Device flexible resistance adjustment servo control method and system

Abstract

The application relates to a device flexible resistance adjusting servo control method and system, which comprises the following steps: acquiring resistance torque data and period control instructions of a target device, carrying out disturbance parameter expansion estimation, obtaining system total disturbance torque data and load equivalent stiffness information; acquiring servo motor data of the target device, carrying out multiple feedforward compensation on the system total disturbance torque data, obtaining anti-disturbance feedforward compensation data, and combining the load equivalent stiffness information to carry out stiffness reference adjustment, thereby obtaining a feedforward reference coefficient; according to the feedforward reference coefficient, torque parameter coupling is carried out on the anti-disturbance feedforward compensation data, thereby obtaining stiffness compensation torque data; according to the servo motor data, the feedforward reference coefficient and the stiffness compensation torque data, instruction construction is carried out, thereby obtaining a servo control instruction. The application can more accurately evaluate the dynamic characteristics of the system under complex working conditions, and improve the precision of servo control.

Description

Technical Field

[0001] This invention relates to the technical field of equipment control, and in particular to a servo control method and system for adjusting the flexible resistance of equipment. Background Technology

[0002] With the increasing demands for intelligent manufacturing and precision control, achieving precise resistance adjustment and dynamic response control of equipment has become a key research topic. Existing servo control methods mainly focus on single torque or position control, often neglecting disturbances and load characteristic changes during actual operation. This traditional control strategy is difficult to adapt to the requirements of flexible loads under complex working conditions, leading to slow system response, insufficient control accuracy, and even affecting the overall performance and service life of the equipment. Summary of the Invention

[0003] The main objective of this invention is to provide a servo control method and system for adjusting the flexible resistance of equipment, which can more accurately evaluate the dynamic characteristics of the system under complex working conditions and improve the accuracy of servo control.

[0004] To achieve the above objectives, the present invention provides a servo control method for adjusting the flexible resistance of a device, comprising: Acquire the resistance torque data and periodic control commands of the target equipment, perform disturbance parameter expansion estimation, and obtain the total disturbance torque data of the system and the equivalent stiffness information of the load. The servo motor data of the target device is obtained, and multiple feedforward compensations are performed on the total disturbance torque data of the system to obtain disturbance rejection feedforward compensation data. The stiffness reference is adjusted in combination with the load equivalent stiffness information to obtain the feedforward reference coefficient. Based on the feedforward reference coefficient, the disturbance rejection feedforward compensation data is coupled with torque parameters to obtain stiffness compensation torque data; The servo control command is obtained by constructing the command based on the servo motor data, the feedforward reference coefficient, and the stiffness compensation torque data.

[0005] Further, the step of acquiring the resistance torque data and periodic control commands of the target device, performing disturbance parameter expansion estimation, and obtaining the total system disturbance torque and load equivalent stiffness information includes: The target device is subjected to motor torque acquisition and periodic signal monitoring to obtain the resistance torque data and the periodic control command; A disturbance observer model is constructed based on the drag torque data and the periodic control command, and multi-dimensional state variable expansion is performed to obtain the initial value of the total disturbance torque of the system. Based on the initial value of the total disturbance torque of the system and the periodic control command, the load end is dynamically identified and the parameters are converged to obtain the equivalent stiffness information of the load. The initial value of the total disturbance moment of the system and the equivalent stiffness information of the load are iteratively mapped and corrected to obtain the total disturbance moment data of the system.

[0006] Further, the process of acquiring the servo motor data of the target device, performing multiple feedforward compensations on the total disturbance torque data of the system to obtain disturbance rejection feedforward compensation data, and combining the load equivalent stiffness information to perform stiffness reference adjustment to obtain feedforward reference coefficients includes: The target device is sampled for motor encoding signals and incrementally decoded to obtain the servo motor data; The total disturbance torque of the system is decoupled and separated to obtain the low-frequency disturbance torque component and the high-frequency resonance suppression component. Based on the servo motor data, phase lead compensation is performed on the low-frequency interference torque component to obtain the anti-interference feedforward compensation data; Based on the servo motor data, an energy coupling calculation is performed on the high-frequency resonance suppression component to obtain the resonance frequency adjustment amount; The feedforward reference coefficient is obtained by performing stiffness reference damping mapping based on the resonant frequency adjustment and the equivalent stiffness information of the load.

[0007] Further, the step of performing energy coupling calculations on the high-frequency resonance suppression component based on the servo motor data to obtain the resonance frequency adjustment amount includes: The servo motor data is processed for real-time load inertia identification to obtain the equivalent load inertia change rate. The equivalent load inertia change rate and the equivalent load stiffness information are processed to calculate the stiffness inertia matching degree, and a stiffness attenuation factor is generated. The high-frequency resonance suppression component is dynamically notch band adjusted according to the stiffness attenuation factor to obtain the resonance suppression amount. The inherent frequency is calculated based on the servo motor data to obtain the characteristic value of the load resonant frequency. The resonant suppression amount and the load resonant frequency characteristic value are used to extract the dominant frequency energy to obtain the resonant frequency adjustment amount.

[0008] Further, the step of coupling the disturbance rejection feedforward compensation data with torque parameters based on the feedforward reference coefficient to obtain stiffness compensation torque data includes: The feedforward reference coefficients are decomposed into flexible stiffness dimensions to obtain axial stiffness gain parameters and radial stiffness compensation coefficients. The deformation direction is corrected based on the radial stiffness compensation coefficient to obtain the deformation suppression compensation torque; Spatial trajectory calculation is performed on the servo motor data to obtain flexible load motion data; The axial stiffness gain parameter and the flexible load motion data are used to perform spatial compensation calculation to obtain the axial stiffness compensation amount. The deformation suppression compensation torque and the axial stiffness compensation amount are fitted using a nonlinear resistance gradient to obtain the stiffness compensation torque data.

[0009] Further, the step of performing spatial trajectory calculation on the servo motor data to obtain flexible load motion data includes: The servo motor data is subjected to flexible deformation friction analysis to obtain the rotor friction vector; Based on the rotor friction vector, the target device is subjected to rigid-flexible transmission coupling to obtain the load end trajectory coordinates; Based on the feedforward reference coefficient, the resistance gradient of the load end trajectory coordinates is calculated to obtain the deformation-induced resistance gradient. The deformation-induced resistance gradient is reversed and converted to obtain the resistance-compensated deformation displacement. The load end trajectory coordinates and the resistance compensation deformation displacement are fused in servo space to output the flexible load motion data.

[0010] Further, the step of constructing servo control commands based on the servo motor data, the feedforward reference coefficient, and the stiffness compensation torque data includes: The position and velocity parameters of the servo motor data are separated to obtain the rotor angular position signal and rotor angular velocity components; The stiffness compensation torque data is phase-synchronized based on the rotor angular position signal to obtain the phase compensation torque vector. Based on the angular velocity component, the phase compensation torque vector is velocity-coupled to obtain a velocity-adaptive compensation component; The speed-adaptive compensation component is linearly superimposed with the feedforward reference coefficient to obtain the disturbance rejection compensation composite torque data. The periodic control command is reconstructed based on the disturbance rejection compensation combined torque data to obtain the servo control command.

[0011] Further, the step of velocity coupling the phase compensation torque vector based on the angular velocity component to obtain a velocity-adaptive compensation component includes: The internal resistance parameters of the flexible material are extracted from the angular velocity components to obtain the viscoelastic hysteresis damping parameters; Based on the viscoelastic hysteresis damping parameters, the phase compensation torque vector is subjected to hysteresis nonlinear compensation to obtain the material internal resistance compensation base value. The deformation rate coupling factor is obtained by performing flexible deformation rate analysis on the angular velocity components. The deformation rate coupling factor and the material internal resistance compensation base value are converted into viscoelastic work equivalent to obtain the internal resistance compensation torque data. The internal resistance compensation torque data is corrected by time-temperature equivalent adjustment based on the preset characteristic data of the flexible connector of the equipment to obtain the speed-adaptive compensation component.

