Fuzzy PID (proportion integration differentiation) synchronous control system for intelligent prestress tensioning

Abstract

The invention discloses a fuzzy PID (Proportion Integration Differentiation) synchronous control system for intelligent prestress tensioning, and particularly relates to the technical field of hydraulic control of constructional engineering, and the fuzzy PID synchronous control system for intelligent prestress tensioning comprises a control module; a servo driving module; an acquisition module; according to the system, through a hierarchical fuzzy rule base, a control strategy is dynamically switched according to different stages of initial tensioning and load holding, a proportionality coefficient is strengthened in the initial tensioning stage so as to quickly respond to the pressure increasing requirement, overshoot is avoided, a differential coefficient is strengthened in the load holding stage so as to improve the stable pressure holding capacity, and the load holding pressure fluctuation is effectively controlled within + / -0.8%; the precision and the stage adaptability of the tensioning process are obviously improved; pID parameters can be dynamically optimized without manual intervention, control misalignment caused by working condition fluctuation is avoided, and the self-adaptive capacity and long-term stability of the system in a complex construction environment are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of hydraulic control technology in building engineering, and more specifically, to a fuzzy PID synchronous control system for intelligent prestressed tensioning. Background Technology

[0002] In the field of prestressed tensioning construction, existing tensioning systems suffer from several shortcomings, including operational errors, equipment and material defects, and loopholes in process management. These shortcomings can easily lead to prestress loss, reduced structural bearing capacity, and even tensioning safety risks. Specifically, these include: First, large synchronization errors. During multi-strand tensioning, the load coupling effect causes asynchronous errors exceeding 5%, resulting in a prestress distribution dispersion of up to 12% in the beam and a synchronization error of ±2.5% in multiple cylinders, leading to insufficient prestress reserve and uneven structural stress. Second, pressure fluctuations in the hydraulic system. Changes in oil temperature during the load-bearing stage cause changes in hydraulic oil viscosity, resulting in pressure fluctuations of ±3%. Traditional PID controllers exhibit overshoot exceeding 15% during the steel strand relaxation stage. The problems include: 1) Prestress loss exceeding design limits and reduced load-bearing capacity; 2) Lagging tension response, with traditional PID controllers experiencing a 3-second response delay in the initial tensioning stage due to the nonlinear dead zone of the hydraulic system, resulting in significant errors in prestress application; 3) Poor anti-interference, with 60Hz hydraulic pulsation amplifying sensor noise and the differential term of the traditional PID controller exacerbating system oscillations, interfering with the monitoring accuracy of the prestress application process. These defects severely restrict the quality and safety of prestressed tensioning construction. Therefore, an intelligent tensioning system that can solve the problems of synchronization error, pressure fluctuation, response lag, and anti-interference is urgently needed. To this end, a fuzzy PID synchronous control system for intelligent prestressed tensioning is proposed. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a fuzzy PID synchronous control system for intelligent prestressed tensioning. This system uses a hierarchical fuzzy rule base to dynamically switch control strategies for different stages of initial tensioning and load holding. During the initial tensioning stage, the proportional coefficient is strengthened to quickly respond to pressure increase demands and avoid overshoot; during the load holding stage, the derivative coefficient is strengthened to improve stable pressure holding capability, effectively controlling load pressure fluctuations within ±0.8%, significantly improving the accuracy and stage adaptability of the tensioning process. Furthermore, with the aid of a dual-mode disturbance rejection mechanism, feedforward compensation can preemptively offset errors caused by oil temperature drift and system delay, achieving adaptive... Notch filtering can directionally suppress 60Hz hydraulic pulsation interference. The dual mechanism works together to overcome the limitations of traditional single anti-interference methods, improving the tension control accuracy to ±1.5% and enhancing the anti-interference capability by more than 40% compared to traditional systems. Combined with the parameter self-tuning algorithm, it can detect changes in system conditions such as oscillation and hysteresis in real time and automatically perform parameter adjustments (such as reducing the proportional coefficient by 15% and increasing the derivative coefficient by 20% during oscillation). It can dynamically optimize PID parameters without manual intervention, avoiding control inaccuracies caused by fluctuations in operating conditions, and significantly improving the system's adaptability and long-term stability in complex construction environments.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a fuzzy PID synchronous control system for intelligent prestressed tensioning, comprising: The control module includes an industrial computer and a fuzzy PID controller. The fuzzy PID controller is used to fuzzify the tensioning error and the rate of change of error and adaptively adjust the PID parameters. A servo drive module, whose signal input terminal is connected to the control signal output terminal of the control module, is used to receive control commands and output drive signals. The acquisition module includes a force sensor and a strain acquisition instrument. The signal output terminals of the force sensor and the strain acquisition instrument are both connected to the data input terminal of the servo drive module, and are used to acquire tension force data and strain data of the tensioned member in real time, respectively. The execution module includes a hydraulic pump station and a jack assembly driven by the hydraulic pump station. The control terminal of the hydraulic pump station is connected to the signal output terminal of the servo drive module for performing tensioning actions. The feedback module is used to collect, organize, and analyze the data output by the acquisition module, and feed the processed data back to the control module to form a closed-loop control.

