Aero-engine nut tightening fuzzy adaptive control method and system

Abstract

The application discloses an aero-engine nut tightening fuzzy adaptive control method and system, and belongs to the technical field of aero-engine manufacturing and precision assembly. Transfer function models under multiple torque levels are identified and obtained, and optimal PID parameters of a group of PLC controllers are obtained by optimizing each transfer function model; a PID parameter base value and an adjustment domain are determined; an adjustment matrix is constructed for each PID parameter; online fuzzy adaptive control is performed according to the adjustment matrix and the PID parameter base value, and a speed instruction is obtained to control a tightening gun to tighten aero-engine nuts. In the case where motor current signals cannot be obtained, the application realizes high-precision adaptive control only by relying on an external torque sensor, so that the tightening gun can automatically adapt to aero-engine nuts of different specifications and different friction characteristics, and overshoot or insufficient torque caused by load changes is avoided.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine manufacturing and precision assembly technology, and particularly relates to a fuzzy adaptive control method and system for tightening aero-engine nuts. Background Technology

[0002] The assembly of aircraft engine nuts places extremely high demands on torque accuracy, tightening process stability, and data traceability (torque accuracy is typically required to be better than ±5%, with critical parts requiring ±2%). Currently, mainstream tightening gun brands such as DDK, Atlas Copco, and Bosch use closed, dedicated control systems, which have the following shortcomings:

[0003] 1. Control algorithm cannot be modified: Users cannot adjust the control strategy according to specific nuts (different materials, friction coefficients, sizes), and can only rely on the parameters preset by the equipment supplier.

[0004] 2. Reliance on current sensor: Most tightening guns estimate torque by using the motor current loop, which requires a built-in current sensor, increasing hardware costs. Furthermore, the current signal is easily affected by motor temperature and power supply voltage fluctuations, resulting in decreased accuracy under low-speed, high-torque conditions.

[0005] 3. Low or closed sampling frequency of output data: Usually only 10-20Hz torque-angle data is provided, which cannot capture transient changes during the tightening process; and the data format is closed, making it difficult to perform secondary analysis; some tightening guns can only show the torque curve in dedicated software and do not provide data at all.

[0006] Research has yielded some results in tightening control for aircraft engine nut assembly. CN112548924B, "A Bolt Wrench Torque Control Method Based on Fuzzy PID," employs a dual closed-loop structure with an outer speed loop and an inner current loop. This requires both current and speed sensors, and torque calculation is based on a motor mathematical model, increasing hardware costs and making the current signal susceptible to temperature and operating condition interference. CN106709077B, "Bolt Tightening Equipment and Method and Bolt Tightening Monitoring System and Method," periodically collects torque and angle data for clamping force control, but this is only used for final quality assessment and does not utilize high-frequency sampling data for real-time dynamic adjustment of control parameters. CN119347683A, "An Electric Tightening Tool Control System and Method," requires the acquisition of motor armature current, torque, and angle values ​​for control, but lacks an adaptive mechanism, resulting in poor robustness of the fixed-parameter PID control when dealing with nuts of different specifications. CN103590969B, "Parameter Optimization Method for PID Turbine Governor Based on Multi-condition Time-Domain Response," utilizes multi-condition identification and intelligent optimization, but it is an offline fixed-parameter PID controller and cannot respond to real-time load changes during the tightening process. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a fuzzy adaptive control method and system for tightening aircraft engine nuts. Even when motor current signals are unavailable, high-precision adaptive control is achieved solely through an external torque sensor. This allows the tightening gun to automatically adapt to aircraft engine nuts of different specifications and friction characteristics, avoiding overshoot or insufficient torque due to load variations. Fuzzy adaptive PID control with moderate computational complexity is implemented on a general-purpose PLC platform, meeting the real-time requirements of industrial environments. It achieves effective data sampling and raw data storage at frequencies exceeding 100Hz, providing a complete basis for tightening quality traceability.

[0008] The technical solution of this invention is as follows:

[0009] On one hand, the present invention provides a fuzzy adaptive control method for tightening nut in an aero-engine, comprising the following steps:

[0010] Speed ​​commands sent by the PLC controller and torque values ​​fed back by external torque sensors are collected at multiple torque levels. Transfer function models for these multiple torque levels are identified and obtained. For each transfer function model, a set of optimal PID parameters for the PLC controller is optimized. The PID parameters include a proportional gain. Integral coefficient Differential coefficients ;

[0011] Based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model, determine the base values ​​of the PID parameters and the domain of adjustment.

[0012] For each PID parameter, an adjustment matrix is ​​constructed. Each matrix element represents the adjustment of the PID parameter under a set of torque errors and torque error change rates, and the matrix element values ​​are located within a defined adjustment domain.

