An improved kalman filtering-based fct test motion control method, system, platform and storage medium

Abstract

This invention discloses a motion control method, system, platform, and storage medium for FCT testing based on improved Kalman filtering. The solution establishes a technical closed loop involving stage identification, configuration selection, fusion estimation, and closed-loop adjustment. This allows the same control framework to adaptively switch filtering strategies at different motion stages. Specifically, during the approach stage, motion state sensing data is used to ensure timely response; during the contact stage, pressure change rate feedforward correction suppresses dynamic overshoot; and during the pressure holding stage, contact state sensing data combined with closed-loop position compensation eliminates steady-state deviations. Thus, within a single pressure cycle, both high-speed approach and high-precision force control are achieved simultaneously. This solves the technical problem of balancing speed and accuracy in existing technologies and improves the system's robustness and adaptability to changing operating conditions while ensuring real-time control.

Description

Technical Field

[0001] This invention belongs to the field of electronic device functional testing technology, specifically relating to an FCT test motion control method, system, platform, and storage medium based on improved Kalman filtering. Background Technology

[0002] During FCT testing, the pressure control of the DUT (Device Under Test) is a core element in ensuring testing efficiency and accuracy. The test fixture must precisely press the DUT into the test station, ensuring both the initial pressure speed to improve testing efficiency and the pressure accuracy after contact to avoid damaging the DUT or affecting the test results.

[0003] Currently, traditional control methods typically employ single-sensor feedback or Kalman filter fusion schemes that are not optimized for the specific scenario. In the single-stroke sensor control method, rapid pressure reduction relies on the position signal from the stroke sensor, achieving a reduction speed of up to 0.8 seconds per cycle. However, this method cannot sense the contact pressure between the DUT and the test fixture, easily leading to damage such as DUT pin deformation and screen breakage due to overpressure, with a damage rate exceeding 5%. Furthermore, it cannot guarantee that the pressure reaches the preset value required for testing, making it difficult to ensure test accuracy.

[0004] For single-pressure sensor control, precise pressure control is achieved through the feedback signal of the pressure sensor, with an accuracy of ±1N. However, the initial pressing phase requires slow probing of the contact position, with a single pressing time of up to 3.2 seconds, resulting in extremely low testing efficiency and failing to meet the needs of large-scale mass production testing.

[0005] Furthermore, in traditional Kalman filter fusion schemes, traditional Kalman filters use a fixed parameter filtering fusion method to fuse stroke and pressure data, but they are not optimized for the stage characteristics of FCT pressure reduction. The process noise covariance Q and the measurement noise covariance R remain unchanged, resulting in insufficient noise suppression in the rapid pressure reduction stage and lag in response during the pressure control stage. The overshoot can reach more than 10%, making it impossible to meet the dual requirements of speed and accuracy.

[0006] Therefore, in view of the above-mentioned technical problems and defects, there is an urgent need to design and develop an FCT test motion control method, system, platform and storage medium based on improved Kalman filtering. Summary of the Invention

[0007] To overcome the shortcomings and difficulties of the existing technology, the purpose of this invention is to provide an FCT test motion control method, system, platform and storage medium based on improved Kalman filtering, so as to solve the technical problems of the existing DUT pressure motion control method in FCT testing, such as the inability to balance speed and accuracy, high DUT damage rate and poor adaptability of traditional Kalman filtering.

[0008] The first objective of this invention is to provide a motion control method for FCT testing based on improved Kalman filtering; the second objective of this invention is to provide a motion control system for FCT testing based on improved Kalman filtering; the third objective of this invention is to provide a motion control platform for FCT testing based on improved Kalman filtering; and the fourth objective of this invention is to provide a computer-readable storage medium.

[0009] The first objective of this invention is achieved by the method comprising:

[0010] The system generates and acquires first and second sensing data corresponding to the object under test during its motion; wherein the object under test is driven by an actuator; the first sensing data characterizes the motion state of the object under test; and the second sensing data characterizes the contact state of the object under test.

[0011] Based on at least one of the first sensing data or the second sensing data, the current motion stage corresponding to the measured object is identified and generated, and a filter configuration matching the current motion stage is established; wherein, the filter configuration includes a state vector and a noise covariance matrix, and at least one of the filter configurations corresponding to different motion stages is different;

[0012] Based on the filtering configuration, the first sensor data and the second sensor data are fused and processed to generate a fused state estimate corresponding to the object under test;

[0013] Based on the estimated fusion state value, the actuator is controlled to drive the object under test to move, so as to adjust the position or contact force of the object under test.

[0014] The second objective of this invention is achieved by providing a system for implementing the FCT test motion control method based on improved Kalman filtering, the system comprising:

[0015] A generation unit is created to generate and acquire first and second sensing data corresponding to the object under test during its motion; wherein the object under test is driven by an actuator; the first sensing data is data characterizing the motion state of the object under test; and the second sensing data is data characterizing the contact state of the object under test.

[0016] A selected unit is established for identifying and constructing a current motion stage corresponding to the measured object based on at least one of the first sensing data or the second sensing data, and establishing a selected filtering configuration that matches the current motion stage; wherein, the filtering configuration includes a state vector and a noise covariance matrix, and at least one of the filtering configurations corresponding to different motion stages is different;

[0017] The fusion processing unit is used to combine the filtering configuration, fuse the first sensing data and the second sensing data, and generate a fusion state estimate corresponding to the object under test.