[0012] The present invention also provides a flexible resistance adjustment servo control system for equipment, applied to any one of the above-described flexible resistance adjustment servo control methods for equipment, comprising: The acquisition module is used to acquire the resistance torque data and periodic control commands of the target device, perform disturbance parameter expansion estimation, and obtain the total disturbance torque data and load equivalent stiffness information of the system. The analysis module is used to acquire the servo motor data of the target device, perform multiple feedforward compensation on the total disturbance torque data of the system to obtain disturbance rejection feedforward compensation data, and combine the load equivalent stiffness information to adjust the stiffness reference to obtain the feedforward reference coefficient. The association module is used to couple the disturbance rejection feedforward compensation data with torque parameters based on the feedforward reference coefficient to obtain stiffness compensation torque data. The processing module is used to construct instructions based on the servo motor data, the feedforward reference coefficient, and the stiffness compensation torque data to obtain servo control instructions.

[0013] The present invention provides a servo control method and system for adjusting the flexible resistance of equipment, which has the following beneficial effects: By obtaining total disturbance torque data and equivalent load stiffness information through disturbance parameter expansion estimation, the dynamic characteristics of the system under complex operating conditions can be more accurately assessed, thereby improving the accuracy of servo control and providing a more reliable foundation for stable equipment operation. Employing multiple feedforward compensation techniques combined with equivalent load stiffness information for stiffness reference adjustment enables refined adaptation to different load characteristics, helping the system maintain stable response under changing operating conditions and avoiding over- or under-control. Coupled torque parameters based on the feedforward reference coefficient and disturbance feedforward compensation data ensure efficient system operation under different operating states and load conditions, reducing control delay and oscillation, and improving the overall dynamic performance of the system. By comprehensively analyzing servo motor data and stiffness compensation torque data, more reasonable servo control commands are constructed, and intelligent adjustment strategies achieve optimized operation of the entire system, effectively improving response speed and reducing energy consumption and mechanical wear. By considering the periodic control commands of the target equipment, the control strategy can be flexibly adjusted according to the characteristics and changing needs of different application scenarios, making the system more adaptable to diverse industrial environments and task requirements. Attached Figure Description

[0014] Figure 1 This is a flowchart of a servo control method for adjusting the flexible resistance of a device, provided by the present invention. Figure 2 This is a structural diagram of a flexible resistance adjustment servo control system for a device provided by the present invention.

[0015] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0018] Reference Figure 1 As shown, the present invention provides a servo control method for adjusting the flexible resistance of a device, comprising: Step S1: Obtain the resistance torque data and periodic control command of the target equipment, perform disturbance parameter expansion estimation, and obtain the total disturbance torque data and load equivalent stiffness information of the system; Step S2: Obtain the servo motor data of the target device, perform multiple feedforward compensation on the total disturbance torque data of the system to obtain the disturbance rejection feedforward compensation data, and combine the load equivalent stiffness information to adjust the stiffness reference to obtain the feedforward reference coefficient. Step S3: Couple the torque parameters based on the feedforward reference coefficients to obtain the stiffness compensation torque data; Step S4: Construct instructions based on servo motor data, feedforward reference coefficients, and stiffness compensation torque data to obtain servo control instructions.

[0019] Based on the steps described above, the detailed process is as follows: Step S1: By acquiring strain sensor data and motor current ripple characteristics at the end of the target equipment's power transmission chain in real time, the resistance torque waveform under periodic operating commands is obtained. A nonlinear extended state observer is used to decouple and reconstruct multi-source disturbances, including load inertia abrupt changes and flexible deformation displacement harmonic components, and to construct the dynamic differential equation of the total system disturbance torque.

[0020] The observer performs three core operations: initializing the variable expansion matrix of the Lyapunov stability constraint and injecting the coupling perturbation factor related to the motor speed; separating the quasi-static and dynamic resistance components of the flexible load through high-dimensional phase space projection; and iteratively updating the load-end stiffness parameters based on the adaptive sliding mode convergence algorithm.

[0021] The final output includes two sets of key parameters: the total disturbance torque tensor (containing the torque fluctuation spectrum characteristics) and the load equivalent stiffness change information (including the principal stiffness axis direction and stiffness attenuation gradient), laying the physical model foundation for feedforward compensation. This step relies on the collaborative operation of a harmonic wavelet decomposer and a recurrent neural network to ensure the entire calculation process is completed within a 2ms period.

[0022] Step S2: The servo motor data output from the incremental encoder undergoes a four-step processing flow: electromagnetic harmonic interference is eliminated using an adaptive Kalman filter; rotor position pulsation characteristics are analyzed using frequency domain cross-correlation analysis; and the dual-frequency components of the total system disturbance torque data are separated based on this analysis. For the low-frequency disturbance torque of 0-50Hz, a complex vector transfer function is constructed using a leading phase compensator, and the compensation amount is made to lead the actual disturbance phase by adjusting the zero-pole distribution; for the high-frequency resonant component of 50-2000Hz, the Coriolis effect gain is calculated based on the real-time motor speed, and the notch filter is reconstructed by combining the viscous damping coefficient in the stiffness information.

[0023] Stiffness reference dynamic adjustment mechanism: Extract the rate of change of elastic modulus of principal stiffness axis from the load equivalent stiffness information and map it as a compensation gain scaling factor; at the same time, use the stiffness damping ratio parameter to correct the notch depth, so that the feedforward reference coefficient becomes a four-dimensional parameter vector including amplitude frequency compensation and phase calibration.

[0024] Step S3: The feedforward reference coefficients are decomposed in three-dimensional stiffness space using Cartesian decomposition: the axial component is solved as a stiffness gain parameter (scalar) along the motor spindle direction, and the radial component is projected onto the load deflection plane to form a compensation coefficient matrix. Third-order processing is applied to the disturbance rejection feedforward compensation data: a deformation direction correction algorithm is used in the radial plane, combined with the Poisson's ratio characteristics of the flexible load, to generate a suppressive torque ellipsoidal distribution; simultaneously, the servo motor data is input into the kinematics solver, and the load end-point trajectory curvature is analyzed using the inverse Jacobian matrix.

[0025] The axial stiffness gain is multiplied by the Hadamard product with the trajectory curvature tensor to generate a dynamic stiffness compensation quantity that varies with curvature. Finally, a tensor contraction operation is performed using a nonlinear drag gradient fitter: the deformation suppression torque is projected onto the tangent space and translated along with the axial compensation quantity on a Riemannian manifold, outputting a pseudo-tensor structure of the stiffness compensation torque. This process employs a conformal geometric algebraic computational framework to ensure the invariance of torque direction during complex surface deformation.

[0026] Step S4: The preprocessing of servo motor data includes: decoupling position and velocity signals using a fourth-order Butterworth filter bank, and constructing a dual-channel observer for angular position and angular velocity in the rotor coordinate system. The phase synchronization module performs four key operations: aligning the stiffness compensation torque to the current rotor position angle using Lie group exponential mapping; establishing a rotating coordinate system transformation including flux harmonic compensation; and using phase-locked loop (PLL) technology to eliminate phase jitter caused by encoder quantization errors. The velocity coupling stage implements rigid-flexible dynamic fusion: establishing a nonlinear transfer function for angular velocity-compensation torque based on the viscoelastic constitutive equation; and extracting the modulation characteristics of the velocity change rate on the drag gradient using fractional derivatives.

