Rigid coupling constant force loading control method, system and device based on adaptive impedance matching and medium

Abstract

The embodiment of the application provides a rigid coupling constant force loading control method, system, equipment and medium based on adaptive impedance matching, and belongs to the field of screw pair performance test and precise force control. The method comprises the following steps: acquiring displacement signals and speed information of a measured screw and a loading screw, a measured force signal of a force sensor installed between the two screws, and a target force instruction, and generating target data; inputting the target data into a pre-constructed comprehensive control model, and outputting a loading screw position instruction and a measured screw position instruction after position error mapping processing, real-time stiffness identification processing, adaptive impedance solving processing, forward-looking smoothing compensation processing, and iterative learning feedforward updating processing; and outputting the loading screw position instruction and the measured screw position instruction to corresponding servo drivers respectively. High-precision constant force or alternating force loading control of a rigid coupling double-screw test bench is realized without reducing the system stiffness and bandwidth.

Description

Technical Field

[0001] This invention relates to the field of lead screw performance testing and precision force control technology, specifically to a rigid coupling constant force loading control method, system, equipment, and medium based on adaptive impedance matching. Background Technology

[0002] As a core transmission component in high-end equipment such as CNC machine tools and precision positioning platforms, the lead screw pair's lifespan reliability, accuracy retention, and dynamic performance need to be verified and evaluated through specialized test benches. This requires applying precise and controllable constant or alternating axial loads to the lead screw during its reciprocating motion. Currently, mainstream test benches generally adopt a dual-lead screw opposing mechanical structure, where the lead screw under test and the loading lead screw are rigidly connected in series via a high-stiffness force sensor. The axial force is adjusted by a servo motor of the loading lead screw. This structure is widely used due to its internalized load and low requirements for foundation stiffness.

[0003] However, the equivalent contact stiffness between the two lead screws is extremely high (typically in the range of 10). 7 ~10 9 The relative displacement deviation at the micrometer level (on the order of N / m) can cause force fluctuations of tens to hundreds of Newtons. In actual systems, the sum of multiple displacement disturbances, such as cumulative error of lead screw pitch (5~50μm), backlash (3~20μm), elastic deformation under load (1~10μm), thermal expansion caused by heat generation during operation (5~30μm), and dynamic error of servo following (1~10μm), can reach tens of micrometers, far exceeding the submicrometer displacement tolerance allowed by the force control accuracy. This forms the core contradiction of uncontrollable force caused by multiple micro-displacement disturbances under extremely high stiffness coupling.

[0004] To address this issue, traditional PID force closed-loop control suffers from extremely high equivalent stiffness, resulting in a forced minimum proportional gain, slow response, and force overshoot caused by integral saturation during commutation. After replacing the lead screw, parameters need to be manually tuned repeatedly. The mechanical softening scheme, which inserts an elastic element between the sensor and the lead screw, sacrifices system bandwidth and cannot meet the requirements of high-frequency alternating force loading. Open-loop feedforward compensation based on offline calibration tables cannot adapt to time-varying parameters caused by stiffness changes, temperature rise, and wear during operation and lacks closed-loop correction capability. Summary of the Invention

[0005] The purpose of this invention is to provide a rigid coupling constant force loading control method, system, device and medium based on adaptive impedance matching, which can achieve high-precision constant force / alternating force loading of a rigid coupling dual screw test bench during reciprocating motion while maintaining high system stiffness and high bandwidth.

[0006] To achieve the above objectives, embodiments of the present invention provide a rigid coupling constant force loading control method based on adaptive impedance matching, comprising: The displacement and velocity information of the tested lead screw and the loading lead screw, the measured force signal of the force sensor installed between the two lead screws, and the target force command are acquired respectively, and target data is generated. The target data is input into a pre-built integrated control model. After processing through position error mapping, real-time stiffness identification, adaptive impedance calculation, look-ahead smoothing compensation, and iterative learning feedforward update, the loading screw position command and the measured screw position command are output. The loading screw position command and the measured screw position command are output to the corresponding servo drivers respectively.