[0005] In a preferred embodiment, the control logic of the fuzzy PID controller is as follows: Fuzzification processing: The error and error change rate The system is divided into seven fuzzy levels: NB, NM, NS, ZO, PS, PM, and PB, with an error control range of -5% to +5%. Different membership functions are used in different stages: the initial tensioning stage uses a triangular membership function, and the holding stage uses a Gaussian membership function. Among them, NB is negative large, NM is negative medium, NS is negative small, ZO is zero, PS is positive small, PM is positive medium, and PB is positive large.

[0006] Dynamic parameter adjustment: During the initial tensioning stage, the proportional coefficient will be adjusted. Magnification 1.7x, differential coefficient Reduced by 50%; differential coefficients will be reduced during the load-bearing phase. Magnification 2.0x, integral coefficient Optimized by 30%; Dual-mode anti-interference control logic: Feedforward compensation: via the formula: Compensation for oil temperature drift and system delay; among which, The rate of change of the setpoint for the tension force. Real-time oil temperature parameters for hydraulic pump stations Adaptive notch filtering: using a transfer function of ( The filter suppresses 60Hz hydraulic pulsation interference.

[0007] In a preferred embodiment: the servo drive module adopts a servo driver with a response frequency of not less than 5kHz, and communicates bidirectionally with the control module and the acquisition module through the Profinet bus, with a data transmission delay of ≤10ms; on the one hand, it receives the drive command from the control module and forwards it to the execution module, and on the other hand, it collects the feedback data from the force sensor and strain acquisition instrument in real time and sends it back to the control module, so as to realize the synchronous processing of control commands and feedback data.

[0008] In a preferred embodiment: the acquisition module has a force sensor with an accuracy of ±0.05% and a range of ≥3000kN, which is installed on the tension transmission path of the jack assembly; the strain acquisition instrument has a sampling rate of ≥1kHz, is connected to the tension member through a strain gauge, synchronously acquires tension force data and member strain data, and transmits the data to the servo drive module in the form of digital signals.

[0009] In a preferred embodiment: the execution module has a hydraulic pump station with a displacement of ≥40L / min and a rated pressure of ≥35MPa, and is equipped with real-time oil temperature monitoring function; the jack assembly is driven by the hydraulic pump station and adopts a multi-cylinder synchronous control strategy. Under the control of the fuzzy PID controller, the tension force is applied synchronously with a synchronization error of <1%.

[0010] In a preferred embodiment: the feedback module collects and organizes the tension force data and strain data output by the acquisition module every 10ms, and analyzes and processes them using a Kalman filter algorithm. The processed data is then transmitted to the control module in a closed-loop feedback form to adjust the control parameters of the fuzzy PID controller in real time to ensure the stability of the tensioning process.

[0011] In a preferred embodiment, the overall workflow of the system is as follows: S1, Start-up phase; S2, Initial tensioning data acquisition and feedback; S3, Initial tensioning fuzzy PID control execution; S4, Load holding phase switching and fuzzy PID control; S5, Closed-loop synchronous adjustment; S6, End phase.