[0013] Online fuzzy adaptive control is performed based on the adjustment matrix and PID parameter base values ​​to obtain speed commands to control the tightening gun to tighten the aircraft engine nuts.

[0014] Furthermore, the identification and acquisition of transfer function models under multiple torque levels, and the optimization of a set of optimal PID parameters for the PLC controller for each transfer function model, specifically includes:

[0015] A1: Collects speed commands sent by the PLC controller and torque values ​​fed back by external torque sensors at multiple torque levels.

[0016] Specifically, select within the working range of the tightening gun. Torque level , For the first For each torque level, multiple tightening tests were conducted using the same type of aircraft engine nut, with the speed command sent by the PLC controller serving as the input to the tightening gun. The torque value fed back by an external torque sensor is used as the output of the tightening gun. , Indicates the time.

[0017] A2: Identify the transfer function model for each torque level based on the speed command sent by the PLC controller and the torque value fed back by the external torque sensor.

[0018] The transfer function model is as follows:

[0019] (1);

[0020] in, For the first Laplace transform at a torque level The transfer function model of the domain. For gain, It is a time constant. It refers to the order.

[0021] A3: For each torque level, the transfer function model is optimized to obtain a set of optimal PID parameters for the PLC controller, including proportional coefficient, integral coefficient, and derivative coefficient.

[0022] The fitness function of the optimization process is defined as:

[0023] (2);

[0024] in, For the fitness function, This is the maximum score. The unit step fitness function response error, For overshoot, The maximum allowable overshoot, This is the penalty coefficient.

[0025] Furthermore, determining the PID parameter base value and adjustment universe based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model specifically includes:

[0026] B1: Determine the base value of the PID parameters based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model;

[0027] Specifically: The median or weighted average of multiple optimal PID parameters is used as the base value of the PID controller's PID parameters, including the proportional coefficient base value. Integral coefficient base value and differential coefficients parameter values ;

[0028] B2: Calculate the maximum positive deviation ratio and the maximum negative deviation ratio of each optimal PID parameter relative to the corresponding PID parameter base value, and then determine the adjustment domain;

[0029] (3);

[0030] (4);

[0031] (5);

[0032] in, , , These are the maximum positive deviation ratios of the proportional coefficient, integral coefficient, and derivative coefficient of the PID controller, respectively. , , These represent the maximum negative deviation ratio of the proportional coefficient, the maximum negative deviation ratio of the integral coefficient, and the maximum negative deviation ratio of the derivative coefficient of the PID controller.

[0033] For each PID parameter, the one with the larger maximum positive deviation ratio and the larger maximum negative deviation absolute value is taken as the symmetric boundary of the adjustment domain, or the one with the larger maximum positive deviation ratio and the larger maximum negative deviation is taken as the asymmetric boundary of the adjustment domain.

[0034] Furthermore, an adjustment matrix is ​​constructed for each PID parameter, where each matrix element represents the PID parameter adjustment under a set of torque errors and torque error change rates, and the matrix element values ​​lie within a defined adjustment universe, specifically including:

[0035] C1: Sets the level of torque error and torque error change rate;

[0036] C2: Based on the set torque error and torque error change rate level, construct the adjustment matrix for each PID parameter.

[0037] Furthermore, the online fuzzy adaptive control based on the adjustment matrix and PID parameter base values ​​to obtain speed commands to control the tightening gun to tighten the aircraft engine nut specifically includes:

[0038] D1: Read the torque setpoint in the current control cycle. Torque value from external torque sensor The torque error is calculated and a first-order low-pass filter is applied to obtain the low-pass filtered torque error.

[0039] (6);

[0040] in, This is for torque error;

[0041] D2: Calculate the torque error change rate based on the torque error after low-pass filtering;

[0042] (7);

[0043] in, The rate of change of torque error. These are the filter coefficients. This represents the torque error from the previous control cycle. The sampling interval is... This represents the rate of change of torque error in the previous control cycle.

[0044] D3: Based on the torque error after low-pass filtering and the rate of change of torque error, find the adjustment matrix and adjust the PID parameters;

[0045] First, the torque error and the rate of change of torque error are linearly mapped to the torque error level index and the rate of change of torque error level index.

[0046] Then read the PID parameter adjustment from the adjustment matrix:

[0047] (8);

[0048] (9);

[0049] (10);

[0050] in, , and These are the adjustment matrices for the proportional coefficient, the integral coefficient, and the differential coefficient, respectively. Index for torque error change rate levels; This is an index for torque error levels.

[0051] Finally, update the PID parameters based on the PID parameter adjustment amount and apply amplitude limiting protection.