[0018] A control drive unit is used to control the actuator to drive the movement of the object under test according to the fusion state estimate, so as to adjust the position or contact force of the object under test.

[0019] The third objective of this invention is achieved as follows: it includes a processor, a memory, and a control program for an FCT test motion control platform based on an improved Kalman filter; wherein the processor executes the control program for the FCT test motion control platform based on an improved Kalman filter, the control program for the FCT test motion control platform based on an improved Kalman filter is stored in the memory, and the control program for the FCT test motion control platform based on an improved Kalman filter implements the FCT test motion control method based on an improved Kalman filter.

[0020] The fourth objective of this invention is achieved as follows: the computer-readable storage medium stores a control program for an FCT test motion control platform based on an improved Kalman filter, and the control program for the FCT test motion control platform based on an improved Kalman filter implements the FCT test motion control method based on an improved Kalman filter.

[0021] The present invention generates and acquires first and second sensing data corresponding to a test object during its motion process through a method; wherein the test object is driven by an actuator; the first sensing data characterizes the motion state of the test object; the second sensing data characterizes the contact state of the test object; then, based on at least one of the first or second sensing data, a current motion stage corresponding to the test object is identified and generated, and a filter configuration matching the current motion stage is established; wherein the filter configuration includes a state vector and a noise covariance matrix, and different motion stages correspond to... At least one of the filtering configurations is different; then, in combination with the filtering configuration, the first sensing data and the second sensing data are fused and processed to generate a fused state estimate corresponding to the object under test; finally, based on the fused state estimate, the actuator is controlled to drive the object under test to move, so as to adjust the position or contact force of the object under test; and the system, platform and storage medium corresponding to the method, based on the fused state estimate, control the actuator to adjust the position or contact force of the object under test, thereby adaptively fusing motion state data and contact state data for closed-loop adjustment at different motion stages, so as to balance the pressing speed and control accuracy.

[0022] In other words, the present invention establishes a technical closed loop of stage identification, configuration selection, fusion estimation, and closed-loop adjustment, enabling the same control framework to adaptively switch filtering strategies in different motion stages. Specifically, in the approach stage, motion state sensing data is used as the primary driver to ensure timely response; in the contact stage, dynamic overshoot is suppressed through pressure change rate feedforward correction; and in the pressure holding stage, contact state sensing data is used as the core, combined with closed-loop position compensation to eliminate steady-state deviation. Thus, the dual objectives of high-speed approach and high-precision force control are achieved simultaneously within a single pressure cycle, solving the technical problem of the inability to balance speed and accuracy in the prior art. Furthermore, it improves the robustness and adaptability to changes in operating conditions while ensuring real-time control. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the process steps of an FCT test motion control method based on an improved Kalman filter according to the present invention.

[0025] Figure 2 This is a schematic diagram of the FCT test motion control system architecture based on improved Kalman filtering according to the present invention.

[0026] Figure 3 This is a schematic diagram of an FCT test motion control platform architecture based on an improved Kalman filter according to the present invention.

[0027] Figure 4 This is a schematic diagram of a computer-readable storage medium architecture in one embodiment of the present invention. Detailed Implementation

[0028] To facilitate a clearer understanding of the objectives, technical solutions, and advantages of this invention, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of this invention from the content disclosed in this specification.

[0029] This invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of this invention.

[0030] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0031] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0032] Preferably, the FCT test motion control method based on improved Kalman filtering of the present invention is applied in one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0033] The terminal can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal can interact with the customer via a keyboard, mouse, remote control, touchpad, or voice control device.

[0034] This invention provides a motion control method, system, platform, and storage medium for FCT testing based on an improved Kalman filter.

[0035] like Figure 1 The diagram shown is a flowchart of the FCT test motion control method based on improved Kalman filtering provided in an embodiment of the present invention.

[0036] In this embodiment, the FCT test motion control method based on improved Kalman filtering can be applied to terminals with display functions or fixed terminals. The terminals are not limited to personal computers, smartphones, tablets, desktop computers or all-in-one computers with cameras, etc.

[0037] The FCT test motion control method based on improved Kalman filtering can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The FCT test motion control method based on improved Kalman filtering in this embodiment can be executed by the server, by the terminal, or by both the server and the terminal.

[0038] For example, for a motion control terminal requiring FCT testing based on improved Kalman filtering, the motion control function for FCT testing based on improved Kalman filtering provided by the method of this invention can be directly integrated into the terminal, or a client for implementing the method of this invention can be installed. Alternatively, the method provided by this invention can also run on servers or other devices in the form of a Software Development Kit (SDK), providing an interface for the motion control function for FCT testing based on improved Kalman filtering. Terminals or other devices can then implement the motion control function for FCT testing based on improved Kalman filtering through the provided interface. The invention will be further described below with reference to the accompanying drawings.