[0012] The technical effects and advantages of this invention are as follows: This system uses a hierarchical fuzzy rule base to dynamically switch control strategies for different stages of initial tensioning and holding load. In the initial tensioning stage, the proportional coefficient is strengthened to quickly respond to the pressure increase demand and avoid overshoot. In the holding load stage, the differential coefficient is strengthened to improve the stable pressure holding capability, effectively controlling the holding load pressure fluctuation within ±0.8%, significantly improving the accuracy and stage adaptability of the tensioning process. With the help of a dual-mode anti-interference mechanism, feedforward compensation can offset the errors caused by oil temperature drift and system delay in advance, and adaptive notch filtering can suppress 60Hz hydraulic pulsation interference in a targeted manner. The dual mechanisms work together to solve the limitations of traditional single anti-interference methods, improving the tension force control accuracy to ±1.5%, and enhancing the anti-interference capability by more than 40% compared with traditional systems. With the parameter self-tuning algorithm, the system can detect changes in operating conditions such as oscillation and hysteresis in real time and automatically adjust parameters (such as reducing the proportional coefficient by 15% and increasing the derivative coefficient by 20% when oscillating). The PID parameters can be dynamically optimized without manual intervention, avoiding control inaccuracies caused by operating condition fluctuations and greatly improving the system's adaptability and long-term stability in complex construction environments. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the control module system of the present invention; Figure 2 This is a system architecture diagram of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] An exemplary embodiment will now be described more fully with reference to the accompanying drawings, a fuzzy PID synchronous control system for intelligent prestressed tensioning; a control module comprising an industrial computer and a fuzzy PID controller, the fuzzy PID controller being used to fuzzify the tensioning error and the rate of change of error and adaptively adjust the PID parameters; A servo drive module, whose signal input terminal is connected to the control signal output terminal of the control module, is used to receive control commands and output drive signals. The acquisition module includes a force sensor and a strain acquisition instrument. The signal output terminals of the force sensor and the strain acquisition instrument are both connected to the data input terminal of the servo drive module, and are used to acquire tension force data and strain data of the tensioned member in real time, respectively. The execution module includes a hydraulic pump station and a jack assembly driven by the hydraulic pump station. The control terminal of the hydraulic pump station is connected to the signal output terminal of the servo drive module for performing tensioning actions. The feedback module is used to collect, organize, and analyze the data output by the acquisition module, and feed the processed data back to the control module to form a closed-loop control. The control logic of the fuzzy PID controller is as follows: Fuzzification processing: The error and error change rate The system is divided into seven fuzzy levels: NB, NM, NS, ZO, PS, PM, and PB, with an error control range of -5% to +5%. Different membership functions are used in different stages: the initial tensioning stage uses a triangular membership function, and the holding stage uses a Gaussian membership function. Specifically, NB is negative large, NM is negative medium, NS is negative small, ZO is zero, PS is positive small, PM is positive medium, and PB is positive large. Its function is to transform the two specific data points during tensioning—the deviation between the actual tension and the set tension, and the rate of change of this deviation—into fuzzy information that the controller can flexibly process. It categorizes the deviation and the rate of change into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, and controls the deviation range between -5% and +5%. This covers all possible error scenarios during construction without slowing down the control response due to overly fine divisions. Furthermore, it selects different processing methods according to different stages of tensioning: in the initial tensioning stage, where a rapid increase in tension to the set value is required, a fast-response processing method is used to ensure rapid pressure increase without exceeding limits; in the holding stage, where stable tension is needed, a smoother processing method is used to avoid frequent fluctuations in tension.

[0016] Dynamic parameter adjustment: During the initial tensioning stage, the proportional coefficient will be adjusted. Magnification 1.7x, differential coefficient Reduced by 50%; differential coefficients will be reduced during the load-bearing phase. Magnification 2.0x, integral coefficient Optimized by 30%; The controller has three key parameters responsible for controlling the response speed, eliminating static errors, and maintaining stability. Because the requirements for initial tensioning and holding load are different, these three parameters cannot be fixed. During initial tensioning, the core is to quickly and accurately increase the pressure, so the parameter responsible for the response speed is amplified by 70% to make the system more sensitive to deviations, while the parameter responsible for stability is halved to avoid a slow response. During holding load, the core is to maintain tension stability, so the parameter responsible for stability is doubled to enhance the ability to resist fluctuations, and the parameter responsible for eliminating static errors is amplified by 30% to quickly offset small deviations and ensure that the tension remains stable at the set value for a long time.