[0007] Optionally, the position error mapping process includes: Read the force sensor readings when the loading screw is running alone under no-load conditions and use them as the no-load reference; read the force sensor readings when the loading screw and the tested screw are running synchronously under preload conditions. The error mapping value is obtained by subtracting the no-load reference from the force sensor reading when the loading screw and the measured screw are running synchronously, and then dividing by the reference stiffness. The system executes a complete stroke once in both the forward and reverse directions to obtain forward and reverse error mapping values. Based on the difference between the two values, the reverse backlash component is separated, and a continuous error compensation function is output after smooth fitting.

[0008] Optionally, the real-time stiffness identification process includes: The relative displacement is calculated based on the displacement signals of the measured lead screw and the loading lead screw, and the relative velocity is calculated based on the velocity information of the two lead screws. Using measured force signals as output, and the model structure consisting of equivalent stiffness multiplied by relative displacement, damping multiplied by the sum of relative velocity and bias force, the system employs a recursive least squares method with a forgetting factor to estimate and output the current equivalent stiffness of the system online.

[0009] Optionally, the adaptive impedance calculation process includes: The difference between the target force and the measured force signal is used as the input excitation force, and the position compensation is calculated through a virtual second-order mass-spring-damping system. The virtual damping is adaptively adjusted based on the current equivalent stiffness of the system.

[0010] Optionally, the virtual second-order mass-spring-damped system is represented as:

[0011] In the formula, These represent virtual mass, virtual damping, and virtual stiffness, respectively. This represents the calculated position compensation amount. The second derivative of the position compensation amount. The first derivative of the position compensation amount is represented by... Indicates the target loading force. This represents the measured applied force.

[0012] Optionally, the look-ahead smoothing compensation process includes: Obtain the common planned trajectory speed of the tested lead screw and the loaded lead screw; Based on the planned trajectory speed and control cycle, the number of look-ahead window steps is calculated, and the error value of the future look-ahead window steps is queried in the continuous error compensation function. After exponential decay weighted smoothing, the look-ahead compensation amount is output.

[0013] Optionally, the iterative learning feedforward update process includes: The force error recorded in each cycle is processed by a proportional-differential iterative learning law and a zero-phase low-pass filter to output the feedforward signal for the next cycle.

[0014] Secondly, the present invention also provides a rigidly coupled constant force loading control system based on adaptive impedance matching, comprising: The data acquisition unit is used to acquire the displacement signals and velocity information of the tested lead screw and the loading lead screw, the measured force signal of the force sensor installed between the two lead screws, and the target force command, and generate target data. The data processing unit is used to input the target data into the pre-built integrated control model, and after processing such as position error mapping, real-time stiffness identification, adaptive impedance calculation, look-ahead smoothing compensation, and iterative learning feedforward update, output the loading screw position command and the measured screw position command. The control unit is used to output the loading screw position command and the measured screw position command to the corresponding servo driver, respectively.

[0015] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described rigid coupling constant force loading control method based on adaptive impedance matching.

[0016] Fourthly, the present invention also provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described rigid coupling constant force loading control method based on adaptive impedance matching.

[0017] Through the above technical solution, the problems of limited gain, slow response and commutation overshoot under extremely high stiffness are avoided by the synergistic effect of various processing within the integrated model. Moreover, by synthesizing two position commands through an asymmetric allocation method, the loading screw undertakes the main compensation task and the measured screw maintains the motion accuracy. The division of labor is clear, and the test bench can apply constant or alternating loads with high precision during reciprocating motion.

[0018] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a loading system physical model provided in an embodiment of the present invention; Figure 2 This is a flowchart of a rigid coupling constant force loading control method based on adaptive impedance matching provided in an embodiment of the present invention; Figure 3 This is an architecture diagram of a comprehensive control model provided in an embodiment of the present invention; Figure 4 This is a detailed implementation flowchart of a rigid coupling constant force loading control method based on adaptive impedance matching provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a rigid coupling constant force loading control system based on adaptive impedance matching provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] Various embodiments of this disclosure will be described more fully in the following detailed description. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0021] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions or operations and do not limit the addition of one or more functions or operations. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing, or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing.