[0027] The disturbance rejection compensation synthesis process is executed in torque space: the speed-adaptive component is projected onto the hyperplane defined by the feedforward reference coefficient, and linear superposition is achieved using a convex optimization algorithm with boundary constraints. The final command reconstruction is achieved through dynamic harmonic injection technology: the fundamental component is extracted from the original periodic control command spectrum, and the synthesized torque is embedded into the command space according to the power angle characteristics to generate servo control commands containing seventh harmonic suppression capabilities.

[0028] This invention provides a flexible resistance adjustment servo control method for equipment. By obtaining the total disturbance torque data and equivalent load stiffness information of the system through disturbance parameter expansion estimation, it can more accurately evaluate the dynamic characteristics of the system under complex operating conditions, thereby improving the accuracy of servo control and providing a more reliable foundation for the stable operation of the equipment. Employing multiple feedforward compensation techniques combined with equivalent load stiffness information for stiffness reference adjustment enables refined adaptation to different load characteristics, helping the system maintain a stable response under changing operating conditions and avoiding over- or under-control. Based on the feedforward reference coefficient, torque parameter coupling with disturbance feedforward compensation data ensures efficient operation of the system under different operating states and load conditions, reducing control delay and oscillation, and improving the overall dynamic performance of the system. By comprehensively analyzing servo motor data and stiffness compensation torque data, more reasonable servo control commands are constructed, and intelligent adjustment strategies are used to optimize the operation of the entire system, effectively improving response speed and reducing energy consumption and mechanical wear. By considering the periodic control commands of the target equipment, the control strategy can be flexibly adjusted according to the characteristics and changing needs of different application scenarios, making the system more adaptable to diverse industrial environments and task requirements.

[0029] In one embodiment, the drag torque data and periodic control commands of the target device are acquired, disturbance parameter expansion estimation is performed, and the total system disturbance torque and load equivalent stiffness information are obtained, including: A high-precision strain gauge torque sensor is installed on the power output shaft of the equipment to capture the raw torque fluctuation signal in real time at a sampling rate of 10kHz. The servo drive command bus is monitored synchronously, and the duty cycle and frequency characteristics of the periodic control commands are recorded using a hardware-level pulse counter. The raw torque signal undergoes third-order preprocessing: a temperature compensation circuit is used to eliminate thermal drift errors; an anti-aliasing filter is used to suppress high-frequency noise; and adaptive threshold segmentation is applied to extract the effective load segment.

[0030] The periodic command parsing module performs command decoding and timing alignment, identifying characteristic parameters of acceleration, constant velocity, and braking segments in motion control. It outputs a drag torque data package containing timestamps (including peak, mean, and harmonic distortion rates) and a periodic control command vector with phase markers, providing a time-domain synchronized data base for disturbance modeling.

[0031] A nonlinear disturbance observer based on generalized momentum is constructed, and a five-dimensional state extension structure is initialized: motor rotor angular displacement, load-end flexural deformation, electromagnetic harmonic disturbance, viscous frictional harmonics, and stiffness coupling error. Lie derivatives are used to establish the state transition equations, mapping the drag torque data to the tangent bundle space to generate a torque gradient tensor. Periodic control commands are injected as external excitation terms, and rigid and flexible disturbance components are separated through phase trajectory differential geometry analysis.

[0032] Perform Singer integral operations in the multidimensional phase space to expand the state variables to a nine-dimensional parameter space containing Coriolis force and centripetal force. Output the initial value matrix of the total disturbance torque of the system (including the frequency domain energy distribution spectrum). This matrix has time-varying symmetric positive definite characteristics and directly reflects the disturbance coupling strength of each node in the transmission chain.

[0033] The initial value of the total disturbance torque of the system is input into the load dynamics solver, and a Newton-Euler recursive equation is constructed by combining the acceleration command component in the periodic control command. A virtual six-dimensional force sensor is set at the load suspension point, and a load mass-stiffness-damping correlation model is established using a model reference adaptive strategy. A three-step convergence operation is performed: the load resonant frequency is identified based on the torque harmonic components; the stiffness characteristic frequency is extracted using envelope spectrum analysis; and the feasible region of the stiffness parameters is solved using the gradient projection method.

[0034] The parameter convergence process is constrained by function, and convergence locking is triggered when the rate of change of stiffness characteristic frequency is less than 0.1 Hz / ms. The output load equivalent stiffness information matrix (3×3 symmetric matrix) is output, whose main diagonal elements contain axial, radial, and tangential stiffness values, and off-diagonal elements represent stiffness coupling coefficients.

[0035] A stiffness-disturbance bivariate correction space is established, and the initial value of the total system disturbance moment is projected onto the tangent plane of this space. A four-level iterative loop is executed: the disturbance gradient is calculated using the direction of the principal eigenvector of the stiffness tensor; the disturbance components are separated using orthogonalization; the amplitude and phase errors are corrected according to the minimum work principle; and the coefficients of the inertia terms of the matrix are updated. After each iteration, the cross-correlation between the stiffness parameters and the disturbance is checked, and the output jump is initiated when the correlation coefficient exceeds 0.95. The maximum number of iterations is set to 20, and the step size is adaptively adjusted within the range of 0.05-0.2 Nm / deg. Finally, the total system disturbance moment data with confidence intervals is generated, including 12-dimensional characteristic parameters such as fundamental amplitude, resonant point location, and phase delay angle.

[0036] This embodiment achieves dynamic capture of the total disturbance torque and equivalent load stiffness of the system through real-time data acquisition and disturbance parameter expansion estimation, effectively improving the adaptive capability and disturbance rejection performance of the servo control system under complex flexible load scenarios. A multi-dimensional state variable expansion and iterative mapping correction mechanism is employed to accurately compensate for resistance fluctuations caused by changes in load stiffness, avoiding control instability due to parameter identification errors. Optimized feedforward compensation and torque parameter coupling reduce energy loss during the control process, simultaneously improving command construction efficiency and overall system response speed.

[0037] In one embodiment, servo motor data of the target device is acquired, multiple feedforward compensations are performed on the total system disturbance torque data to obtain disturbance rejection feedforward compensation data, and stiffness reference adjustment is performed in conjunction with load equivalent stiffness information to obtain feedforward reference coefficients, including: High-precision position sensors are deployed to monitor the angle of the motor shaft, acquiring pulse signals output from the motor encoder. A high-speed sampling circuit is used during sampling to ensure signal integrity and avoid signal loss due to insufficient sampling frequency. Incremental decoding is the process of converting the acquired pulse signals into meaningful position, velocity, and acceleration information.

[0038] The decoder processes the A and B phase quadrature encoded signals, determines the rotation direction by judging the phase difference, and accumulates the pulse count to obtain the angular displacement. The decoding process is implemented using hardware decoding circuits or software algorithms; hardware decoding is suitable for high-speed applications. The decoded data is filtered to eliminate noise interference and improve signal quality. Through differential calculation, the position information is converted into velocity and acceleration information, completing the acquisition of the servo motor's basic parameters. These parameters form the data foundation for subsequent control strategies, providing the necessary input conditions for decoupling the system's total disturbance torque and feedforward compensation. The accuracy of the servo motor data directly affects the performance of the control system; therefore, employing high-precision sampling and decoding methods is crucial. Appropriate data smoothing and preprocessing are also key steps in ensuring data quality.

[0039] Characteristic decoupling and separation of the total system disturbance torque is based on the analysis of the overall disturbance characteristics, decomposing the complex disturbance torque into two parts: a low-frequency disturbance torque component and a high-frequency resonance suppression component. This decoupling process employs frequency domain analysis to convert the time-domain disturbance signal to the frequency domain. In the frequency domain, by setting an appropriate cutoff frequency, the low-frequency and high-frequency components are distinguished. The low-frequency disturbance torque component mainly originates from slowly varying factors such as load changes, friction, and gravity, manifesting as a steady-state deviation in the system response. The high-frequency resonance suppression component is mainly caused by factors such as mechanical resonance, electrical noise, and elastic deformation of the transmission chain, manifesting as system oscillations and instability.