[0039] like Figure 1 As shown, this invention provides a motion control method for FCT testing based on improved Kalman filtering. The method includes the following steps:

[0040] S01. Create, generate, and acquire first and second sensing data corresponding to the object under test during the motion process; wherein, the object under test is driven by an actuator; the first sensing data is data characterizing the motion state of the object under test; the second sensing data is data characterizing the contact state of the object under test;

[0041] S02. Based on at least one of the first sensing data or the second sensing data, identify and construct the current motion stage corresponding to the measured object, and establish a filter configuration that matches the current motion stage; wherein, the filter configuration includes a state vector and a noise covariance matrix, and at least one of the filter configurations corresponding to different motion stages is different;

[0042] S03. Combining the filtering configuration, the first sensing data and the second sensing data are fused and processed to generate a fused state estimate corresponding to the object under test.

[0043] S04. Based on the estimated fusion state value, control the actuator to drive the object under test to move, so as to adjust the position or contact force of the object under test.

[0044] The motion phases include a first motion phase and a second motion phase;

[0045] In the first motion phase, the state vector includes position state quantities, and the value of the position-related component in the noise covariance matrix is ​​greater than the value of the position-related component in the noise covariance matrix in the second motion phase.

[0046] In the second motion phase, the state vector includes a pressure state quantity or a pressure change rate state quantity, and the value of the pressure-related component in the noise covariance matrix is ​​less than the value of the pressure-related component in the noise covariance matrix in the first motion phase.

[0047] The movement phase also includes a third movement phase;

[0048] In the third motion stage, the state vector is dominated by pressure state variables, and the value of the pressure-related component in the noise covariance matrix is ​​less than the value of the pressure-related component in the noise covariance matrix in the second motion stage.

[0049] The pressure measurement noise covariance has a first value in the first motion phase and gradually decreases from the first value to a second value in the second motion phase based on the pressure value in the second sensing data; wherein the second value is less than the first value.

[0050] The position process noise covariance is a third value in the first motion phase, and gradually decreases from the third value to a fourth value in the second motion phase based on the pressure value in the second sensing data, where the fourth value is less than the third value.

[0051] The method further includes:

[0052] S051. During the second motion phase, a corresponding pressure change rate is generated and obtained based on the second sensing data;

[0053] S052. When the pressure change rate is greater than a threshold, a corresponding feedforward control quantity is generated based on the pressure change rate; wherein, the feedforward control quantity is used to correct the motion command of the actuator.

[0054] The method further includes:

[0055] S061. In the third motion stage, a corresponding position compensation amount is generated based on the deviation between the pressure estimate and the target pressure value; wherein, the position compensation amount is used to adjust the position of the actuator.

[0056] Specifically, in this embodiment of the invention, an improved Kalman filter algorithm and system are provided. Targeting the phased characteristics of rapid approach, precise contact, and stable pressure holding during the DUT's downward pressure motion in FCT testing, the filter parameters and sensor weights are dynamically adjusted to achieve an optimal balance between speed and accuracy. Its main core technologies include phased state vector design, dynamic noise covariance adjustment, adaptive sensor weight allocation, and overshoot suppression mechanism.

[0057] The phased state vector design, based on the different pressure stages, designs differentiated state vectors, focusing on the stroke state in the early stages and the pressure state in the later stages, achieving targeted fusion of sensor data. Dynamic noise covariance adjustment automatically adjusts the process noise covariance (Q) and measurement noise covariance (R) based on real-time pressure values ​​and stroke position, optimizing the Kalman gain (K) to balance response speed and control accuracy at different stages. Adaptive sensor weight allocation dynamically adjusts the weight ratio of stroke and pressure sensors at different pressure stages, prioritizing stroke data to ensure speed in the early stages and pressure data to ensure accuracy in the later stages, achieving a smooth transition. An overshoot suppression mechanism predicts pressure change trends during the contact stage and adjusts control commands in advance, keeping pressure overshoot within 5% to prevent DUT damage.

[0058] Furthermore, regarding the principle of the improved Kalman filter algorithm, the traditional Kalman filter achieves state estimation through a "prediction-update" loop, with core formulas including the prediction equation, update equation, and Kalman gain calculation. This invention improves the traditional Kalman filter for FCT downcompression scenarios by introducing stage parameters and a dynamic adjustment mechanism. The specific principle is as follows:

[0059] The core formula of traditional Kalman filtering: The state estimation of traditional Kalman filtering is divided into two stages: prediction and update. The core formula is as follows:

[0060] State prediction equation:

[0061] (1)

[0062] In the formula, Let be the predicted state vector at time k. Here is the state transition matrix. Let be the optimal estimated state vector at time k-1. To control the input matrix, This is the control input at time k-1.

[0063] Covariance prediction equation:

[0064] (2)

[0065] In the formula, is the covariance matrix of the predicted state at time k, is the covariance matrix of the optimal estimated state at time k-1, is the process noise covariance matrix.

[0066] Kalman gain calculation:

[0067] (3)

[0068] In the formula, is the Kalman gain, is the observation matrix, is the measurement noise covariance matrix.

[0069] State update equation:

[0070] (4)

[0071] In the formula, is the optimal estimated state vector at time k, is the measurement value vector at time k.

[0072] Covariance update equation:

[0073] (5)

[0074] In the formula, is the identity matrix.