[0022] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] See Figure 1 As shown, in the physical model of the loading system targeted by this invention, the tested lead screw and the loading lead screw are rigidly connected in series through a high-stiffness force sensor. The force transmission relationship between the two is determined by the equivalent stiffness and structural damping. The key physical relationship is as follows:

[0025] In the formula, This represents the equivalent stiffness of rigid coupling (maximum value, typically 10). 7 10 9 N / m), This indicates the relative displacement between the two lead screws. Indicates the displacement of the lead screw. This indicates the displacement of the lead screw being measured. This indicates structural damping.

[0026] when At its maximum, even Displacement error → Force fluctuations. However, disturbances such as pitch error, backlash, and thermal deformation are much greater than... .

[0027] The various error sources in the system are shown in Table 1 below: Table 1 Error Source Analysis Table

[0028] See Figure 2 The diagram shows a flowchart of a rigidly coupled constant force loading control method based on adaptive impedance matching in a specific embodiment, including the following execution steps: S200: Acquires the displacement signals and velocity information of the tested lead screw and the loading lead screw, the measured force signal of the force sensor installed between the two lead screws, and the target force command, and generates target data.

[0029] Specifically, the tested lead screw and the loading lead screw are each equipped with an independent servo motor and servo driver; displacement encoders are installed on the tested lead screw and the loading lead screw respectively to directly measure the actual displacement of their respective lead screws; a high-rigidity force sensor is installed coaxially in series between the two lead screws to measure the actual axial force transmitted between the two lead screws; the system is also equipped with a motion controller, which is electrically connected to the two servo drivers, the two displacement encoders and the force sensor respectively.

[0030] During system operation, a displacement encoder installed on the lead screw under test senses the actual displacement of the lead screw in real time and converts the displacement into an electrical signal, which is then output to the controller. The controller obtains the real-time displacement signal of the lead screw under test in each control cycle by sampling and analyzing the encoder signal.

[0031] Similarly, the displacement encoder installed on the loading screw senses the actual displacement of the loading screw in real time and outputs the signal to the controller. The controller obtains the real-time displacement signal of the loading screw in each control cycle.

[0032] The controller subtracts the displacement signal sampled in the current cycle from the displacement signal sampled in the previous cycle, and then divides the result by the control cycle to obtain the speed estimate for the current cycle.

[0033] The force sensor installed between the two lead screws directly senses the axial force transmitted between them. When the two lead screws are rigidly connected in series through the force sensor, the elastic element of the force sensor produces a slight deformation proportional to the magnitude of the force under the action of the axial force. The strain gauge inside the force sensor converts the deformation into a change in resistance, which is then converted into a voltage signal or a digital signal by a bridge circuit and a signal conditioning circuit and output to the controller.

[0034] The controller performs analog-to-digital sampling and calibration conversion on the force sensor signal in each control cycle to obtain the current measured axial force signal.

[0035] S201: Input the target data into the pre-built integrated control model, and after processing through position error mapping, real-time stiffness identification, adaptive impedance calculation, look-ahead smoothing compensation and iterative learning feedforward update, output the loading screw position command and the measured screw position command.

[0036] For details, please refer to Figure 3 The diagram shown illustrates the architecture of a comprehensive control model provided in this embodiment of the invention. It includes a first layer, Position Error Mapping (PDEM), for offline or online mapping to provide a basis for feedforward supplementation; a second layer, Real-time Stiffness Identification (RSI), for online parameter estimation; a third layer, Adaptive Impedance Force Control (AIFC), for real-time force closed-loop control to address the problem of easy oscillation in force control under rigid coupling; a fourth layer, Look-forward Predictive Compensation (PCL), which uses a known error mapping table to predict and smoothly compensate N steps in advance, avoiding force shocks at error abrupt changes; and a fifth layer, Iterative Learning Feedforward (ILF), which utilizes the periodic repeatability of reciprocating motion to learn and eliminate residual force errors cycle by cycle.

[0037] Furthermore, the position error mapping process includes: Read the force sensor readings when the loading screw is running alone under no-load conditions and use them as the no-load reference; and read the force sensor readings when the loading screw and the tested screw are running synchronously under preload conditions.

[0038] The error mapping value is obtained by subtracting the no-load reference from the force sensor reading when the loaded lead screw and the tested lead screw are running synchronously, and then dividing by the reference stiffness.

[0039] The system performs a complete stroke once in both the forward and reverse directions to obtain forward and reverse error mapping values. Based on the difference between the two values, the reverse backlash component is separated, and a continuous error compensation function is output after smooth fitting.