[0040] During decoupling, bandpass filters are used to extract signals from different frequency bands, ensuring effective separation of the two components. After extraction, low-frequency interference signals undergo trend analysis to identify their variation patterns and characteristics; high-frequency components are identified by resonant frequency recognition to determine the main resonant points. The accuracy of characteristic decoupling depends on the appropriate setting of frequency boundaries and the selection of filtering algorithms; therefore, parameter adjustments are necessary based on specific application scenarios and equipment characteristics. The two decoupled components then enter different compensation stages: the low-frequency component is used for phase lead compensation, and the high-frequency component is used for resonance suppression. This separation approach enables more effective compensation strategies for disturbances of different natures, improving the system's anti-disturbance capability.

[0041] Phase lead compensation for low-frequency interference torque components based on servo motor data is the core component of anti-interference feedforward compensation. Phase lead compensation aims to predict and eliminate the impact of low-frequency interference on the system in advance. During the compensation process, position and speed information extracted from servo motor data is used, combined with the characteristics of the low-frequency interference torque components. Furthermore, by analyzing the relationship between interference and motor response in historical data, the propagation law and time delay characteristics of the interference are identified. The compensator design employs a lead filter structure, whose transfer function includes zeros and poles, resulting in phase advance within a specific frequency range. The calculation of the compensation amount considers the amplitude and frequency characteristics of the interference, as well as the system's dynamic response time, ensuring that the compensation signal reaches the actuator before the interference actually affects the system.

[0042] The compensation signal acts directly on the control output through the feedforward channel, bypassing the feedback loop and reducing the lag in feedback control. The evaluation of the compensation effect is based on the stability of the system response and the reduction in tracking error; the compensation effect is optimized by adjusting the compensator parameters. The realization of phase lead compensation depends on an accurate understanding of the disturbance characteristics; therefore, the compensator parameters should be dynamically adjusted based on actual operating data. The disturbance rejection feedforward compensation data, after phase lead compensation processing, can effectively reduce the impact of low-frequency interference on system performance, improve the system's steady-state accuracy and dynamic response characteristics, and provide a more stable control basis for subsequent stiffness reference adjustment.

[0043] Performing energy coupling calculations on the high-frequency resonance suppression components based on servo motor data is a crucial step in processing the high-frequency dynamic characteristics of the system. Energy coupling calculations are based on the principles of energy transfer and conversion, analyzing the energy exchange relationship between the high-frequency resonance components and various parts of the system. During the calculation process, the speed and current data of the servo motor are used to calculate the motor input power and mechanical output power, determining the energy flow direction and distribution. Through resonance characteristic analysis, energy accumulation points and release mechanisms in the system are identified, establishing a resonance energy model. The model considers the elastic potential energy of the mechanical structure, the kinetic energy of the mass, and the energy consumed by damping, comprehensively describing the energy transfer process of high-frequency vibration. The energy coupling index is calculated based on the energy distribution ratio at different frequencies, quantifying the degree of influence of resonance on system performance.

[0044] The energy coupling calculation results reflect the system's resonance sensitivity at different frequencies, with highly sensitive frequency bands marked as key suppression targets. The generation of the resonance frequency adjustment is based on minimizing the system's resonance energy, achieved by adjusting control parameters to alter the system's frequency response characteristics. The energy coupling calculation process employs real-time computation to ensure timely response to changes in system state. The accuracy of the calculation results directly impacts the resonance suppression effect; therefore, the precision of data processing and the stability of the algorithm are crucial during implementation. The resonance frequency adjustment obtained through energy coupling calculations provides a basis for adjusting high-frequency dynamic characteristics in subsequent stiffness-based damping mapping, enabling the system to more effectively cope with high-frequency disturbances and resonance problems.

[0045] The crucial step in determining the feedforward reference coefficients is to perform stiffness-based damping mapping based on the resonant frequency adjustment and the load's equivalent stiffness information. Stiffness-based damping mapping establishes a correspondence between the system's stiffness characteristics and damping requirements, enabling the control system to adaptively adjust control parameters according to load characteristics. The mapping process uses the load's equivalent stiffness as the reference point and combines it with the resonant frequency adjustment for multi-dimensional spatial mapping. The load's equivalent stiffness information is obtained through force sensor measurement or model estimation, reflecting the load's resistance to position changes. The mapping function design employs a nonlinear mapping relationship, using different mapping curves in different stiffness ranges to ensure suitable damping characteristics under various load conditions. The mapping results—the feedforward reference coefficients—directly affect the system's impedance characteristics. Larger coefficient values ​​correspond to higher impedance, suitable for precise positioning tasks; smaller coefficient values ​​correspond to lower impedance, suitable for compliant control scenarios. Precise adjustment of damping characteristics is crucial for system stability; excessive damping leads to sluggish system response, while insufficient damping causes oscillations. The implementation of stiffness-based damping mapping considers the system's physical constraints and safety boundaries, ensuring that the generated feedforward reference coefficients are within an effective range. The dynamic nature of the mapping process enables the system to respond to load changes in real time and maintain optimal performance under different operating conditions.

[0046] This embodiment achieves effective decoupling and targeted compensation of system disturbance torque by implementing a servo control method for adjusting the flexible resistance of the equipment. This method separates the total system disturbance torque into low-frequency disturbance components and high-frequency resonant components, performing phase lead compensation and energy coupling calculations respectively, significantly improving the system's ability to suppress disturbances of different frequencies. Accurate acquisition and decoding of servo motor data provides high-quality basic data for the control system, ensuring the accuracy of disturbance identification and compensation. Through stiffness-based damping mapping of resonant frequency adjustment and load equivalent stiffness information, control parameters can be adaptively adjusted according to actual load characteristics, maintaining optimal performance under different operating conditions.

[0047] In one embodiment, energy coupling calculation is performed on the high-frequency resonance suppression component based on servo motor data to obtain the resonance frequency adjustment amount, including: Real-time load inertia identification processing of servo motor data is a fundamental step in obtaining the system's dynamic characteristics. This process achieves dynamic estimation of load inertia by analyzing data such as servo motor torque commands, speed response, and position feedback. The identification process employs a recursive parameter estimation method, establishing a relationship based on motor acceleration and output torque. By collecting and processing data during motor acceleration and deceleration, key information reflecting load inertia characteristics is extracted.

[0048] The collected data is filtered and preprocessed to eliminate measurement noise and interference. The identification results are continuously updated through an adaptive mechanism to ensure timely tracking of load inertia changes. The calculation of the equivalent load inertia change rate is based on the difference in inertia estimates within adjacent time periods, reflecting the changing trend of the load's dynamic characteristics.

[0049] The rate of change directly affects the subsequent stiffness-inertia matching degree calculation, providing an important basis for system parameter adjustment. The accuracy of the identification process has a significant impact on the performance of the entire control system; therefore, multiple verification mechanisms are adopted during implementation to ensure the reliability of the identification results. The equivalent load inertia rate of change obtained through real-time identification provides accurate data support for subsequent stiffness-inertia matching degree calculations, and is a key element in achieving precise control.

