tuning device based on a distributed control system. The device is deployed in a virtual distributed processing unit or a real distributed processing unit of the distributed control system, such as... Figure 8 As shown, it includes: The parameter tuning module is used to set the control loop where the controlled object is located to an open-loop state and input a step signal to the controlled object; A step response acquisition module is used to acquire the step response of the controlled object; The system identification module is used to acquire the step response, perform system identification based on the step response, obtain the transfer function corresponding to the first-order plus pure time delay model of the control loop, and send the identification parameters included in the transfer function to the parameter tuning module. The parameter tuning module is further configured to obtain the calculation expression corresponding to the controller of the controlled object, select a PID tuning method, and set the parameter to be tuned in the calculation expression according to the hyperparameters of the PID tuning method and the identification parameters, generate the parameter tuning formula of the controller, tune the parameter to be tuned according to the parameter tuning formula, standardize the tuned parameters to obtain standard parameters, and send the standard parameters to the controller. The controller includes a PI controller or a PID controller.

[0044] In this embodiment, the PID self-tuning device based on the distributed control system can be deployed in the VDPU or RDPU of the distributed control system. The PID self-tuning device based on the distributed control system executes the PID self-tuning method based on the distributed control system, obtains the tuned parameters, performs standardization processing, and sends the standard parameters to the PI controller or PID controller of the distributed control system to implement PI control or PID control on the controlled object.

[0045] In this embodiment, the PID self-tuning device based on the distributed control system can be directly configured on the distributed control system without the need for other hardware devices. The step response acquisition module can acquire the step response in real time, and the system identification module can incorporate multiple system identification algorithms, supporting various system identification methods, including the area method, the two-point method, and the inflection point method. The parameter tuning module can incorporate multiple parameter tuning algorithms, supporting various parameter tuning methods, including the various parameter tuning methods described in Table 1. It can meet the automatic parameter tuning requirements of PI controllers and PID controllers under actual working conditions, and can standardize the tuned parameters and transmit the standard parameters to the PID module in the DCS configuration logic to complete PI control or PID control.

[0046] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term "computer storage medium" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A PID self-tuning method based on a distributed control system, characterized in that, The method is executed based on a virtual distributed processing unit or a real distributed processing unit of a distributed control system, and includes: Set the control loop containing the controlled object to an open-loop state, input a step signal to the controlled object, and collect the step response of the controlled object; Based on the step response, system identification is performed to obtain the transfer function corresponding to the first-order plus pure time delay model of the control loop. The transfer function includes identification parameters: system gain K, system inertial time T, and time delay τ. Obtain the calculation expression corresponding to the controller of the controlled object, select a PID tuning method, and set the parameter to be tuned in the calculation expression according to the hyperparameter of the PID tuning method and the identification parameter to generate the parameter tuning formula of the controller. Tune the parameter to be tuned according to the parameter tuning formula. The controller includes a PI controller or a PID controller. The tuned parameters are standardized to obtain standard parameters, which are then sent to the controller.

2. The PID self-tuning method based on a distributed control system as described in claim 1, characterized in that, The step of identifying the system based on the step response and obtaining the transfer function corresponding to the first-order plus pure time-delay model of the control loop includes: Based on the step response, multiple system identification methods are used to identify the system and obtain the control object transfer function corresponding to each system identification method. Each control object transfer function includes a set of parameters: system gain K0, system inertial time T0, and lag time τ0. Determine if there is an anomaly in each set of parameters. If K0=0, or T0≤0, or τ0≤0, then there is an anomaly in that set of parameters; if K0≠0 and T0>0 and τ0>0, then there is no anomaly in that set of parameters. For each of the controlled object transfer functions with no abnormal parameters, the following operations are performed: a step experiment is performed based on the step signal and the controlled object transfer function to generate a step test response, and the mean square error corresponding to the controlled object transfer function is calculated based on the step test response and the step response. The minimum value is selected from all mean square errors, and the transfer function of the controlled object corresponding to the minimum value is used as the transfer function. Wherein, the transfer function is s is a complex frequency domain variable in the Laplace transform; the various system identification methods include the area method, the two-point method, and the inflection point method.

3. The PID self-tuning method based on a distributed control system as described in claim 1, characterized in that, The step of obtaining the calculation expression corresponding to the controller of the controlled object includes: Obtain the calculation expression corresponding to the PI controller or the PID controller of the controlled object: ； in, This is the gain coefficient. ρ is the proportional gain coefficient, b is the proportional weight setpoint, SP is the control loop setpoint, and PV is the control loop process value. For integration time, The differential gain coefficient is... is the differential time, and n is the parameter for adjusting the pole values ​​of the differential term.