[0040] In one specific implementation, with the lead screw under test not installed (unloaded), the loading lead screw is driven to complete its full stroke at an extremely low speed, such as 1 mm / s. The force sensor reading represents the resistance fluctuation of the loading lead screw itself. Records are used as a benchmark.

[0041] After installing the lead screw under test, drive both lead screws synchronously at the same extremely low speed under a small preload, such as 50N, and record the force sensor readings. :

[0042] in, For reference stiffness.

[0043] Execute once in the forward direction and once in the reverse direction to obtain... .

[0044] The difference between the two can be used to separate the backlash component: .

[0045] B-spline or Gaussian process regression is used to smoothly fit the discrete sampling points, generating a continuous error compensation function. The resolution reaches the μm level.

[0046] No additional sensors are required; the entire stroke error can be mapped using only the existing high-precision force sensor. The two-way differential method separates pitch error and backlash in one step.

[0047] Specifically, the real-time stiffness identification process includes: The relative displacement is calculated based on the displacement signals of the measured lead screw and the loading lead screw, and the relative velocity is calculated based on the velocity information of the two lead screws.

[0048] Using measured force signals as output, and the model structure consisting of equivalent stiffness multiplied by relative displacement, damping multiplied by the sum of relative velocity and bias force, the system employs a recursive least squares method with a forgetting factor to estimate and output the current equivalent stiffness of the system online.

[0049] In one specific implementation, the model structure is as follows:

[0050] in, For relative velocity, This is the bias force.

[0051] parameter vector Regression vector .

[0052] RLS recursive formula:

[0053]

[0054] Among them, the forgetting factor To achieve a balance between recognition speed and stability.

[0055] Activation conditions: Only Activate identification in a timely manner to avoid erroneous updates under quasi-static conditions.

[0056] By utilizing the conditionally activated forgetting factor RLS, we can avoid erroneous stiffness estimates during the low signal-to-noise ratio phase dominated by force sensor noise.

[0057] Specifically, the adaptive impedance calculation process includes: The difference between the target force and the measured force signal is used as the input excitation force, and the position compensation is calculated through a virtual second-order mass-spring-damping system. The virtual damping is adaptively adjusted based on the current equivalent stiffness of the system.

[0058] The virtual second-order mass-spring-damped system is represented as follows:

[0059] In the formula, These represent virtual mass, virtual damping, and virtual stiffness, respectively. This represents the calculated position compensation amount. The second derivative of the position compensation amount. The first derivative of the position compensation amount is represented by... Indicates the target loading force. This represents the measured applied force.

[0060] In one specific implementation, traditional force control directly adjusts the position command. However, under extremely high stiffness, even a slight misalignment in gain can cause oscillation. Therefore, the concept of virtual impedance is introduced: A virtual second-order mass-spring-damped system is established inside the controller:

[0061] in, These represent virtual mass, virtual damping, and virtual stiffness, respectively. This represents the calculated position compensation amount. The second derivative of the position compensation amount. The first derivative of the position compensation amount is represented by... Indicates the target loading force. This represents the measured applied force.

[0062] Design criteria: Virtual impedance parameters must meet the following requirements: This means that the virtual stiffness is much smaller than the actual stiffness, thus "softening" the rigid system into a controllable system.

[0063] Virtual Damping Based on real-time identification Adaptive adjustment:

[0064] in, For nominal stiffness, This is the nominal virtual damping.

[0065] The greater the actual stiffness, the greater the required virtual damping to suppress potential oscillations. The square root relationship originates from the critical damping formula for second-order systems. .

[0066] Specifically, the look-ahead smoothing compensation process includes: Obtain the planned trajectory speed of the tested lead screw and the loaded lead screw.

[0067] Based on the planned trajectory speed and control cycle, the number of look-ahead window steps is calculated, and the error value of the future look-ahead window steps is queried in the continuous error compensation function. After exponential decay weighted smoothing, the look-ahead compensation amount is output.

[0068] In one specific implementation, based on the current speed and control cycle Calculate the lookahead window:

[0069] in, The preset look-ahead time is, for example, 50ms.