[0017] Dual-mode anti-interference control logic: Feedforward compensation: via the formula: Compensation for oil temperature drift and system delay; among which, The rate of change of the setpoint for the tension force. Real-time oil temperature parameters for hydraulic pump stations Adaptive notch filtering: using a transfer function of ( The filter suppresses 60Hz hydraulic pulsation interference; In actual construction, hydraulic systems are susceptible to two types of interference: first, changes in oil temperature alter the viscosity of the hydraulic oil, affecting the accuracy of pressure transmission; second, fluctuations at a fixed frequency generated by the hydraulic pump during operation cause tension jitter. To address the first type of interference, a pre-compensation method is used. Based on the rate of tension increase and real-time oil temperature, the control quantity requiring compensation is calculated in advance to offset the error caused by oil temperature changes. To address the second type of interference, a filtering mechanism is employed to selectively weaken these fixed-frequency fluctuations without affecting the normal tension control signal, thereby ensuring that the system maintains high precision even in complex construction environments.

[0018] Example 1: A fuzzy PID synchronous control system for prestressed intelligent tensioning is used for tensioning a 32m box girder. First, the steel strands of the box girder are initially tensioned (0→1164kN). Through the intervention of fuzzy PID, the hydraulic oil pump is controlled, and the response time is shortened from 3 seconds to 1.5 seconds. The hydraulic pump station controls the jacks. Force sensors and strain acquisition instruments collect and organize stress and strain data during the prestressing tensioning process, calculate errors, and calculate the error change rate. Then, the data is defuzzified and output as ΔKp, ΔKi, and ΔKd. The control quantities are output and fed back to the prestressing tensioning control equipment. The same process is used to load the holding stage (2329kN). The pressure fluctuation can be controlled within ±0.8% (compared to ±3.0% for traditional PID) through the fuzzy PID synchronous control system. This prestressed intelligent tensioning fuzzy PID synchronous control system can reduce the synchronization error of the two cylinders to 0.7% (compared to 5% for traditional systems).

[0019] The servo drive module adopts A kHz servo driver communicates bidirectionally with the control module and acquisition module via a Profinet bus, with a bus communication rate of [missing information]. Mbps, data transmission latency Satisfying the formula: ,in, For the length of data transmitted on the bus, For bus communication speed, This formula, representing the servo drive data processing latency, is used to verify whether the total data transmission latency meets the real-time requirement of ≤10ms. On one hand, it receives drive commands from the control module and forwards them to the execution module; the drive command output value... Satisfying the formula: ,in, This is the servo drive gain, used to amplify the control signal to adapt to the drive requirements of the hydraulic pump station. This formula is used to convert the weak current commands of the control module into strong current drive signals that the execution module can recognize, as the raw control signal output by the control module. On the other hand, it collects feedback data from the force sensor and strain gauge in real time and sends it back to the control module to achieve synchronous processing of control commands and feedback data.

[0020] In the data acquisition module, the force sensor has an accuracy of ±0.05% and a measurement range of [missing information]. kN, installed on the tension transmission path of the jack assembly, its measured value The accuracy verification formula is as follows: ,in, The tension force value is measured by the force sensor. This is the standard tensile calibration value. ,in, The tension force value is measured by the force sensor. For standard tensile calibration value, This formula is used to ensure the accuracy of data acquired by the force sensor, representing the relative error in tensile force measurement; the sampling rate of the strain gauge. kHz, connected to the tensioning member via strain gauges, strain measurement value Satisfying the formula: ,in, For the strain gauge sensitivity coefficient, This formula represents the voltage change of the strain gauge after it is subjected to force. It is used to convert the electrical signal of the strain gauge into the mechanical strain data of the component. Both the force sensor and the strain acquisition instrument transmit the acquired data to the servo drive module in the form of digital signals to ensure the anti-interference of data transmission.