[0075] For the stage adjustment mechanism of the improved Kalman filter, the proposed solution of this invention divides the FCT pressing movement into three stages: the rapid approach stage, the precise contact stage, and the stable pressure maintaining stage, and designs different filtering parameters and state vectors according to the characteristics of each stage:

[0076] In the rapid approach stage (pressure value P < P1, where P1 is the contact threshold, usually set to 5N), the core goal of this stage is to quickly move the DUT to a position close to the test station. The data of the travel sensor is the main guide, and the data of the pressure sensor is only used to assist in judging the contact state. For the design of the state vector, the state vector , where s is the travel position, v is the pressing speed, and a is the pressing acceleration. For the process noise covariance Q, a relatively large Q value ( ) is set to allow large changes in the state and ensure a rapid response. For the measurement noise covariance R, the noise covariance of the travel sensor , and the noise covariance of the pressure sensor , to reduce the weight of the pressure data. For the adjustment of the Kalman gain, by increasing , the Kalman gain is tilted towards the travel data to ensure the pressing speed.

[0077] Precise contact stage (pressure value P1 ≤ P < P2, where P2 is the pressure holding threshold, usually set to 90% of the preset pressure). The core goal of this stage is to precisely control the contact process, avoid pressure overshoot, and gradually switch from stroke-dominated to pressure-dominated. For the state vector design, the state vector , where P is the pressure value and F is the pressure change rate, integrating the stroke and pressure states. The process noise covariance Q is dynamically adjusted. As the pressure approaches P2, it is gradually decreased , , and , are increased. The formula is as follows:

[0078] (6)

[0079] Measurement noise covariance R: The value of R is dynamically adjusted, gradually decreasing , and increasing . The formula is as follows:

[0080] (7)

[0081] Overshoot suppression mechanism: Predict the pressure trend through the pressure change rate F. When F > 0.5 N / ms, reduce the downward pressure speed in advance. The formula is as follows:

[0082] (8)

[0083] For the stable pressure holding stage (pressure value P ≥ P2), the core goal of this stage is to maintain the pressure stable at the preset value, dominated by the pressure sensor data, and the stroke sensor data is used to assist in adjusting the position. For the state vector design, the state vector , with the pressure state as the core. The process noise covariance Q is set to a relatively small ( ), to ensure the stability of the pressure state, , allowing for a small adjustment of the position. The measurement noise covariance R, the pressure sensor noise covariance , and the stroke sensor noise covariance . Increase the weight of the pressure data. For the pressure closed-loop control, adjust the downward pressure position according to the deviation between the optimally estimated pressure value and the preset value. The formula is as follows:

[0084] (9)

[0085] In the formula, is the proportionality coefficient, is the integral coefficient, is the preset pressure value, is the optimally estimated pressure value.

[0086] In the improved Kalman filter system architecture, the improved Kalman filter system of this invention consists of three parts: a hardware layer, an algorithm layer, and a control layer. The specific architecture is as follows:

[0087] The hardware layer includes a stroke sensor (accuracy ±0.01mm), a pressure sensor (accuracy ±0.1N), a servo motor driver, and an industrial control computer (IPC). The stroke sensor is mounted on the servo motor shaft end to collect the pressing position in real time; the pressure sensor is mounted on the pressure head of the test fixture to collect the contact pressure in real time; the IPC is responsible for data acquisition, algorithm calculation, and control command output.

[0088] The algorithm layer includes a data preprocessing module, a stage determination module, an improved Kalman filter module, and a parameter dynamic adjustment module. The data preprocessing module filters and denoises the sensor data; the stage determination module determines the current pressure stage based on the real-time pressure value; the improved Kalman filter module estimates the state based on stage characteristics; and the parameter dynamic adjustment module updates Q, R, and Kalman gain in real time.

[0089] The control layer includes a speed control module and a pressure control module. The speed control module dominates the rapid approach phase, adjusting the servo motor speed based on stroke data; the pressure control module dominates the precise contact and stable pressure holding phase, adjusting the servo motor position based on the optimal estimated pressure value to achieve closed-loop control.

[0090] In setting up the experimental environment for this invention, an FCT test platform was built to verify the effectiveness of the improved Kalman filter algorithm. The specific parameters are as follows:

[0091] The test subject is a motherboard of a certain brand of smartphone, with a preset downward pressure of 20N and an allowable error of ±1N.

[0092] Hardware equipment includes a Panasonic MHMF042L1U2M servo motor, an Omron E2B-M18KN16-WZ-C1 stroke sensor, a KELI PST-50 pressure sensor, and an Advantech IPC-610 industrial control computer.

[0093] Experimental parameters: contact threshold P1 = 5N, holding pressure threshold P2 = 18N, rapid approach speed = 100mm / s, precise contact speed = 20mm / s, stable holding pressure speed = 5mm / s.

[0094] The specific experimental steps are as follows:

[0095] Fix the smartphone motherboard to the test station, set the preset pressure to 20N, and start the FCT test system.

[0096] The servo motor drives the test fixture to press down rapidly, the stroke sensor collects position data in real time, the pressure sensor collects pressure data in real time, and the data is transmitted to the IPC.