[0050] The stiffness-inertia matching degree calculation is based on the mechanical and dynamic characteristics of the load. By analyzing the relationship between stiffness and inertia, the dynamic response capability of the system is evaluated. During the calculation, the equivalent load inertia change rate is correlated with the equivalent load stiffness to establish a stiffness-inertia characteristic curve. The evaluation criteria for the matching degree include indicators such as stiffness-inertia ratio, natural frequency, and damping characteristics, which comprehensively reflect the dynamic performance of the system. The stiffness attenuation factor is generated based on the matching degree calculation results and is used to adjust the stiffness characteristics of the system. The magnitude of the attenuation factor directly affects the dynamic response characteristics of the system; a larger attenuation factor indicates that the system requires stronger stiffness suppression, while a smaller attenuation factor indicates that the system can maintain higher response characteristics.

[0051] The matching degree calculation process employs real-time processing to ensure timely response to changes in system state. The accuracy of the calculation results directly impacts the subsequent dynamic notch band adjustment effect; therefore, calculation precision and stability are emphasized during implementation. The stiffness attenuation factor obtained through stiffness-inertia matching degree calculation provides crucial control parameters for subsequent dynamic notch band adjustment, enabling the system to better adapt to changes in load characteristics.

[0052] Dynamic notch filter bandwidth adjustment is based on changes in the stiffness attenuation factor. By adjusting the center frequency and bandwidth of the notch filter, precise suppression of high-frequency resonances is achieved. During the adjustment process, the parameters of the notch filter are dynamically updated according to the changes in the stiffness attenuation factor, ensuring optimal notch filtering performance. The selection of the notch filter bandwidth considers the system's resonance characteristics and operating frequency range, avoiding excessive attenuation of useful signals. The generation of resonance suppression is based on the output characteristics of the notch filter, reflecting the system's ability to suppress high-frequency resonances.

[0053] The notch filter is designed with an adaptive structure, automatically adjusting its characteristics according to changes in the resonant frequency. The accuracy of the frequency band adjustment directly affects the resonance suppression effect; therefore, a high-precision digital filter is used in implementation. During dynamic notch filtering, the stability of the suppression effect is ensured by real-time response monitoring. The calculation of the resonance suppression amount considers the system's bandwidth requirements and stability constraints, ensuring the suppression effect while avoiding the introduction of new instability factors. The resonance suppression amount obtained through dynamic notch frequency band adjustment provides the basic data for subsequent dominant frequency energy extraction, which is a key step in achieving precise resonance control.

[0054] The natural frequency calculation is based on the operating data of the servo motor. By analyzing the free vibration characteristics of the system, its natural vibration frequency is identified. During the calculation, the system's frequency response characteristics are extracted using spectral analysis methods based on the motor's speed, position, and current data. Multiple frequency analysis techniques, including Fast Fourier Transform and wavelet analysis, are employed to ensure accurate extraction of frequency features. The calculation of the load resonant frequency characteristic value considers the system's mechanical structure characteristics and motion state, reflecting the load's vibration characteristics under different operating conditions. Real-time tracking of frequency characteristics is performed during the characteristic value extraction process. The accuracy of the natural frequency calculation has a significant impact on the system's resonance control effect; therefore, high-precision numerical processing methods are used in the calculation process. Real-time processing is used to update the frequency characteristic value, ensuring timely response to changes in system state. The load resonant frequency characteristic value obtained through the natural frequency calculation provides an accurate frequency reference for subsequent extraction of dominant frequency energy, forming the foundation for achieving precise resonance control.

[0055] The dominant frequency energy extraction process is based on a comparative analysis of the resonant suppression value and the load resonant frequency characteristic value, identifying the main resonant components in the system through energy distribution characteristics. The extraction process employs frequency domain analysis methods to quantitatively evaluate the energy contribution of different frequency components. The identification of the dominant frequency is based on energy concentration analysis, selecting the frequency component with the largest energy proportion as the adjustment target. The generation of the resonant frequency adjustment value considers the system's stability requirements and control bandwidth limitations to ensure the reliability of the adjustment effect.

[0056] The calculation of the adjustment amount comprehensively considers the influence of multiple frequency components. The accuracy of the energy extraction process directly affects the resonance control effect of the system; therefore, high-precision signal processing technology is employed during implementation. The identification result of the dominant frequency is kept up-to-date through a real-time update mechanism to ensure timely response to changes in system characteristics. The resonance frequency adjustment amount obtained through the energy extraction of the dominant frequency completes the processing of the high-frequency resonance suppression component, providing a precise adjustment basis for the resonance control of the system.

[0057] This embodiment achieves precise control of the system's dynamic characteristics by real-time identification of load inertia in servo motor data and obtaining the equivalent load inertia change rate, providing fundamental data support for flexible resistance adjustment. The matching degree between the equivalent load inertia change rate and the load's equivalent stiffness information is calculated to generate a stiffness attenuation factor, enabling the system to accurately assess the degree of dynamic characteristic matching and improving control precision. Based on the stiffness attenuation factor, dynamic notch filtering is applied to the high-frequency resonance suppression component to obtain the resonance suppression amount, achieving precise suppression of high-frequency resonance and effectively reducing system vibration. The natural frequency is calculated using servo motor data to obtain the load resonance frequency characteristic value, providing an accurate frequency reference for the system. The dominant frequency energy is extracted from the resonance suppression amount and the load resonance frequency characteristic value to obtain the resonance frequency adjustment amount, completing the processing of the high-frequency resonance suppression component. This enables the system to maintain stable dynamic response capability under different load characteristics, significantly improving the flexible control performance of the servo system.

[0058] In one embodiment, torque parameter coupling is performed based on the feedforward reference coefficients to obtain stiffness compensation torque data, including: Feedforward reference coefficients represent predictive compensation parameters for external disturbances. In flexible load devices, these parameters need to be transformed into more precise control quantities through stiffness dimension decomposition. The stiffness dimension decomposition process starts with unified feedforward reference coefficients and maps them to the two main dimensions of axial and radial in space.

[0059] During the decomposition process, the feedforward reference coefficients are deconstructed into stiffness characteristics in different directions. The axial stiffness gain parameter reflects the stiffness variation characteristics along the principal axis, while the radial stiffness compensation coefficient characterizes the stiffness characteristics perpendicular to the principal axis. This decomposition method conforms to the differences in physical characteristics exhibited by flexible loads in different directions, making control more precise. Dimensional decomposition employs a matrix projection method, projecting the feedforward reference coefficients onto a predefined stiffness feature space. The feature space is constructed based on the system's structural characteristics and real-time operating state, including the inherent properties of the flexible material and the current load conditions.

[0060] The decomposition process considers the differences in dynamic response of flexible loads in different directions. By collecting historical operating data, a stiffness characteristic model is established to make the decomposition results more consistent with actual working conditions. The axial stiffness gain parameter usually exhibits high-frequency response characteristics, while the radial stiffness compensation coefficient reflects more the ability to suppress large-amplitude deformation at low frequencies.

[0061] Correcting the deformation direction based on the radial stiffness compensation coefficient using the disturbance rejection feedforward compensation data is a crucial step in accurately suppressing deformation under flexible loads. The disturbance rejection feedforward compensation data includes an estimated response to external disturbances. The deformation direction correction process utilizes the radial stiffness compensation coefficient to perform a spatial mapping transformation on the disturbance rejection feedforward compensation data, ensuring that the direction of the compensation torque is consistent with the actual deformation direction.

[0062] The correction process considers the nonlinear deformation characteristics of the flexible load under different working conditions. The directionality of the feedforward compensation data is adjusted using a radial stiffness compensation coefficient to generate a more accurate deformation suppression compensation torque. The correction algorithm employs a vector decomposition method, decomposing the disturbance rejection feedforward compensation data into components parallel and perpendicular to the deformation direction, and then recombines these components based on the radial stiffness compensation coefficient. By considering the radial anisotropy of the flexible load, differentiated compensation is provided for deformation in different directions. During the generation of the deformation suppression compensation torque, based on the inertial and damping characteristics of the flexible load, the compensation torque not only suppresses the current deformation but also prevents future deformation trends.