[0021] In the execution module, the hydraulic pump station displacement L / min, rated pressure MPa, with real-time oil temperature monitoring function, oil temperature measurement value The precision formula is: ,in, This is the actual measured oil temperature. This is the standard oil temperature calibration value. This formula represents the relative error in oil temperature measurement and is used to ensure the accuracy of oil temperature data monitoring. The jack assembly is driven by the hydraulic pump station and adopts a multi-cylinder synchronous control strategy, minimizing synchronization error. Satisfying the formula: ,in, The measured maximum tension value for each jack. The minimum tension value measured for each jack. The formula is used to quantify the accuracy of multi-cylinder synchronous tensioning by setting the tension force value. Under the control of the fuzzy PID controller, the hydraulic pump station outputs the appropriate hydraulic power to drive the jack group to achieve synchronous application of tension force and ensure that the synchronization error meets the standard.

[0022] The feedback module collects and processes the tension and strain data output by the acquisition module every 10ms, and then analyzes and processes them using the Kalman filter algorithm. The core formula of the Kalman filter includes: State prediction equation: ,in, Predict the state value at time k. This is the state transition matrix (values ​​range from 0.98 to 0.99), used to characterize the temporal correlation of the data. The optimal state value at time k-1. To control the input matrix (values ​​range from 0.01 to 0.02), This formula is used to predict the theoretical data value at time k-1, and it represents the control input at time k-1. Measurement update equation: ,in, The optimal state value at time k (i.e., the filtered data) The Kalman gain (adaptively adjusted from 0.1 to 0.3) is used to balance the weights of predicted and measured values. The measured data from the module at time k is the actual data collected. The observation matrix (value 1.0) is used to correct the predicted value by combining the measured data and reduce noise interference. The feedback module transmits the filtered optimal data to the control module in the form of closed-loop feedback, which is used to adjust the control parameters of the fuzzy PID controller in real time to ensure the stability of the tensioning process.

[0023] Finally, the overall workflow of the system is as follows: S1. Start-up phase: The industrial control computer inputs the tension setpoint, initial tension threshold and holding time parameters. The industrial control computer sends a start command to the fuzzy PID controller and servo driver. The servo driver triggers the acquisition unit to enter the data acquisition ready state. S2. Initial tensioning data acquisition and feedback: The force sensor and strain acquisition instrument of the acquisition unit acquire the initial tensioning force data of the jack group and the initial strain data of the tensioning component in real time, and transmit them to the data feedback unit via the servo driver. The data feedback unit filters the raw data and then sends it back to the industrial control computer. S3. Initial tensioning fuzzy PID control execution: The industrial control computer compares the initial collected data with the set value, calculates the initial tensioning error and the error change rate, and sends them to the fuzzy PID controller. The fuzzy PID controller sequentially executes "fuzzification processing (triangle membership function) → fuzzy rule base reasoning → defuzzification output ΔKp, ΔKi, ΔKd → update PID parameters → output control quantity", driving the hydraulic pump station to run and driving the jack group to perform the initial tensioning action; S4. Load holding stage switching and fuzzy PID control: When the force sensor detects that the tension force reaches the set load holding threshold, it sends a switching signal to the industrial control computer through the data feedback unit. The industrial control computer instructs the fuzzy PID controller to switch to the load holding control mode, that is, to use the Gaussian membership function for fuzzification processing, dynamically optimize the PID parameters according to the load holding stage parameter adjustment rules, and start dual-mode anti-interference control at the same time. S5. Closed-loop synchronous adjustment: During the entire tensioning process, the acquisition unit updates the tension force data and strain data every 10ms, and transmits them back to the industrial control computer in real time through the data feedback unit. The industrial control computer continuously calculates the real-time error e and the error change rate ec. The fuzzy PID controller repeatedly executes the closed-loop logic of "fuzzification → rule reasoning → defuzzification → parameter update → control output". The servo driver synchronously optimizes the drive instructions to realize the synchronous tensioning of the jack group. S6. End Phase: When the holding time reaches the set value and the tension fluctuation meets the preset requirements, the industrial control computer sends a stop command, the hydraulic pump station depressurizes, the jack assembly resets, and the data acquisition unit records and archives the final tension data. The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0024] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0025] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0026] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0027] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0028] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0029] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A fuzzy PID synchronous control system for intelligent prestressed tensioning, characterized in that: include: The control module includes an industrial computer and a fuzzy PID controller. The fuzzy PID controller is used to fuzzify the tensioning error and the rate of change of error and adaptively adjust the PID parameters. A servo drive module, whose signal input terminal is connected to the control signal output terminal of the control module, is used to receive control commands and output drive signals. The acquisition module includes a force sensor and a strain acquisition instrument. The signal output terminals of the force sensor and the strain acquisition instrument are both connected to the data input terminal of the servo drive module, and are used to acquire tension force data and strain data of the tensioned member in real time, respectively. The execution module includes a hydraulic pump station and a jack assembly driven by the hydraulic pump station. The control terminal of the hydraulic pump station is connected to the signal output terminal of the servo drive module for performing tensioning actions. The feedback module is used to collect, organize, and analyze the data output by the acquisition module, and feed the processed data back to the control module to form a closed-loop control.