[0070] Query the future in the PDEM error table Error value of the step:

[0071] Calculate the weighted smoothing compensation amount:

[0072] The weights use exponential decay:

[0073] In the formula, This is the attenuation coefficient, which is related to the system bandwidth.

[0074] The smoothed compensation is superimposed on the position command, allowing for an earlier transition rather than hard compensation at the point of error abruptness.

[0075] Specifically, the iterative learning feedforward update process includes: The force error recorded in each cycle is processed by a proportional-differential iterative learning law and a zero-phase low-pass filter to output the feedforward signal for the next cycle.

[0076] In one specific implementation, the PD-type iterative learning law is:

[0077] in, Indicates the first Feedforward signal for one reciprocating cycle Indicates the first Periodic force error, Indicates the proportional learning gain. Represents the differential learning gain. This indicates a zero-phase low-pass filter, which filters out high-frequency noise and prevents learning divergence.

[0078] -Filter design (zero phase):

[0079] To achieve true zero-phase characteristics, forward-backward filtering is used, and the cutoff frequency is typically set to 1 / 3 to 1 / 2 of the system bandwidth.

[0080] Convergence condition (frequency domain):

[0081] in, The transfer function from position compensation to measured force. .

[0082] Period alignment: Use a trigger signal (such as a zero-crossing signal) to mark the start point of each period, and use interpolation resampling to ensure that the number of data points in each period is consistent.

[0083] By using a PD-type learning law and a zero-phase Q-filter, the compensable error bandwidth is maximized while ensuring learning convergence.

[0084] In one specific embodiment, the pitch error feedforward term Further exponential weighted smoothing is performed using spatial domain look-ahead prediction compensation (PCL module) to achieve this. Instead of directly looking up table values, this avoids force impacts at locations of sudden error changes; backlash compensation item. Further employing the hyperbolic tangent function Replace the sign function to achieve a continuous and smooth transition for commutation time gap compensation.

[0085] The basic form of the position command sent to the two servo motors is:

[0086]

[0087] in, This represents the planned reciprocating motion trajectory. The force compensation distribution coefficient is expressed as follows: ,generally This allows the lead screw to handle more compensation. This indicates the error feedforward provided by the PDEM module. This indicates gap compensation for orientation perception.

[0088] The terms in this basic formula will be further enhanced by adding an iterative learning feedforward term to the right-hand side of the formula. The residual periodic repetitive error is gradually eliminated by the ILF module based on the force error of the previous cycle through a PD-type learning law.

[0089] The correspondence between the basic form and the enhanced form is as follows: In the basic form... Corresponding to the enhanced form In the basic form Corresponding to the enhanced form Enhanced form adds extra item.

[0090] That is, the final position instruction is as follows:

[0091] .

[0092] By using asymmetric compensation distribution, the loading screw undertakes the main compensation task, while the measured screw maintains the motion accuracy.

[0093] S202: Output the loading screw position command and the measured screw position command to the corresponding servo drivers respectively.

[0094] By integrating five types of processing—position-related error mapping, online system parameter identification, adaptive impedance calculation, look-ahead smoothing compensation, and iterative learning feedforward update—into a single comprehensive control model, and taking the obtained displacement signal, velocity information, measured force signal, and target force command as inputs, the model directly outputs the position commands of the loading screw and the measured screw after comprehensive calculation. This achieves high-precision constant or alternating force loading control of a rigidly coupled dual-screw test bench without reducing system stiffness and bandwidth.

[0095] See Figure 4 The diagram shown is a detailed implementation flowchart of a rigid coupling constant force loading control method based on adaptive impedance matching provided in an embodiment of the present invention, including: Initialization phase: Initialize the equivalent stiffness estimate to the nominal stiffness value. The damping estimate is initialized to the nominal damping value. The initial covariance matrix of the recursive least squares algorithm is the initial covariance matrix S. The inverse is used to create an array for storing the feedforward signals of iterative learning. Set all values ​​to zero; set the current cycle number to 0; calculate the critical virtual damping value:

[0096] In the formula, For virtual quality, This value represents the nominal stiffness and serves as the benchmark for subsequent adaptive virtual damping adjustment.