[0052] The method for updating PID parameters is as follows:

[0053] (11);

[0054] (12);

[0055] (13);

[0056] D4: Calculate the control quantity using PID parameters. That is, speed command;

[0057] (15);

[0058] D5: The control input is sent to the servo driver of the tightening gun via the bus protocol, which controls the servo motor and reducer to achieve tightening;

[0059] D6: Temporarily store the variables used in this tightening operation in the PLC controller as an array;

[0060] D7: Determine whether tightening is complete. If complete, execute D8; otherwise, return to D1.

[0061] D8: Filter the variables temporarily stored in the PLC controller for this tightening process, then send them to the host computer to generate a CSV file and a torque-angle curve.

[0062] On the other hand, the present invention also provides a fuzzy adaptive control system for tightening aircraft engine nuts, for implementing a fuzzy adaptive control method for tightening aircraft engine nuts, comprising:

[0063] The host computer generates a CSV file from the variables uploaded by the PLC controller for this tightening process, and also generates a torque-angle curve.

[0064] The PLC controller uses a data acquisition module to acquire the torque detected by an external torque sensor in real time; a parameter storage module to store the adjustment matrix and the current initial PID parameters; a quantization module to calculate and quantize the torque error and the rate of change of torque error; a lookup module to read the PID parameter adjustment from the adjustment matrix based on the quantized torque error and the rate of change of torque error, and update the PID parameters; a PID calculation module to calculate the speed command based on the updated PID parameters and send it to the tightening gun; and a data recording module to filter the variables temporarily stored in this tightening operation and upload them to the host computer.

[0065] A tightening gun is used to tighten nuts on aircraft engines.

[0066] Furthermore, the PLC controller also includes a low-pass filter module for filtering torque errors; the PLC controller also includes a limiting module for limiting and protecting the updated PID parameters and speed commands.

[0067] Furthermore, the tightening gun includes a tightening gun body, a servo driver, a servo motor, and a reducer;

[0068] The servo driver is used to drive the servo motor and the speed reducer.

[0069] The servo motor and reducer are used to control the tightening gun to tighten the aircraft engine nuts.

[0070] Thirdly, this application proposes an electronic device comprising: one or more processors, and a memory for storing instructions, which, when executed by the one or more processors, cause the one or more processors to execute the aforementioned fuzzy adaptive control method for tightening aircraft engine nuts.

[0071] Fourthly, this application proposes a computer-readable storage medium storing executable instructions that, when executed, cause a processor to perform the aforementioned fuzzy adaptive control method for tightening aircraft engine nuts.

[0072] Fifthly, this application proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned fuzzy adaptive control method for tightening aircraft engine nuts.

[0073] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0074] 1. The advantage of this invention is that it eliminates the need for a current sensor and achieves high-precision adaptive control solely through an external torque sensor, thereby reducing hardware costs, avoiding the influence of temperature and voltage fluctuations on the motor current signal, and improving control reliability.

[0075] 2. The advantages of this invention lie in the high efficiency of the hierarchical matrix lookup table method. It uses a 13×7 offline pre-stored matrix to replace online fuzzy inference, resulting in low computational load, fast response speed, and suitability for PLC platform implementation. Compared with conventional fuzzy PID, this invention eliminates the need for real-time fuzzy inference, significantly reducing the PLC's computing power requirements.

[0076] 3. The advantages of this invention lie in multi-condition identification and quantitative determination of the parameter universe of discourse. It obtains the optimal PID parameters under different torque levels through intelligent algorithms, and then calculates the parameter base values ​​and adjustment universe of discourse, making the design of the fuzzy controller based on evidence rather than purely empirical trial and error. Compared with CN103590969B, this invention uses the multi-condition identification results for online adaptive adjustment, rather than fixing the parameters offline.

[0077] 4. The advantage of this invention is that it is adaptive across the entire torque range. By identifying and determining the base value and domain of discourse under multiple operating conditions, the adjustment range of the PID parameters precisely covers the optimal value at each operating point, thus avoiding oscillations caused by blindly adjusting over a large range.

[0078] 5. The advantage of this invention lies in the integration of high-frequency sampling data-driven control and traceability. ≥100Hz high-frequency sampling data is used both for real-time parameter adjustment and saved as an original CSV file, balancing control performance and quality traceability. Compared to CN106709077B, the high-frequency sampling data of this invention is used for real-time updating of PID parameters, rather than solely for control decisions.

[0079] 6. The advantage of this invention is that it is robust to different nuts. When replacing aircraft engine nuts of different specifications and friction characteristics, the fuzzy controller can automatically adjust the PID parameters to the optimal value close to the new operating point, which is significantly better than fixed PID. Attached Figure Description

[0080] Figure 1 This is a flowchart of a fuzzy adaptive control method for tightening aircraft engine nuts according to an embodiment of the present invention;

[0081] Figure 2 This is an example diagram of the torque-angle curve generated after tightening in an embodiment of the present invention;

[0082] Figure 3 This is a comparison chart of the effects of the present invention and traditional fixed PID control in the embodiments of the present invention;

[0083] Among them, (a) is a comparison chart of torque response; (b) is a comparison chart of speed command.