2. The fuzzy PID synchronous control system for prestressed intelligent tensioning according to claim 1, characterized in that: The control logic of the fuzzy PID controller is as follows: Fuzzification processing: The error and error change rate The system is divided into seven fuzzy levels: NB, NM, NS, ZO, PS, PM, and PB, with an error control range of -5% to +5%. Different membership functions are used in different stages: the initial tensioning stage uses a triangular membership function, and the holding stage uses a Gaussian membership function. Among them, NB is negative large, NM is negative medium, NS is negative small, ZO is zero, PS is positive small, PM is positive medium, and PB is positive large. Dynamic parameter adjustment: During the initial tensioning stage, the proportional coefficient will be adjusted. Magnification 1.7x, differential coefficient Reduced by 50%; differential coefficients will be reduced during the load-bearing phase. Magnification 2.0x, integral coefficient Optimized by 30%; Dual-mode anti-interference control logic: Feedforward compensation: via the formula: Compensation for oil temperature drift and system delay; among which, The rate of change of the setpoint for the tension force. Real-time oil temperature parameters for hydraulic pump stations Adaptive notch filtering: using a transfer function of ( The filter suppresses 60Hz hydraulic pulsation interference.

3. The fuzzy PID synchronous control system for prestressed intelligent tensioning according to claim 1, characterized in that: The servo drive module uses a servo driver with a response frequency of not less than 5kHz. It communicates bidirectionally with the control module and the acquisition module via the Profinet bus, with a data transmission delay of ≤10ms. On the one hand, it receives the drive commands from the control module and forwards them to the execution module. On the other hand, it collects feedback data from the force sensor and strain gauge in real time and sends it back to the control module, realizing the synchronous processing of control commands and feedback data.

4. The fuzzy PID synchronous control system for prestressed intelligent tensioning according to claim 1, characterized in that: In the acquisition module, the force sensor has an accuracy of ±0.05% and a range of ≥3000kN, and is installed on the tension transmission path of the jack assembly; the strain acquisition instrument has a sampling rate of ≥1kHz, is connected to the tensioned member through a strain gauge, synchronously acquires tension force data and member strain data, and transmits the data to the servo drive module in the form of digital signals.

5. The fuzzy PID synchronous control system for intelligent prestressed tensioning according to claim 1, characterized in that: In the execution module, the hydraulic pump station has a displacement of ≥40L / min and a rated pressure of ≥35MPa, and has a real-time oil temperature monitoring function; the jack group is driven by the hydraulic pump station and adopts a multi-cylinder synchronous control strategy. Under the control of the fuzzy PID controller, the tension force is applied synchronously with a synchronization error of <1%.

6. The fuzzy PID synchronous control system for intelligent prestressed tensioning according to claim 1, characterized in that: The feedback module collects and organizes the tension force and strain data output by the acquisition module every 10ms, and analyzes and processes them using a Kalman filter algorithm. The processed data is then transmitted to the control module in a closed-loop feedback form to adjust the control parameters of the fuzzy PID controller in real time, ensuring the stability of the tensioning process.

7. The fuzzy PID synchronous control system for intelligent prestressed tensioning according to claim 1, characterized in that: The overall workflow of the system is as follows: S1, Start-up phase; S2, Initial tensioning data acquisition and feedback; S3, Initial tensioning fuzzy PID control execution; S4, Load holding phase switching and fuzzy PID control; S5, Closed-loop synchronous adjustment; S6, End phase.

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