4. The PID self-tuning method based on a distributed control system as described in claim 3, characterized in that, The selection of a PID tuning method includes: When the controller is a PI controller, one of the following methods is selected as the PID tuning method: Lambda method, Zeta method, Delta method, ZN method, KT method, and Beta method. The parameters to be tuned for the PI controller include... , , And b, in the calculation expression, =0, =0, n=1; When the controller is a PID controller, one of the following methods is selected as the PID tuning method: IMC method, ZN method, KT method, and Beta method. The parameters to be tuned for the PID controller include... , , , , n and b.

5. The PID self-tuning method based on a distributed control system as described in claim 4, characterized in that, The step of setting the parameter to be tuned in the calculation expression according to the hyperparameters of the PID tuning method and the identification parameters to generate the parameter tuning formula of the controller, and tuning the parameter to be tuned according to the parameter tuning formula, includes: When the controller is the PI controller and the Lambda method is selected as the tuning method, the settings are as follows: =1, b=1, using the system gain K, the system inertia time T, the lag time τ, and the hyperparameter λ to adjust the parameters to be tuned in the calculation expression. and Configure the settings to generate the first parameter tuning formula for the PI controller: ,Will The system inertial time T is set, and the magnitude of the hyperparameter λ is adjusted. Perform the adjustment; When the controller is the PI controller and the Beta method is selected as the tuning method, the following settings are made: =1, b=1, using the system gain K, the system inertia time T, the lag time τ, and the hyperparameter β to adjust the parameters to be tuned in the calculation expression. and Configure the settings to generate the second parameter tuning formula for the PI controller: ,Will The system inertial time T is set, and the magnitude of the hyperparameter β is adjusted. Adjustments are performed, among which, Let K, T, and τ be the functional relationship between them.

6. The PID self-tuning method based on a distributed control system as described in claim 5, characterized in that, The process of standardizing the tuned parameters to obtain standard parameters includes: When the controller is the PI controller, according to the first standardized formula Obtain the standard parameters , ;in, This is the gain coefficient for the standard proportional term. Given the standard integral time, the standard calculation expression for the PI controller obtained based on the standard parameters is: .

7. The PID self-tuning method based on a distributed control system as described in claim 4, characterized in that, The step of setting the parameter to be tuned in the calculation expression according to the hyperparameters of the PID tuning method and the identification parameters to generate the parameter tuning formula of the controller, and tuning the parameter to be tuned according to the parameter tuning formula, includes: When the controller is the PID controller and the Beta method is selected as the tuning method, b=1 is set, and the system gain K, the system inertia time T, the lag time τ, and the hyperparameters λ and β are used to adjust the parameters to be tuned in the calculation expression. , , , , By setting n, the third parameter tuning formula for the PID controller is generated: Adjusting the values ​​of the hyperparameters λ and β to... Perform tuning; adjust the value of the hyperparameter λ to... , , , And n is tuned.

8. The PID self-tuning method based on a distributed control system as described in claim 7, characterized in that, The process of standardizing the tuned parameters to obtain standard parameters includes: When the controller is the PID controller, according to the second standardized formula Obtain the standard parameters , , , ;in, This is the gain coefficient for the standard proportional term. For standard integration time, The standard differential gain coefficient, Given the standard derivative time, the standard calculation expression for the PID controller obtained based on the standard parameters is: .

9. The PID self-tuning method based on a distributed control system as described in claim 1, characterized in that, The control loop containing the controlled object is set to an open-loop state, a step signal is input to the controlled object, and the step response of the controlled object is acquired, including: The control loop containing the controlled object is set to an open-loop state, the step signal is input to the controlled object, and the step response signal is acquired before the control loop reaches a steady state. The step response signal is processed for outlier points and smoothed to generate the step response.

10. A PID self-tuning device based on a distributed control system, characterized in that, The device is deployed in a virtual distributed processing unit or a real distributed processing unit of a distributed control system, including: The parameter tuning module is used to set the control loop where the controlled object is located to an open-loop state and input a step signal to the controlled object; A step response acquisition module is used to acquire the step response of the controlled object; The system identification module is used to acquire the step response, perform system identification based on the step response, obtain the transfer function corresponding to the first-order plus pure time delay model of the control loop, and send the identification parameters included in the transfer function to the parameter tuning module. The parameter tuning module is further configured to obtain the calculation expression corresponding to the controller of the controlled object, select a PID tuning method, and set the parameter to be tuned in the calculation expression according to the hyperparameters of the PID tuning method and the identification parameters to generate the parameter tuning formula of the controller, tune the parameter to be tuned according to the parameter tuning formula, standardize the tuned parameters to obtain standard parameters, and send the standard parameters to the controller. The controller includes a PI controller or a PID controller.

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