[0063] Spatial trajectory calculation of servo motor data is a crucial step in obtaining the actual motion state of a flexible load. Servo motor data contains motion state information such as the motor's position, velocity, and acceleration. Spatial trajectory calculation transforms this basic data into comprehensive characteristics describing the motion of the flexible load. The spatial trajectory calculation process involves the fusion of data from multiple dimensions, including motor encoder data, current data, and historical motion trajectory data. The calculation process first filters the raw servo motor data to eliminate the influence of noise on the trajectory calculation. The filtered data is then mapped to the workspace using a pre-defined forward kinematics model to obtain the spatial position and attitude information of the end effector. Spatial trajectory calculation not only considers the motion of the rigid components but also incorporates emerging flexible deformation to predict the deformation state of the flexible load during motion.

[0064] By analyzing positional changes over a continuous time series, the velocity and acceleration distributions of the flexible load are calculated. This distribution information is crucial for subsequent compensation calculations. Spatial trajectory calculation also includes feature point extraction and trajectory smoothing to ensure that the calculation results accurately reflect the motion characteristics of the flexible load. Feature point extraction identifies target points in the trajectory, such as points of direction change and velocity extremes; these feature points are essential for understanding the load's motion patterns. Trajectory smoothing ensures that the calculated flexible load motion data is continuous and smooth, avoiding abrupt changes in compensation calculations.

[0065] Spatial compensation calculation establishes a mapping relationship between stiffness characteristics and actual motion state, enabling the generation of accurate axial compensation based on the current motion state. The spatial compensation calculation process employs a vector integration method, using the axial stiffness gain parameter as a weighting function to integrate the flexible load motion data in the spatial domain. The calculation process considers the stiffness variation characteristics of the flexible load under different motion states, particularly the dynamic stiffness characteristics during high-speed motion and rapid changes in direction.

[0066] Spatial compensation calculation first decomposes the motion data of the flexible load into spatial components to identify the axial components in the motion. The decomposed axial motion data is then multiplied by the axial stiffness gain parameter to generate a preliminary axial compensation amount. This preliminary compensation amount needs to be adjusted using a nonlinear mapping function to adapt to changes in stiffness characteristics under different operating conditions. The nonlinear mapping function, constructed based on historical operating data, reflects the stiffness variation patterns under different load conditions and motion states. Spatial compensation calculation also considers the system's response delay, performing time-domain prediction of the axial compensation amount to ensure that the compensation effect is synchronized with actual needs. The prediction method, based on current motion trends and historical response characteristics, can effectively compensate for the system's delay effects. Through this comprehensive compensation calculation that considers both spatial and temporal characteristics, the axial stiffness compensation amount generated by the system accurately corresponds to the actual needs of the flexible load, effectively improving the system's axial stiffness performance.

[0067] Nonlinear resistance gradient fitting merges compensation quantities from two different dimensions into unified torque data while considering the nonlinear characteristics of the system. The fitting process employs a piecewise continuous function model, mapping the compensation characteristics under different operating conditions to a unified torque space. The piecewise function is designed based on the system's response characteristics under different loads and speeds, ensuring a smooth and continuous compensation torque across the entire operating range.

[0068] The nonlinear drag gradient fitting process first normalizes the deformation suppression compensation torque and axial stiffness compensation to ensure consistency in numerical scale. The normalized data is then preliminarily fused using a weighting function dynamically adjusted based on the current operating state to ensure the contribution of each compensation quantity matches actual requirements. The preliminarily fused data is then converted into gradient characteristics using a nonlinear mapping function, reflecting the variation of the compensation torque with deformation. The gradient characteristic curve is smoothed using spline interpolation to eliminate discontinuities.

[0069] The smoothed gradient characteristics are combined with the system's current state parameters to generate the final stiffness compensation torque data. This stiffness compensation torque data possesses good directionality and amplitude characteristics, enabling precise compensation for deformations of different directions and degrees. Through this nonlinear gradient fitting method, the system-generated compensation torque effectively suppresses deformation while maintaining motion smoothness, significantly improving the motion accuracy and stability of the flexible load system. This embodiment decomposes the feedforward reference coefficients into flexible stiffness dimensions, mapping unified parameters to both axial and radial dimensions. This allows the control system to finely adjust stiffness characteristics in different directions, improving the system's adaptability to complex environmental changes. Deformation direction correction is performed on the disturbance-resistant feedforward compensation data based on the radial stiffness compensation coefficient, ensuring that the direction of the compensation torque is consistent with the actual deformation direction, effectively suppressing radial deformation under various operating conditions. Spatial trajectory calculation is performed on the servo motor data to obtain the actual motion state of the flexible load, providing a reliable basis for subsequent compensation calculations. Spatial compensation calculations are performed on the axial stiffness gain parameters and the flexible load motion data to generate accurate axial stiffness compensation, effectively improving the system's axial stiffness performance.

[0070] In one embodiment, spatial trajectory calculation is performed on servo motor data to obtain flexible load motion data, including: Flexible deformation friction analysis is a fundamental step in flexible resistance regulation servo control methods. This process begins with the raw data from the servo motor, acquiring key parameters such as current, voltage, speed, and position during motor operation via a data acquisition system. After preprocessing, this data enters the deformation friction analysis module, which establishes a mapping relationship between the motor's operating state and the frictional characteristics of the rotor surface.

[0071] During the analysis process, the difference between the motor output torque and the actual load is analyzed to identify the frictional resistance component caused by the deformation of the flexible material. Deformation friction analysis employs dynamic boundary layer theory to establish a correlation between microscopic deformation on the contact surface and macroscopic frictional force. The analysis engine constructs a frictional stress distribution map on the rotor surface by measuring the minute torque fluctuations during rotor rotation and combining parameters from the material property library. This distribution map is represented as a vector field in three-dimensional space, where the direction of each vector represents the direction of the frictional force, and the magnitude represents the frictional force intensity. The analysis process also considers the influence of temperature on material properties, correcting the friction coefficient through a temperature compensation function. The final output rotor friction vector contains three key dimensions: direction, magnitude, and time-varying characteristics, providing accurate frictional boundary conditions for subsequent rigid-flexible transmission coupling. Accurate analysis of the rotor friction vector plays a decisive role in reducing system resistance fluctuations and improving control precision.

[0072] By establishing the transfer function between the motor output and the actual motion at the load end, the nonlinear deformation problem in the flexible transmission system is solved. The coupling process uses the rotor friction vector as input and, combined with the mechanical structural parameters of the transmission chain, constructs a complete force transmission path. The coupling model divides the transmission system into two parts: a rigid segment and a flexible segment. The rigid segment uses a rigid body mechanics model to describe the relationship between force and motion, while the flexible segment introduces a material elastic deformation model. The two segments maintain continuity in force and displacement through interface conditions, forming a complete coupled system.

[0073] In the coupled calculation, the rotor friction vector is first converted into torque at the entrance of the transmission chain and then transmitted along the chain, taking into account factors such as gear ratio, bearing friction, and the elasticity of connecting components. When the torque is transmitted to the flexible section, the system calculates the deformation of the flexible component based on the stress-strain relationship of the material and converts the deformation into displacement deviation at the load end. Through iterative calculation, the system determines the load end position that satisfies force balance and geometric constraints, i.e., the load end trajectory coordinates. These coordinates are represented by a sequence of points in three-dimensional space, fully describing the position, velocity, and acceleration characteristics of the load during motion. The accurate acquisition of the load end trajectory coordinates lays the foundation for subsequent drag gradient calculations.