[0097] The stage determination module determines the current stage based on the real-time pressure value: when P < 5N, it enters the rapid approach stage, calling the five basic Kalman iteration formulas and fixing the state vector. Fixed initial noise parameters , Dynamic adjustment formulas and pressure closed-loop control are not enabled; full-speed operation speed loop control is used to match high-speed descent requirements. When 5N≤P<18N, the precise contact stage is entered, and the state vector is automatically expanded. The linear dynamic Q and R adjustment formula of this invention is activated to continuously and smoothly correct the noise matrix as the pressure rises; the pressure change rate F is calculated synchronously in real time, and when F > 0.5 N / ms, the speed feedforward correction formula is automatically invoked to reduce the servo running speed in advance and actively suppress pressure spikes; when P ≥ 18 N, the system enters the stable pressure holding stage, and the state vector is reconstructed. Locking low differential pressure noise parameters The pressure PI position compensation formula is used throughout the process to eliminate pressure drift caused by equipment vibration and workpiece deformation, and achieve steady-state pressure stabilization.

[0098] The improved Kalman filter module estimates the state based on the current state vector and parameters through two core stages: prediction and update, outputting optimal travel and pressure values. In the prediction stage, the module uses the optimal estimate from the previous time step to predict the current state based on the system model and updates the error covariance matrix to reflect the prediction uncertainty. The state vector contains key parameters such as travel, pressure, and motor current, while the system matrix describes the dynamic relationships between these parameters. In the update stage, the module fuses the predicted values ​​with the actual observed values, calculates the Kalman gain, weighs the reliability of the predicted and observed values, and then corrects the state estimation results to obtain the optimal travel and pressure values. The observation matrix establishes the mapping relationship between observed values ​​and state values, while the noise covariance matrix quantifies the uncertainty of the system model and sensor measurements, providing a basis for calculating the Kalman gain. The following are specific parameter examples and calculation processes:

[0099] 1. State vector: ;

[0100] 2. System matrix: This indicates the dynamic relationship between stroke, pressure, and motor current;

[0101] 3. Observation matrix: This indicates that the observations only include travel and pressure;

[0102] 4. Process noise covariance matrix: This indicates the uncertainty of the system model;

[0103] 5. Measurement noise covariance matrix: This indicates the calculation of the uncertainty prediction stage of sensor measurements:

[0104] Predicted status: ;

[0105] Prediction error covariance: ;

[0106] Update phase calculations:

[0107] Kalman gain: ;

[0108] Update status: In the formula, For the observed values; update the error covariance: ;

[0109] The control layer adjusts the speed and position of the servo motor using a PID control algorithm based on the optimal estimate to complete the pressure reduction and holding process. In the PID control algorithm, the proportional term directly adjusts the motor output based on the current error, the integral term eliminates steady-state error, and the derivative term predicts the error change trend and adjusts the motor output in advance to improve the system's response speed and stability. Specific parameter settings are as follows:

[0110] The proportional gain (Kp) is 8, the integral gain (Ki) is 0.5, and the derivative gain (Kd) is 2. The calculation formula for the PID control algorithm is as follows:

[0111] Proportional term:

[0112] (10)

[0113] In the formula, This is the current error;

[0114] Integral term:

[0115] (11)

[0116] In the formula, This is the cumulative sum of errors;

[0117] Differential term:

[0118] (12)

[0119] In the formula, This is the error from the previous time;

[0120] Total control output:

[0121] (13)

[0122] During the downward pressure process, the control layer adjusts the servo motor speed in real time based on the optimal estimate of the stroke, enabling the actuator to quickly and accurately reach the target position. For example, if the target position is 100mm and the current position is 20mm, the error... proportional term Integral term Differential term Total control output The control layer adjusts the servo motor speed based on this output, causing the actuator to move rapidly towards the target position. During pressure holding, the control layer fine-tunes the servo motor position based on the optimal pressure estimate, ensuring the pressure remains stable within the set range. For example, if the set pressure is 10N and the current pressure is 9.5N, the error... proportional term Integral term Differential term Total control output The control layer then fine-tunes the position of the servo motor based on this output, gradually stabilizing the pressure within the set range. Through this closed-loop control method, the system can precisely complete the pressing and holding processes, improving molding quality and production efficiency.

[0123] Repeat the experiment 100 times, record the time, pressure accuracy and overshoot for each press, and calculate the average value.

[0124] In traditional fixed-parameter Kalman filter fusion schemes, the control logic and parameter configuration are synchronously connected to both stroke and pressure sensors. The five basic formulas of standard Kalman filtering are used, and the Q and R matrix parameters remain fixed throughout the entire process: [unified settings]. , It does not perform state vector segmentation switching, does not enable dynamic noise adjustment formula, has no pressure change rate feedforward overshoot suppression, and has no PI pressure compensation. It uses the same set of filter parameters to complete the entire process of down-pressure.

[0125] Actual performance results: total duration of single pressure application is 1.5s, steady-state pressure error is ±5N, pressure peak overshoot is 10%, and workpiece damage rate is 2.1% per 100 tests.

[0126] The root cause of the defect is that the fixed Q and R parameters cannot adapt to the three-stage variable operating conditions. The high-speed section has poor noise suppression and the contact section has lag in response. This directly confirms the necessity of the dynamic parameter adjustment and segmented state vector improvement of this invention, which corresponds to the third core improvement pain point of this invention.