[0084] Figure 4 This is a structural diagram of a fuzzy adaptive control system for tightening aircraft engine nuts according to an embodiment of the present invention. Detailed Implementation

[0085] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0086] Example 1:

[0087] A fuzzy adaptive control method for tightening nut in an aero-engine employs a three-stage technical approach: "offline identification under multiple operating conditions → determination of parameter baselines and universe of discourse → online hierarchical matrix lookup and adaptive control." Figure 1 As shown, it includes the following steps:

[0088] Step 1: Collect speed commands sent by the PLC controller and torque values ​​fed back by the external torque sensor at multiple torque levels, identify and obtain the transfer function models at multiple torque levels, and optimize a set of optimal PID parameters for the PLC controller for each transfer function model; the PID parameters include proportional coefficients. Integral coefficient Differential coefficients ;

[0089] Step 1.1: Collect speed commands sent by the PLC controller and torque values ​​fed back by the external torque sensor at multiple torque levels.

[0090] Specifically, select within the tightening gun's working range (10-100 Nm). Torque level (e.g., 20Nm, 40Nm, 60Nm, 80Nm, 100Nm) For the first For each torque level, multiple tightening experiments were conducted using the same type of aircraft engine nut. The speed command (unit: rpm) sent by the PLC controller was used as the input to the tightening gun. The torque value (unit: Nm) fed back by an external torque sensor is used as the output of the tightening gun. The sampling frequency is 1000Hz, and the input and output datasets are recorded. Indicates the time.

[0091] Step 1.2: For the speed command sent by the PLC controller and the torque value fed back by the external torque sensor at each torque level, the transfer function model at each torque level is identified using an intelligent optimization algorithm.

[0092] The transfer function model is as follows:

[0093] (1);

[0094] in, For the first Laplace transform at a torque level The transfer function model of the domain. For gain, It is a time constant. It refers to the order.

[0095] In this embodiment, the Bat Algorithm can be used, or the Grey Wolf Optimizer (GWO) or the Cuckoo Search (CS) can be selected for identification; the fitness function of the identification process is the root mean square error (RMSE) or integral absolute error (IAE) between the output of the transfer function model and the actual torque.

[0096] Step 1.3: For each torque level, the transfer function model is optimized using an intelligent algorithm to obtain a set of optimal PID parameters for the PLC controller, including proportional coefficient, integral coefficient, and derivative coefficient.

[0097] For each identified torque level, the transfer function model The intelligent optimization algorithm is used again, with time multiplied by absolute error integral (ITAE) as the main factor and an overshoot penalty term added, to optimize and obtain a set of optimal PID parameters;

[0098] The fitness function of the optimization process is defined as:

[0099] (2);

[0100] in, For the fitness function, This is the maximum score. The unit step fitness function response error, Overshoot (%) The maximum allowable overshoot (e.g., 2%). This is the penalty coefficient; This is an overshoot penalty item;

[0101] Step 2: Determine the base values ​​of the PID parameters and the tuning domain based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model;

[0102] Step 2.1: Determine the base values ​​of the PID parameters based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model;

[0103] Specifically: The median or weighted average of multiple optimal PID parameters is used as the base value of the PID controller's PID parameters, including the proportional coefficient base value. Integral coefficient base value and differential coefficients parameter values ;

[0104] Step 2.2: Calculate the maximum positive deviation ratio and the maximum negative deviation ratio of each optimal PID parameter relative to the corresponding PID parameter base value, and then determine the adjustment domain;

[0105] (3);

[0106] (4);

[0107] (5);

[0108] in, , , These are the maximum positive deviation ratios of the proportional coefficient, integral coefficient, and derivative coefficient of the PID controller, respectively. , , These represent the maximum negative deviation ratio of the proportional coefficient, the maximum negative deviation ratio of the integral coefficient, and the maximum negative deviation ratio of the derivative coefficient of the PID controller.

[0109] For each PID parameter, the one with the larger maximum positive deviation ratio and the one with the larger maximum negative deviation absolute value is taken as the symmetric boundary of the adjustment domain, or the one with the larger maximum positive deviation ratio and the one with the larger maximum negative deviation is taken as the asymmetric boundary of the adjustment domain.

[0110] In this embodiment, the adjustment domain for the scaling factor is -20% to +20%, and the adjustment domain for the integral factor is -30% to +40%.

[0111] The adjustment domain of the differential coefficients is: -30% to +30%.