[0074] The drag gradient calculation is the process of converting load-end trajectory information into a system drag distribution. Based on the load-end trajectory coordinates and combined with preset feedforward reference coefficients, this calculation analyzes the drag variation trends in each direction during motion. The feedforward reference coefficients are a pre-determined set of parameters based on historical system operating data and material properties, used to calibrate the accuracy of the drag calculation model. The drag gradient calculation extracts motion direction change information from the load-end trajectory coordinate sequence, identifying the curvature and torsional characteristics of the motion trajectory. The calculation engine combines the trajectory characteristics with the elastic deformation characteristics of the material to derive the degree of deformation of the flexible component in each direction.

[0075] Multiplying the degree of deformation by the material's stiffness characteristic yields the internal stress distribution generated by the deformation. The projection of this internal stress onto the contact surface constitutes the deformation-induced drag. The drag gradient calculation processes each point on the trajectory, forming a complete drag gradient field. This gradient field describes the changes in the magnitude and direction of the drag during load movement; regions with large gradient values ​​indicate drastic drag changes and high system control difficulty. The calculation results of the deformation-induced drag gradient directly affect the subsequent drag compensation effect and are the core of the entire control method. Drag gradient information enables the control system to anticipate future drag changes during movement, providing a basis for proactive control.

[0076] The reverse motion conversion is the process of transforming drag gradient information into the calculation of actual compensation. This step takes the deformation-induced drag gradient as input and, through reverse derivation, calculates the compensation displacement required to eliminate or reduce the drag effect. The reverse motion conversion first establishes a mapping function between drag and deformation, which describes how a specific deformation generates corresponding drag.

[0077] By inverting the function, the system obtains the transformation relationship from the target drag to the desired deformation. The inverse transformation process considers the nonlinear characteristics of the material and improves computational efficiency through piecewise linearization. The transformation algorithm extracts key points from the drag gradient field; these points are typically located in regions where drag changes significantly. For each key point, the system calculates the deformation that can neutralize the drag at that point, forming a local compensation scheme. The local compensation scheme undergoes spatial smoothing to ensure the spatial continuity of the compensated deformation and avoid the generation of new vibrations during the compensation process.

[0078] The processed compensated deformation constitutes a complete deformation field, describing the displacement adjustment amounts that need to be applied to various parts of the system. The drag compensation deformation displacement is represented as a vector field, with each vector pointing in the compensation direction and its magnitude representing the compensation amount. These compensation amounts will be combined with the original trajectory coordinates in subsequent servo space fusion to form the corrected motion commands. The precise calculation of the drag compensation deformation displacement ensures that the system can actively counteract drag fluctuations in flexible load motion, improving motion stability.

[0079] Servo spatial fusion is the final step in the flexible drag control method. This step integrates the original trajectory data with the compensation data to generate optimized motion control commands. Spatial fusion takes the load-end trajectory coordinates and drag compensation deformation displacement as inputs, and constructs the corrected motion space through coordinate transformation and data synthesis. The fusion process first transforms the two sets of data to the same reference coordinate system to ensure data consistency. After transformation, the system applies the corresponding compensation displacement to each point on the trajectory to obtain the corrected spatial position. The fusion algorithm uses a weighted average method, dynamically adjusting the weight ratio of the original trajectory and compensation amount according to the current system state. The weight adjustment considers factors such as motion speed, load changes, and external disturbances, maximizing the compensation effect while ensuring control stability.

[0080] The fusion results form complete motion data for the flexible load, including multi-dimensional information such as position, velocity, acceleration, and torque. This data is organized in time series form and directly fed into the servo controller for execution. The main difference between flexible load motion data and traditional rigid body control commands is that it includes pre-compensation for flexible deformation, actively suppressing vibration and positional deviations during motion. The output of servo spatial fusion enables the control system to accurately track the set trajectory while adapting to the special requirements of flexible loads, achieving high-precision, low-vibration motion control. The fused motion data is fed back to the servo actuator, completing the entire flexible resistance adjustment control closed loop.

[0081] This embodiment uses flexible deformation friction analysis of servo motor data to accurately obtain the rotor friction vector, providing accurate frictional boundary conditions for the system, effectively reducing resistance fluctuations and improving control accuracy. The rigid-flexible transmission coupling technology based on the rotor friction vector solves the nonlinear deformation problem in the flexible transmission system, making the load-end trajectory coordinates more accurate. The calculation of deformation-induced resistance gradient allows the control system to anticipate future resistance changes during motion, providing a reliable basis for forward-looking control. Accurate calculation of resistance compensation deformation displacement ensures the system can actively counteract resistance fluctuations during flexible load motion, significantly improving motion stability. Finally, the flexible load motion data generated through servo spatial fusion includes pre-compensation for flexible deformation, actively suppressing vibration and position deviations during motion, achieving high-precision, low-vibration motion control, and significantly improving the response speed and positioning accuracy of the flexible load system.

[0082] In one embodiment, servo control commands are constructed based on servo motor data, feedforward reference coefficients, and stiffness compensation torque data, including: Servo motor data contains mixed information of various physical quantities. Obtaining the rotor angular position signal and rotor angular velocity component through parameter separation is the foundation for achieving precise control. In this step, the raw data collected by the motor encoder includes multi-dimensional information such as position, velocity, and acceleration, which is stored in the form of digital signals. The parameter separation process uses signal analysis technology to deconstruct the composite signal into independent physical quantities. The rotor angular position signal represents the instantaneous angular position of the motor shaft, usually in radians or degrees, recording the rotation angle of the rotor relative to a reference zero point. The angular velocity component reflects the rotor's rotational speed, representing the change in angular position per unit time. Digital filtering methods are used during parameter separation to eliminate measurement noise and interference, ensuring a high signal-to-noise ratio for the separated signal. The filter design considers the system's dynamic response characteristics, avoiding the introduction of excessive phase delay.

[0083] The position signal employs a high-precision interpolation algorithm to improve resolution, enabling the system to detect minute angular changes. The velocity component is obtained through position differential calculation, and time-window smoothing is used to reduce transient fluctuations. The accuracy of parameter separation directly affects the accuracy of subsequent control; therefore, the separation algorithm is optimized to reduce computational delay and improve real-time performance. This step outputs high-precision rotor angular position and angular velocity components, laying the data foundation for subsequent phase synchronization and velocity coupling.

[0084] There is a phase relationship between the stiffness compensation torque data and the rotor angular position. Precise application of the compensation torque is achieved through phase synchronization. The system reads preset stiffness compensation torque data, which is typically generated based on the system's resistance characteristic model and includes the required compensation torque values ​​for different angular positions. The phase synchronization process extracts the torque value corresponding to the current angular position from the stiffness compensation torque data based on the current rotor angular position signal. This correspondence is achieved through table lookup interpolation, ensuring precise matching between the compensation torque and the actual angular position. To overcome mechanical backlash and frictional nonlinearity issues in the system, a phase correction mechanism is introduced during the phase synchronization process to compensate for time delay effects in the system.

[0085] The phase compensation torque vector is represented as a function of angular position, with its elements corresponding to the torque compensation value at each angle. The vector elements are arranged in ascending order of angle for easy indexing and interpolation calculations. Phase synchronization considers the continuity of torque over the angular period, avoiding sudden torque changes when the angle crosses the period boundary. The synchronization process also dynamically adjusts the amplitude of the compensation torque based on the current system operating state, adapting to different load conditions. The phase compensation torque vector output in this step precisely corresponds to the current rotor position, ensuring that the compensation torque acts at the most appropriate time, improving the smoothness and accuracy of the system response.