[0097] Within each control cycle, the following operations are performed sequentially: The actual displacements of the two lead screws are obtained by reading the displacement encoders installed on the lead screw under test and the lead screw under load, respectively. and The force sensor installed between the two lead screws is used to obtain the measured axial force F. Then, the speed of the measured lead screw is estimated by dividing the displacement difference between the two time points by the sampling period. and loading screw speed And obtain the planned trajectory speed at the current moment from the trajectory planner. .

[0098] The algorithm calculates the relative displacement based on the current displacement and velocity. and relative velocity The system model assumes the following:

[0099] in, Define a parameter vector for the bias force. Regression vector .

[0100] If the current Then the recursive least squares update is activated:

[0101]

[0102] Among them, the forgetting factor After the update, from Extract equivalent stiffness estimates If the conditions are not met, the update is skipped, and the previous period's estimate is retained.

[0103] Based on the latest identified equivalent stiffness Calculate the current adaptive virtual damping:

[0104] This results in greater virtual damping when the actual stiffness is greater, thus suppressing oscillations.

[0105] Construct a virtual second-order mass-spring-damped system; its continuous-domain differential equation is:

[0106] in, The desired position compensation amount is... For virtual stiffness, For virtual quality.

[0107] In the discrete domain, the position at the previous time step is known. and speed Current virtual acceleration The following equation can be solved:

[0108] Then update the speed and location:

[0109]

[0110] The calculated As a position compensation amount for force control, and saved. , For use in the next cycle.

[0111] Based on the current planned trajectory speed Calculate the number of steps in the look-ahead window:

[0112] in, To pre-set the look-ahead time, This is the average speed reference value.

[0113] Subsequently, the future locations within the forward-looking window... For the query point, from the location-related error mapping table Obtain the corresponding error value for each future time. Assign an exponential decay weight:

[0114] Finally, calculate the weighted smoothing compensation amount: .

[0115] According to the current planned location Query the backlash mapping table Obtain the gap value in the current direction, then multiply it by the hyperbolic tangent function of the planned velocity to achieve a smooth transition:

[0116] in, This is the smoothing coefficient.

[0117] With planned trajectory Based on the asymmetric allocation coefficient , Synthesize two position commands: The command for the lead screw being tested:

[0118] Load lead screw instruction:

[0119] When the end of the previous complex motion cycle is detected, iterative learning of the feedforward signal update is performed. Let the current state be the nth... A cycle, within which there are a total of Each sampling point Calculate the rate of change of force error:

[0120] in, For the first cycle Force error at any given moment.

[0121] The update law for PD-type iterative learning is:

[0122] in, , These are the proportional and differential learning gains, respectively.

[0123] The original update value is passed through a zero-phase low-pass filter. Filtering yields the feedforward signal for the next cycle:

[0124] in, Zero-phase characteristics are achieved using forward-backward filtering, and its transfer function is as follows:

[0125] The convergence of iterative learning is guaranteed by the frequency domain condition:

[0126] in, The transfer function from position compensation to measured force. .

[0127] After the update is complete, increment the cycle number by 1, clear the error cache, and prepare for data collection in the next cycle.

[0128] like Figure 5 As shown, the following are embodiments of the rigid coupling constant force loading control system based on adaptive impedance matching provided in this disclosure. These embodiments belong to the same inventive concept as the rigid coupling constant force loading control methods based on adaptive impedance matching described above. For details not described in detail in the embodiments of the rigid coupling constant force loading control system based on adaptive impedance matching, please refer to the embodiments of the rigid coupling constant force loading control methods based on adaptive impedance matching described above.

[0129] A rigidly coupled constant force loading control system based on adaptive impedance matching includes: The data acquisition unit is used to acquire the displacement signals and velocity information of the tested lead screw and the loading lead screw, the measured force signal of the force sensor installed between the two lead screws, and the target force command, and generate target data. The data processing unit is used to input the target data into the pre-built integrated control model, and after processing such as position error mapping, real-time stiffness identification, adaptive impedance calculation, look-ahead smoothing compensation, and iterative learning feedforward update, output the loading screw position command and the measured screw position command. The control unit is used to output the loading screw position command and the measured screw position command to the corresponding servo driver, respectively.

[0130] Figure 6 This is a schematic diagram of the hardware structure of an electronic device that implements various embodiments of the present invention.