[0127] Experimental Results and Analysis: Experimental results show that the improved algorithm of this invention has significant advantages over traditional control methods and traditional Kalman filtering. Specific data are as follows:

[0128] Table 1 Performance Comparison of Different Control Methods

[0129] Single-stroke sensor control 0.8 5 15 5.2 Single pressure sensor control 3.2 1 2 0.1 Traditional Kalman filter fusion 1.5 5 10 2.1 This invention improves Kalman filtering. 0.9 1 3 0.08

[0130] Experimental data shows that, in terms of compression speed, the improved algorithm of this invention achieves a single compression time of only 0.9s, close to the 0.8s of a single stroke sensor, and significantly faster than the 3.2s of a single pressure sensor and the 1.5s of traditional Kalman filtering, resulting in a test efficiency improvement of over 30%. Regarding pressure accuracy, the improved algorithm achieves a pressure control accuracy of ±1N, comparable to a single pressure sensor and far superior to the ±5N of a single stroke sensor and the ±5N of traditional Kalman filtering, meeting the accuracy requirements of FCT testing. In terms of overshoot, the improved algorithm achieves a pressure overshoot of only 3%, far lower than the 15% of a single stroke sensor and the 10% of traditional Kalman filtering, effectively preventing DUT damage. Finally, the improved algorithm achieves a DUT damage rate of only 0.08%, far lower than the 5.2% of a single stroke sensor and the 2.1% of traditional Kalman filtering, reducing test costs.

[0131] Compared to existing technologies, this invention offers several advantages for phased adaptive control. It employs differentiated filtering strategies for the three stages of FCT pressure reduction, dynamically adjusting state vectors and parameters to balance the dual requirements of rapid initial pressure reduction and precise pressure holding in the later stages, thus resolving the speed-accuracy contradiction inherent in traditional control methods. In the deep fusion of multiple sensors, an improved Kalman filter algorithm achieves optimal fusion of stroke and pressure sensor data, dynamically adjusting sensor weights at different stages to ensure both rapid response and improved control accuracy. For dynamic noise covariance adjustment, the process noise covariance Q and measurement noise covariance R are automatically adjusted based on real-time pressure values, overcoming the poor adaptability problem caused by fixed noise in traditional Kalman filters and improving algorithm robustness. In the overshoot suppression mechanism, pressure trends are predicted using the pressure change rate, allowing for early adjustment of control commands, effectively reducing pressure overshoot, preventing DUT damage, and improving test reliability. Furthermore, this invention boasts strong hardware compatibility, adapting to the hardware architecture of existing FCT testing equipment without large-scale modifications, requiring only software updates, resulting in low implementation costs and easy widespread application.

[0132] In summary, the improved Kalman filter algorithm and system for motion control in FCT testing proposed in this invention successfully resolves the speed and accuracy contradiction in DUT pressure motion control during FCT testing through staged state vector design, dynamic noise covariance adjustment, adaptive sensor weight allocation, and overshoot suppression mechanisms. Experimental results show that the algorithm achieves high-precision pressure control while ensuring rapid pressure reduction, possessing significant theoretical and engineering application value, and can be widely applied in the FCT testing of various electronic devices.

[0133] To achieve the above objectives, the present invention also provides an FCT test motion control system based on an improved Kalman filter, such as... Figure 2 As shown, the system is applied to the FCT test motion control method based on improved Kalman filtering, and the system includes:

[0134] A generation unit is created to generate and acquire first and second sensing data corresponding to the object under test during its motion; wherein the object under test is driven by an actuator; the first sensing data is data characterizing the motion state of the object under test; and the second sensing data is data characterizing the contact state of the object under test.

[0135] A selected unit is established for identifying and constructing a current motion stage corresponding to the measured object based on at least one of the first sensing data or the second sensing data, and establishing a selected filtering configuration that matches the current motion stage; wherein, the filtering configuration includes a state vector and a noise covariance matrix, and at least one of the filtering configurations corresponding to different motion stages is different;

[0136] The fusion processing unit is used to combine the filtering configuration, fuse the first sensing data and the second sensing data, and generate a fusion state estimate corresponding to the object under test.

[0137] A control drive unit is used to control the actuator to drive the movement of the object under test according to the fusion state estimate, so as to adjust the position or contact force of the object under test.

[0138] Specifically, the motion phase includes a first motion phase and a second motion phase;

[0139] In the first motion phase, the state vector includes position state quantities, and the value of the position-related component in the noise covariance matrix is ​​greater than the value of the position-related component in the noise covariance matrix in the second motion phase.

[0140] In the second motion phase, the state vector includes a pressure state quantity or a pressure change rate state quantity, and the value of the pressure-related component in the noise covariance matrix is ​​less than the value of the pressure-related component in the noise covariance matrix in the first motion phase.

[0141] The movement phase also includes a third movement phase;

[0142] In the third motion stage, the state vector is dominated by pressure state variables, and the value of the pressure-related component in the noise covariance matrix is ​​less than the value of the pressure-related component in the noise covariance matrix in the second motion stage.

[0143] The pressure measurement noise covariance has a first value in the first motion phase and gradually decreases from the first value to a second value in the second motion phase based on the pressure value in the second sensing data; wherein the second value is less than the first value.