[0112] Step 3: Construct an adjustment matrix for each PID parameter. Each element in the matrix represents the adjustment of the PID parameter under a set of torque errors and torque error change rates, and the values ​​of the matrix elements are located within a defined adjustment domain.

[0113] Step 3.1: Set the levels of torque error and torque error change rate;

[0114] Torque error E: Since there is only insufficient torque (positive deviation) during the tightening process, only 7 positive deviation levels are considered: E0 (close to 0), E1, E2, E3, E4, E5, E6; corresponding to the actual torque error range of 0-100Nm (can be adjusted according to the maximum error).

[0115] Torque error change rate EC: Divided into 13 levels, from EC0 (negative maximum, approximately -200 Nm / s) to EC12 (positive maximum, approximately +200 Nm / s), covering the dynamic range of torque error changes during tightening.

[0116] Step 3.2: Based on the set torque error and torque error change rate level, construct the adjustment matrix (13×7 level matrix) for each PID parameter.

[0117] In this embodiment, based on the variation law of optimal PID parameters under multiple operating conditions and expert experience, three 13-row × 7-column two-dimensional arrays are designed to store the percentage adjustment of the proportional coefficient, the percentage adjustment of the integral coefficient, and the percentage adjustment of the derivative coefficient, respectively. , , .

[0118] Table 1. Adjustment matrix of proportional coefficients;

[0119] EC0 (Negative Large) -20 -18 -15 -12 -8 -3 +5 EC1 -18 -16 -13 -10 -6 -1 +7 EC2 -16 -14 -11 -8 -4 +1 +9 EC3 -14 -12 -9 -6 -2 +3 +11 EC4 -12 -10 -7 -4 0 +5 +13 EC5 -10 -8 -5 -2 +2 +7 +15 EC6 (Zero) -8 -6 -3 0 +4 +9 +17 EC7 -6 -4 -1 +2 +6 +11 +18 EC8 -4 -2 +1 +4 +8 +13 +20 EC9 -2 0 +3 +6 +10 +15 +20 EC10 0 +2 +5 +8 +12 +17 +20 EC11 +2 +4 +7 +10 +14 +18 +20 EC12 (Chia Tai) +4 +6 +9 +12 +16 +20 +20

[0120] Table 2 Adjustment matrix for integral coefficients;

[0121] EC0 (Negative Large) -30 -28 -25 -22 -18 -12 -6 EC1 -28 -25 -22 -19 -15 -9 -3 EC2 -25 -22 -19 -16 -12 -6 0 EC3 -22 -19 -16 -13 -9 -3 +3 EC4 -18 -15 -12 -9 -5 0 +6 EC5 -14 -11 -8 -5 -1 +4 +10 EC6 (Zero) -10 -7 -4 -1 +2 +8 +14 EC7 -6 -3 0 +3 +6 +12 +18 EC8 -2 +1 +4 +7 +10 +16 +22 EC9 +2 +5 +8 +11 +14 +20 +26 EC10 +6 +9 +12 +15 +18 +24 +30 EC11 +10 +13 +16 +19 +22 +28 +35 EC12 (Chia Tai) +14 +17 +20 +23 +26 +32 +40

[0122] Table 3. Adjustment matrix of differential coefficients;

[0123] EC0 (Negative Large) +30 +28 +25 +22 +18 +10 +5 EC1 +28 +25 +22 +19 +15 +8 +3 EC2 +25 +22 +19 +16 +12 +6 +1 EC3 +22 +19 +16 +13 +9 +4 -1 EC4 +18 +15 +12 +9 +5 +2 -3 EC5 +14 +11 +8 +5 +1 -1 -5 EC6 (Zero) +10 +7 +4 +1 -2 -4 -8 EC7 +6 +3 0 -3 -5 -7 -11 EC8 +2 -1 -4 -7 -8 -10 -14 EC9 -2 -5 -8 -11 -11 -13 -17 EC10 -6 -9 -12 -15 -14 -16 -20 EC11 -10 -13 -16 -19 -17 -19 -23 EC12 (Chia Tai) -14 -17 -20 -23 -20 -22 -26

[0124] Matrix design principles:

[0125] When the torque error is large (E5-E6) and the torque error trend is to continue to increase (EC8 and above), increase... With a rapid response; when the error approaches zero (E0-E2) and the torque error shows a rapid decreasing trend (EC0-EC2), reduce... To prevent overshoot; integral coefficient Primarily activated in the small error region, and reduced in the large error region to prevent saturation; differential coefficients It is enhanced when the error is close to zero and the rate of change is large, in order to suppress overshoot.

[0126] Step 4: Perform online fuzzy adaptive control based on the adjustment matrix and PID parameter base values ​​to obtain speed commands to control the tightening gun to tighten the aircraft engine nuts;

[0127] Step 4.1: Read the torque setpoint in the current control cycle (e.g., 1ms, corresponding to 1000Hz). Torque value from external torque sensor The torque error is calculated and a first-order low-pass filter is applied to obtain the low-pass filtered torque error.