[0131] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0132] A processor may include one or more processing units, such as a central processing unit, application processor, modem processor, graphics processor, image signal processor, controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0133] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0134] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0135] Internal memory can be used to store executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0136] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A rigidly coupled constant force loading control method based on adaptive impedance matching, characterized in that, include: The displacement and velocity information of the tested lead screw and the loading lead screw, the measured force signal of the force sensor installed between the two lead screws, and the target force command are acquired respectively, and target data is generated. The target data is input into a pre-built integrated control model. After processing through position error mapping, real-time stiffness identification, adaptive impedance calculation, look-ahead smoothing compensation, and iterative learning feedforward update, the loading screw position command and the measured screw position command are output. The loading screw position command and the measured screw position command are output to the corresponding servo drivers respectively.

2. The rigid coupling constant force loading control method based on adaptive impedance matching according to claim 1, characterized in that, The position error mapping process includes: Read the force sensor readings when the loading screw is running alone under no-load conditions and use them as the no-load reference; read the force sensor readings when the loading screw and the tested screw are running synchronously under preload conditions. The error mapping value is obtained by subtracting the no-load reference from the force sensor reading when the loading screw and the measured screw are running synchronously, and then dividing by the reference stiffness. The system executes a complete stroke once in both the forward and reverse directions to obtain forward and reverse error mapping values. Based on the difference between the two values, the reverse backlash component is separated, and a continuous error compensation function is output after smooth fitting.

3. The rigid coupling constant force loading control method based on adaptive impedance matching according to claim 1, characterized in that, The real-time stiffness identification process includes: The relative displacement is calculated based on the displacement signals of the measured lead screw and the loading lead screw, and the relative velocity is calculated based on the velocity information of the two lead screws. Using measured force signals as output, and the model structure consisting of equivalent stiffness multiplied by relative displacement, damping multiplied by the sum of relative velocity and bias force, the system employs a recursive least squares method with a forgetting factor to estimate and output the current equivalent stiffness of the system online.

4. The rigid coupling constant force loading control method based on adaptive impedance matching according to claim 3, characterized in that, The adaptive impedance calculation process includes: The difference between the target force and the measured force signal is used as the input excitation force, and the position compensation is calculated through a virtual second-order mass-spring-damping system. The virtual damping is adaptively adjusted based on the current equivalent stiffness of the system.

5. The rigid coupling constant force loading control method based on adaptive impedance matching according to claim 4, characterized in that, The virtual second-order mass-spring-damped system is represented as follows: In the formula, These represent virtual mass, virtual damping, and virtual stiffness, respectively. This represents the calculated position compensation amount. The second derivative of the position compensation amount. The first derivative of the position compensation amount is represented by... Indicates the target loading force. This represents the measured applied force.

6. The rigid coupling constant force loading control method based on adaptive impedance matching according to claim 2, characterized in that, The process of the look-ahead smoothing compensation includes: Obtain the common planned trajectory speed of the tested lead screw and the loaded lead screw; Based on the planned trajectory speed and control cycle, the number of look-ahead window steps is calculated, and the error value of the future look-ahead window steps is queried in the continuous error compensation function. After exponential decay weighted smoothing, the look-ahead compensation amount is output.

7. The rigid coupling constant force loading control method based on adaptive impedance matching according to claim 1, characterized in that, The iterative learning feedforward update process includes: The force error recorded in each cycle is processed by a proportional-differential iterative learning law and a zero-phase low-pass filter to output the feedforward signal for the next cycle.

8. A rigidly coupled constant force loading control system based on adaptive impedance matching, characterized in that, include: The data acquisition unit is used to acquire the displacement signals and velocity information of the tested lead screw and the loading lead screw, the measured force signal of the force sensor installed between the two lead screws, and the target force command, and generate target data. The data processing unit is used to input the target data into the pre-built integrated control model, and after processing such as position error mapping, real-time stiffness identification, adaptive impedance calculation, look-ahead smoothing compensation, and iterative learning feedforward update, output the loading screw position command and the measured screw position command. The control unit is used to output the loading screw position command and the measured screw position command to the corresponding servo driver, respectively.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the rigid coupling constant force loading control method based on adaptive impedance matching as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the rigid coupling constant force loading control method based on adaptive impedance matching as described in any one of claims 1 to 7.

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