[0144] The position process noise covariance is a third value in the first motion phase, and gradually decreases from the third value to a fourth value in the second motion phase based on the pressure value in the second sensing data, where the fourth value is less than the third value.

[0145] Furthermore, in the system, the system also includes:

[0146] A construction module is used in the second motion phase to construct, generate, and acquire the corresponding pressure change rate based on the second sensing data.

[0147] A creation module is used to generate a corresponding feedforward control quantity based on the pressure change rate when the pressure change rate is greater than a threshold; wherein the feedforward control quantity is used to correct the motion command of the actuator.

[0148] The system also includes:

[0149] A module is established to generate a corresponding position compensation amount based on the deviation between the pressure estimate and the target pressure value during the third motion phase; wherein the position compensation amount is used to adjust the position of the actuator.

[0150] In the system embodiment of the present invention, the specific details of the method steps involved in the FCT test motion control based on improved Kalman filtering have been described above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, and will not be repeated here.

[0151] To achieve the above objectives, the present invention also provides an FCT test motion control platform based on an improved Kalman filter, such as... Figure 3As shown, it includes a processor, a memory, and a control program for an FCT test motion control platform based on an improved Kalman filter; wherein, the processor executes the control program for the FCT test motion control platform based on the improved Kalman filter, and the control program for the FCT test motion control platform based on the improved Kalman filter is stored in the memory. The control program for the FCT test motion control platform based on the improved Kalman filter implements the steps of the FCT test motion control method based on the improved Kalman filter, for example:

[0152] S01. Create, generate, and acquire first and second sensing data corresponding to the object under test during the motion process; wherein, the object under test is driven by an actuator; the first sensing data is data characterizing the motion state of the object under test; the second sensing data is data characterizing the contact state of the object under test;

[0153] S02. Based on at least one of the first sensing data or the second sensing data, identify and construct the current motion stage corresponding to the measured object, and establish a filter configuration that matches the current motion stage; wherein, the filter configuration includes a state vector and a noise covariance matrix, and at least one of the filter configurations corresponding to different motion stages is different;

[0154] S03. Combining the filtering configuration, the first sensing data and the second sensing data are fused and processed to generate a fused state estimate corresponding to the object under test.

[0155] S04. Based on the estimated fusion state value, control the actuator to drive the object under test to move, so as to adjust the position or contact force of the object under test.

[0156] The specific details of the steps have been explained above and will not be repeated here.

[0157] In this embodiment of the invention, the built-in processor of the FCT test motion control platform based on improved Kalman filtering can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor connects to various components using various interfaces and lines, and executes programs or units stored in memory, as well as calling data stored in memory, to perform various functions of FCT test motion control based on improved Kalman filtering and process data.

[0158] The memory is used to store program code and various data. It is installed in the FCT test motion control platform based on improved Kalman filtering and enables high-speed, automatic access to programs or data during operation. The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0159] To achieve the above objectives, the present invention also provides a computer-readable storage medium, such as... Figure 4 As shown, the computer-readable storage medium stores a control program for an FCT test motion control platform based on an improved Kalman filter. This control program implements the steps of the FCT test motion control method based on an improved Kalman filter; for example:

[0160] S01. Create, generate, and acquire first and second sensing data corresponding to the object under test during the motion process; wherein, the object under test is driven by an actuator; the first sensing data is data characterizing the motion state of the object under test; the second sensing data is data characterizing the contact state of the object under test;

[0161] S02. Based on at least one of the first sensing data or the second sensing data, identify and construct the current motion stage corresponding to the measured object, and establish a filter configuration that matches the current motion stage; wherein, the filter configuration includes a state vector and a noise covariance matrix, and at least one of the filter configurations corresponding to different motion stages is different;

[0162] S03. Combining the filtering configuration, the first sensing data and the second sensing data are fused and processed to generate a fused state estimate corresponding to the object under test.

[0163] S04. Based on the estimated fusion state value, control the actuator to drive the object under test to move, so as to adjust the position or contact force of the object under test.

[0164] The specific details of the steps have been explained above and will not be repeated here.

[0165] In the description of embodiments of the present invention, it should be noted that any process or method description in the flowcharts or otherwise described herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0166] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, a “computer-readable medium” can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0167] The present invention generates and acquires first and second sensing data corresponding to a test object during its motion process through a method; wherein the test object is driven by an actuator; the first sensing data characterizes the motion state of the test object; the second sensing data characterizes the contact state of the test object; then, based on at least one of the first or second sensing data, a current motion stage corresponding to the test object is identified and generated, and a filter configuration matching the current motion stage is established; wherein the filter configuration includes a state vector and a noise covariance matrix, and different motion stages correspond to... At least one of the filtering configurations is different; then, in combination with the filtering configuration, the first sensing data and the second sensing data are fused and processed to generate a fused state estimate corresponding to the object under test; finally, based on the fused state estimate, the actuator is controlled to drive the object under test to move, so as to adjust the position or contact force of the object under test; and the system, platform and storage medium corresponding to the method, based on the fused state estimate, control the actuator to adjust the position or contact force of the object under test, thereby adaptively fusing motion state data and contact state data for closed-loop adjustment at different motion stages, so as to balance the pressing speed and control accuracy.