[0128] (6);

[0129] in, This is for torque error;

[0130] Step 4.2: Calculate the torque error change rate based on the torque error after low-pass filtering;

[0131] (7);

[0132] in, The rate of change of torque error. These are the filter coefficients. This represents the torque error from the previous control cycle. The sampling interval is... This represents the rate of change of torque error in the previous control cycle.

[0133] Step 4.3: Based on the torque error after low-pass filtering and the rate of change of torque error, find the adjustment matrix and adjust the PID parameters;

[0134] First, the torque error and the rate of change of torque error are linearly mapped to the grade index. and ;

[0135] Then read the PID parameter adjustment from the adjustment matrix:

[0136] (8);

[0137] (9);

[0138] (10);

[0139] in, , and These are the adjustment matrices for the proportional coefficient, the integral coefficient, and the differential coefficient, respectively. Index for torque error change rate levels; This is an index for torque error levels.

[0140] Finally, update the PID parameters based on the PID parameter adjustment and apply amplitude limiting protection:

[0141] The method for updating PID parameters is as follows:

[0142] (11);

[0143] (12);

[0144] (13);

[0145] The amplitude limiting protection is as follows:

[0146] (14);

[0147] Step 4.4: Calculate the control quantity using PID parameters. That is, speed command;

[0148] (15);

[0149] The integral term is activated only after the torque reaches a certain threshold, such as 50% of the maximum target torque, to prevent integral saturation.

[0150] This embodiment limits the output amplitude of the control quantity to within the allowable range of the servo motor of the tightening gun, and simultaneously adds a control quantity change rate limit.

[0151] Step 4.5: Send the control signal to the servo driver of the tightening gun via the bus protocol to control the servo motor and reducer to achieve tightening;

[0152] Step 4.6: Temporarily store the main variables of this tightening process in the PLC controller in the form of an array, such as the current timestamp, torque, angle, speed command, PID parameters, etc.

[0153] Step 4.7: Determine if tightening is complete. If complete, proceed to step 4.8; otherwise, return to step 4.1.

[0154] In this embodiment, tightening is completed and the servo motor stops when any of the following conditions are met:

[0155] (1) Torque error (e.g., ±1% of the target torque) and remain stable for more than 20ms. To set a threshold;

[0156] (2) Speed ​​command The torque drops to 0 and reaches more than 98% of the target value;

[0157] (3) Exceeding the maximum tightening time;

[0158] (4) Exceeding the maximum permissible angle.

[0159] Step 4.8: Filter the main variables (sampling rate ≥ 100Hz, practically up to 1000Hz) temporarily stored in the PLC controller for this tightening operation. Then, communicate with the host computer software using the UDP protocol and send the data to the host computer. Depending on the actual requirements, the data that can be sent includes: time, torque, angle, speed command, and actual speed. , , It can generate CSV files containing parameters such as error E and rate of change EC, produce torque-angle curves, and perform analyses such as pass / fail determination and feature extraction. Figure 2 As shown.

[0160] like Figure 3 As shown, compared with the patent of Huazhong University of Science and Technology, the present invention uses the multi-condition identification results for online adaptive adjustment instead of offline fixed parameters. Experiments show that the method of the present invention is robust to different nuts. When replacing the aircraft engine nuts of different specifications and different friction characteristics, the fuzzy controller can automatically adjust the PID parameters to the optimal value close to the new operating point. Experiments show that the torque accuracy is better than ±2%, which is significantly better than fixed parameter PID.

[0161] Example 2:

[0162] A fuzzy adaptive control system for tightening aircraft engine nuts, used to implement a fuzzy adaptive control method for tightening aircraft engine nuts, such as... Figure 4 As shown, it includes:

[0163] The host computer generates a CSV file from the main variables uploaded by the PLC controller for this tightening process, and also generates a torque-angle curve.

[0164] The PLC controller uses a data acquisition module to acquire the torque detected by an external torque sensor in real time; a parameter storage module to store the adjustment matrix and the current initial PID parameters; a quantization module to calculate and quantize the torque error and the rate of change of torque error; a lookup module to read the PID parameter adjustment from the adjustment matrix based on the quantized torque error and the rate of change of torque error, and update the PID parameters; a PID calculation module to calculate the speed command based on the updated PID parameters and send it to the tightening gun; and a data recording module to filter the main variables in this tightening operation and upload them to the host computer.

[0165] A tightening gun is used to tighten nuts on aircraft engines.

[0166] The tightening gun includes a tightening gun body, a servo driver, a servo motor, and a reducer;

[0167] The servo driver is used to drive the servo motor and the speed reducer.

[0168] The servo motor and reducer are used to control the tightening gun to tighten the aircraft engine nuts.

[0169] The PLC controller also includes a low-pass filter module for filtering torque errors; the PLC controller also includes a limiting module for limiting and protecting the updated PID parameters and speed commands.

[0170] Example 3:

[0171] This embodiment proposes an electronic device, including: one or more processors, and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the aforementioned fuzzy adaptive control method for tightening aircraft engine nuts.

[0172] The electronic device may be a mobile phone, computer, or tablet computer, etc., and includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements a fuzzy adaptive control method for tightening aircraft engine nuts as described in the embodiments. It is understood that the electronic device may also include an input / output (I / O) interface and communication components.

[0173] The processor is used to execute all or part of the steps in the fuzzy adaptive control method for tightening aircraft engine nuts as described in the above embodiments. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0174] The processor can be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the fuzzy adaptive control method for tightening aircraft engine nuts described in the above embodiments.

[0175] Example 4:

[0176] This embodiment proposes a computer-readable storage medium that stores executable instructions. When these instructions are executed, if they are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0177] The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the fuzzy adaptive control method for tightening aircraft engine nuts as described in the various embodiments of this application.

[0178] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory), random access memory (RAM), static random-access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, disk, optical disk, server, APP (Application) application store, and other media capable of storing program verification codes. These media store computer programs, which, when executed by a processor, can implement the various steps of the aforementioned fuzzy adaptive control method for tightening aircraft engine nuts.

[0179] Example 5:

[0180] This embodiment proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned fuzzy adaptive control method for tightening aircraft engine nuts.

[0181] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a computer program product.

[0182] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0183] The scope of protection of this application is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of this disclosure and its equivalents, then the intent of this disclosure also includes these modifications and variations.

Claims

1. A fuzzy adaptive control method for tightening nut in an aero-engine, characterized in that, Includes the following steps: Speed ​​commands sent by the PLC controller and torque values ​​fed back by external torque sensors are collected at multiple torque levels. Transfer function models for these multiple torque levels are identified and obtained. For each transfer function model, a set of optimal PID parameters for the PLC controller is optimized. The PID parameters include a proportional gain. Integral coefficient Differential coefficients ; Based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model, determine the base values ​​of the PID parameters and the domain of adjustment. For each PID parameter, an adjustment matrix is ​​constructed. Each matrix element represents the adjustment of the PID parameter under a set of torque errors and torque error change rates, and the matrix element values ​​are located within a defined adjustment domain. Online fuzzy adaptive control is performed based on the adjustment matrix and PID parameter base values ​​to obtain speed commands to control the tightening gun to tighten the aircraft engine nuts.

2. The fuzzy adaptive control method for tightening aero-engine nuts according to claim 1, characterized in that, The process involves identifying and acquiring transfer function models under multiple torque levels, and optimizing a set of optimal PID parameters for the PLC controller for each transfer function model. Specifically, this includes: A1: Collect speed commands sent by the PLC controller and torque values ​​fed back by the external torque sensor under multiple torque levels; Specifically, select within the working range of the tightening gun. Torque level , For the first For each torque level, multiple tightening tests were conducted using the same type of aircraft engine nut, with the speed command sent by the PLC controller serving as the input to the tightening gun. The torque value fed back by an external torque sensor is used as the output of the tightening gun. , Indicates time; A2: Identify the transfer function model for each torque level based on the speed command sent by the PLC controller and the torque value fed back by the external torque sensor. The transfer function model is as follows: （1）； in, For the first Laplace transform at a torque level The transfer function model of the domain. For gain, It is a time constant. For order; A3: For each torque level, the optimal PID parameters of the PLC controller are obtained by optimizing the transfer function model, including the proportional coefficient, integral coefficient and derivative coefficient; The fitness function of the optimization process is defined as: （2）； in, For the fitness function, This is the maximum score. The unit step fitness function response error, For overshoot, The maximum allowable overshoot, This is the penalty coefficient.

3. The fuzzy adaptive control method for tightening aircraft engine nuts according to claim 1, characterized in that, The step of determining the PID parameter base value and adjustment universe based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model specifically includes: B1: Determine the base value of the PID parameters based on the optimal PID parameters of a set of PLC controllers corresponding to each transfer function model; Specifically: The median or weighted average of multiple optimal PID parameters is used as the base value of the PID controller's PID parameters, including the proportional coefficient base value. Integral coefficient base value and differential coefficients parameter values ; B2: Calculate the maximum positive deviation ratio and the maximum negative deviation ratio of each optimal PID parameter relative to the corresponding PID parameter base value, and then determine the adjustment domain; （3）； （4）； （5）； in, , , These are the maximum positive deviation ratios of the proportional coefficient, integral coefficient, and derivative coefficient of the PID controller, respectively. , , These represent the maximum negative deviation ratio of the proportional coefficient, the maximum negative deviation ratio of the integral coefficient, and the maximum negative deviation ratio of the derivative coefficient of the PID controller, respectively. For each PID parameter, the one with the larger maximum positive deviation ratio and the larger maximum negative deviation absolute value is taken as the symmetric boundary of the adjustment domain, or the one with the larger maximum positive deviation ratio and the larger maximum negative deviation is taken as the asymmetric boundary of the adjustment domain.