[0168] In other words, the present invention establishes a technical closed loop of stage identification, configuration selection, fusion estimation, and closed-loop adjustment, enabling the same control framework to adaptively switch filtering strategies in different motion stages. Specifically, in the approach stage, motion state sensing data is used as the primary driver to ensure timely response; in the contact stage, dynamic overshoot is suppressed through pressure change rate feedforward correction; and in the pressure holding stage, contact state sensing data is used as the core, combined with closed-loop position compensation to eliminate steady-state deviation. Thus, the dual objectives of high-speed approach and high-precision force control are achieved simultaneously within a single pressure cycle, solving the technical problem of the inability to balance speed and accuracy in the prior art. Furthermore, it improves the robustness and adaptability to changes in operating conditions while ensuring real-time control.

[0169] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A motion control method for FCT testing based on improved Kalman filtering, characterized in that, The method includes: The system generates and acquires first and second sensing data corresponding to the object under test during its motion; wherein the object under test is driven by an actuator; the first sensing data characterizes the motion state of the object under test; and the second sensing data characterizes the contact state of the object under test. Based on at least one of the first sensing data or the second sensing data, the current motion stage corresponding to the measured object is identified and generated, and a filter configuration matching the current motion stage is established; wherein, the filter configuration includes a state vector and a noise covariance matrix, and at least one of the filter configurations corresponding to different motion stages is different; Based on the filtering configuration, the first sensor data and the second sensor data are fused and processed to generate a fused state estimate corresponding to the object under test; Based on the estimated fusion state value, the actuator is controlled to drive the measured object to move.

2. The FCT test motion control method based on improved Kalman filtering according to claim 1, characterized in that, The motion phases include a first motion phase and a second motion phase; In the first motion phase, the state vector includes position state quantities, and the value of the position-related component in the noise covariance matrix is ​​greater than the value of the position-related component in the noise covariance matrix in the second motion phase. In the second motion phase, the state vector includes a pressure state quantity or a pressure change rate state quantity, and the value of the pressure-related component in the noise covariance matrix is ​​less than the value of the pressure-related component in the noise covariance matrix in the first motion phase.

3. The FCT test motion control method based on improved Kalman filtering according to claim 2, characterized in that, The movement phase also includes a third movement phase; In the third motion stage, the state vector is dominated by pressure state variables, and the value of the pressure-related component in the noise covariance matrix is ​​less than the value of the pressure-related component in the noise covariance matrix in the second motion stage.

4. The FCT test motion control method based on improved Kalman filtering according to claim 2, characterized in that, The pressure measurement noise covariance has a first value in the first motion phase and gradually decreases from the first value to a second value in the second motion phase based on the pressure value in the second sensing data; wherein the second value is less than the first value.

5. The FCT test motion control method based on improved Kalman filtering according to claim 2, characterized in that, The position process noise covariance is a third value in the first motion phase, and gradually decreases from the third value to a fourth value in the second motion phase based on the pressure value in the second sensing data, where the fourth value is less than the third value.

6. The FCT test motion control method based on improved Kalman filtering according to claim 2, characterized in that, The method further includes: During the second motion phase, the corresponding pressure change rate is generated and acquired based on the second sensing data; When the pressure change rate is greater than a threshold, a corresponding feedforward control quantity is generated based on the pressure change rate; wherein, the feedforward control quantity is used to correct the motion command of the actuator.

7. The FCT test motion control method based on improved Kalman filtering according to claim 2, characterized in that, The method further includes: In the third motion phase, a corresponding position compensation amount is generated based on the deviation between the pressure estimate and the target pressure value; wherein, the position compensation amount is used to adjust the position of the actuator.

8. A motion control system for FCT testing based on improved Kalman filtering, characterized in that, The system is applied to the FCT test motion control method based on improved Kalman filtering as described in any one of claims 1 to 7, and the system comprises: A generation unit is created to generate and acquire first and second sensing data corresponding to the object under test during its motion; wherein the object under test is driven by an actuator; the first sensing data is data characterizing the motion state of the object under test; and the second sensing data is data characterizing the contact state of the object under test. A selected unit is established for identifying and constructing a current motion stage corresponding to the measured object based on at least one of the first sensing data or the second sensing data, and establishing a selected filtering configuration that matches the current motion stage; wherein, the filtering configuration includes a state vector and a noise covariance matrix, and at least one of the filtering configurations corresponding to different motion stages is different; The fusion processing unit is used to combine the filtering configuration, fuse the first sensing data and the second sensing data, and generate a fusion state estimate corresponding to the object under test. A control drive unit is used to control the actuator to drive the measured object to move based on the fusion state estimate.

9. A motion control platform for FCT testing based on improved Kalman filtering, characterized in that, The system includes a processor, a memory, and a control program for an FCT test motion control platform based on an improved Kalman filter. The processor executes the control program, which is stored in the memory. The control program implements the FCT test motion control method based on an improved Kalman filter as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a control program for an FCT test motion control platform based on an improved Kalman filter. The control program for the FCT test motion control platform based on an improved Kalman filter implements the FCT test motion control method based on an improved Kalman filter as described in any one of claims 1 to 7.

Images