4. The fuzzy adaptive control method for tightening aircraft engine nuts according to claim 1, characterized in that, The step involves constructing an adjustment matrix for each PID parameter. Each element in the matrix represents the PID parameter adjustment under a set of torque errors and torque error change rates, and the matrix element values ​​lie within a defined adjustment universe, specifically including: C1: Sets the level of torque error and torque error change rate; C2: Based on the set torque error and torque error change rate level, construct the adjustment matrix for each PID parameter.

5. The fuzzy adaptive control method for tightening aircraft engine nuts according to claim 1, characterized in that, The online fuzzy adaptive control based on the adjustment matrix and PID parameter base values, to obtain speed commands to control the tightening gun to tighten the aircraft engine nuts, specifically includes: D1: Read the torque setpoint in the current control cycle. Torque value from external torque sensor The torque error is calculated and a first-order low-pass filter is applied to obtain the low-pass filtered torque error. （6）； in, This is for torque error; D2: Calculate the torque error change rate based on the torque error after low-pass filtering; （7）； in, The rate of change of torque error. These are the filter coefficients. This represents the torque error from the previous control cycle. The sampling interval is... This represents the rate of change of torque error in the previous control cycle; D3: Based on the torque error after low-pass filtering and the rate of change of torque error, find the adjustment matrix and adjust the PID parameters; First, the torque error and the rate of change of torque error are linearly mapped to the torque error level index and the rate of change of torque error level index. Then read the PID parameter adjustment from the adjustment matrix: （8）； （9）； （10）； in, , and These are the adjustment matrices for the proportional coefficient, the integral coefficient, and the differential coefficient, respectively. Index for torque error change rate levels; For torque error level index; Finally, update the PID parameters based on the PID parameter adjustment amount and apply amplitude limiting protection. The method for updating PID parameters is as follows: （11）； （12）； （13）； D4: Calculate the control quantity using PID parameters. That is, speed command; （15）； D5: The control input is sent to the servo driver of the tightening gun via the bus protocol, which controls the servo motor and reducer to achieve tightening; D6: Temporarily store the variables used in this tightening operation in the PLC controller as an array; D7: Determine whether tightening is complete. If complete, execute D8; otherwise, return to D1. D8: Filter the variables temporarily stored in the PLC controller for this tightening process, then send them to the host computer to generate a CSV file and a torque-angle curve.

6. A fuzzy adaptive control system for tightening aircraft engine nuts, used to implement the fuzzy adaptive control method for tightening aircraft engine nuts as described in any one of claims 1-4, characterized in that, include: The host computer generates a CSV file from the variables uploaded by the PLC controller for this tightening process, and also generates a torque-angle curve. The PLC controller uses a data acquisition module to acquire the torque detected by an external torque sensor in real time; a parameter storage module to store the adjustment matrix and the current initial PID parameters; and a quantization module to calculate and quantize the torque error and the rate of change of torque error. The lookup module reads the PID parameter adjustment amount from the adjustment matrix based on the quantified torque error and the torque error change rate, and updates the PID parameters; the PID calculation module calculates the speed command based on the updated PID parameters and sends it to the tightening gun; the data recording module filters the variables temporarily stored in this tightening and uploads them to the host computer. A tightening gun is used to tighten nuts on aircraft engines.

7. A fuzzy adaptive control system for tightening aircraft engine nuts according to claim 6, characterized in that, The PLC controller also includes a low-pass filter module for filtering torque errors; the PLC controller also includes a limiting module for limiting and protecting the updated PID parameters and speed commands.

8. A fuzzy adaptive control system for tightening aircraft engine nuts according to claim 6, characterized in that, The tightening gun includes a tightening gun body, a servo driver, a servo motor, and a reducer; The servo driver is used to drive the servo motor and the speed reducer. The servo motor and reducer are used to control the tightening gun to tighten the aircraft engine nuts.

9. An electronic device, characterized in that, include: One or more processors, and a memory for storing instructions that, when executed by the one or more processors, cause the one or more processors to perform a fuzzy adaptive control method for tightening an aero-engine nut as described in any one of claims 1-4.

10. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a processor, implement the fuzzy adaptive control method for tightening nut of an aircraft engine as described in any one of claims 1-4.

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