[0086] Angular velocity modulates the compensation torque, and adaptive adjustment of the compensation torque as speed changes is achieved through velocity coupling. In this step, the system uses the rotor angular velocity component as a modulation factor to correct the phase compensation torque vector. Velocity coupling employs a nonlinear mapping relationship, applying different coupling functions in different speed ranges to adapt to the dynamic characteristics of the system at various speeds. In the low-speed region, the compensation effect is enhanced to overcome static friction and starting resistance; in the high-speed region, compensation is appropriately reduced to avoid oscillations caused by over-compensation. The velocity coupling process considers inertial effects, introducing speed look-ahead compensation to predict the system's motion trend in advance and improve the timeliness of compensation.

[0087] The velocity-adaptive compensation component integrates position-dependent stiffness characteristics and velocity-dependent damping characteristics to achieve a comprehensive compensation effect. The coupling algorithm employs an adaptive weight allocation mechanism, dynamically adjusting the compensation intensity based on the rate of velocity change, providing a smooth transition during rapid velocity changes. Velocity coupling also considers directional factors, using a symmetrical compensation strategy for both forward and reverse motion to ensure consistent system performance in bidirectional motion. The velocity-adaptive compensation component output in this step can automatically adjust its compensation characteristics according to changes in system velocity, adapting to various dynamic conditions and improving the system's stability and tracking accuracy under variable speed conditions.

[0088] The feedforward reference coefficients provide the basic compensation torque, which, when superimposed with the speed-adaptive compensation component, forms a complete disturbance rejection compensation strategy. In this step, preset feedforward reference coefficients are read; these coefficients reflect the basic compensation requirements under standard operating conditions and are identified by the system. Linear superposition employs a weighted summation method, combining the speed-adaptive compensation component with the feedforward reference compensation. The superposition weights are dynamically adjusted according to the current system state; during steady-state operation, the proportion of reference compensation is increased to improve system stability; during transient processes, the proportion of speed-adaptive compensation is increased to improve system responsiveness. A smooth transition mechanism is introduced during the superposition process to avoid shocks caused by sudden changes in compensation torque.

[0089] The disturbance rejection compensation composite torque data comprises three parts: position-dependent compensation, velocity-dependent compensation, and reference compensation, forming a comprehensive compensation strategy. The composite torque undergoes amplitude limiting to prevent exceeding system physical constraints and protect the motor and mechanical structure. The superposition algorithm considers the frequency characteristics of each compensation component, ensuring appropriate compensation effects at different frequencies. The disturbance rejection compensation composite torque data output in this step comprehensively considers both the static and dynamic characteristics of the system, effectively resisting the influence of external disturbances and internal nonlinear factors, thus improving the system's robustness and accuracy.

[0090] The fusion of periodic control commands and compensation torque is key to achieving flexible resistance regulation. The final servo control commands are generated through command reconstruction. In this step, the system corrects the original periodic control commands by incorporating disturbance rejection compensation synthetic torque data. Command reconstruction employs a feedforward compensation strategy, converting the compensation torque into an equivalent current command increment, which is then superimposed on the basic current command. The reconstruction process considers the motor's torque coefficient, achieving precise conversion from torque commands to current commands. Command reconstruction also introduces phase correction to compensate for electrical and mechanical delays in the system, ensuring that compensation operates at the optimal timing. The servo control commands consist of three components: position command, speed command, and torque command, forming a complete three-loop control structure.

[0091] During the command reconfiguration process, the system monitors motor status parameters in real time and implements command limiting measures when the motor approaches saturation to avoid overcurrent and overvoltage. The reconfiguration algorithm employs an adaptive adjustment mechanism, dynamically adjusting the compensation gain based on the system response characteristics to maintain system stability under various operating conditions. Servo control commands undergo timing optimization to ensure orderly execution within the servo control cycle, avoiding command backlog and execution conflicts. The servo control commands output in this step integrate basic motion control requirements and disturbance rejection compensation requirements, enabling precise adjustment of flexible resistance and improving the system's adaptability and maneuverability in complex environments.

[0092] This embodiment achieves precise control of the resistance characteristics of the servo system through the implementation of a flexible resistance adjustment servo control method, improving the system's adaptability under complex operating conditions. Position and speed parameters are separated from the servo motor data to obtain high-precision angular position and angular velocity information, laying a data foundation for subsequent compensation. Phase synchronization technology based on the rotor angular position signal ensures that the compensation torque acts at the optimal time, significantly improving the smoothness of the system response. A speed coupling mechanism is introduced to enable the compensation torque to adaptively adjust with the rotational speed. A comprehensive disturbance rejection compensation strategy is constructed through the linear superposition of the speed-adaptive compensation component and the feedforward reference coefficient, enhancing the system's ability to resist external interference. The final command reconfiguration integrates basic motion control and disturbance rejection compensation requirements, enabling the system to exhibit excellent flexible resistance characteristics, improving control accuracy and user experience.

[0093] In one embodiment, the phase compensation torque vector is velocity-coupled based on the angular velocity component to obtain a velocity-adaptive compensation component, including: Angular velocity components contain information about the internal resistance characteristics of flexible materials during dynamic processes. These characteristics are quantified into viscoelastic hysteresis damping parameters through parameter extraction. In this step, the angular velocity components of the motor rotor reflect the response behavior of the flexible connector at different deformation rates. The internal resistance parameter extraction process is based on time-domain analysis of the angular velocity components, capturing the correspondence between velocity change trends and damping effects. The angular velocity data is filtered to eliminate high-frequency noise interference and retain effective dynamic characteristics.

[0094] The extraction process focuses on the rate of change of angular velocity, an indicator that reflects the rate of rearrangement of the molecular chain structure within the flexible material and is directly related to the material's viscoelastic behavior. The viscoelastic hysteresis damping parameter comprises two main components: the static damping coefficient and the dynamic hysteresis factor. The static damping coefficient describes the drag characteristics of the material at a steady velocity, while the dynamic hysteresis factor characterizes the hysteresis effect of the material during velocity changes.

[0095] The parameter extraction process employs a segmented approach, independently identifying material properties across different velocity ranges and capturing nonlinear characteristics. The periodic variation pattern of angular velocity is used to identify the fatigue characteristics of the material under cyclic loading; these characteristics are expressed through a dynamic memory factor. The extracted viscoelastic hysteresis damping parameters are normalized to ensure consistency across different operating conditions. The viscoelastic hysteresis damping parameters output in this step provide the foundational data for subsequent hysteresis nonlinear compensation, ensuring that the compensation torque matches the actual internal resistance characteristics of the material.

[0096] Flexible materials exhibit significant hysteresis nonlinear characteristics. Hysteresis nonlinear compensation allows the phase compensation torque vector to more accurately reflect the actual material behavior. In this step, the viscoelastic hysteresis damping parameter is combined with the phase compensation torque vector to construct a hysteresis compensation model. The compensation process is based on the torque-displacement hysteresis curve, which describes the asymmetric response characteristics of flexible materials during loading and unloading. Hysteresis nonlinear compensation introduces a memory term, making the current compensation value dependent not only on the current state but also on historical states, thus simulating the material's memory effect.

[0097] The compensation process distinguishes between the torque rise and fall phases, employing different compensation strategies for each stage to accurately recreate the hysteresis loop characteristics. The phase compensation torque vector maintains its angular correlation during the compensation process while simultaneously adding velocity history dependence, forming a two-dimensional mapping relationship. The material internal resistance compensation baseline includes static and dynamic components; the static component corresponds to the material's equilibrium resistance, while the dynamic component reflects the additional resistance during deformation. The compensation process considers the material's elastic limit, adjusting the compensation strategy as it approaches the limit to prevent model failure.

[0098] Hysteresis compensation employs a progressive iterative method, approximating the actual material behavior through multiple iterations to improve model accuracy. The material internal resistance compensation baseline value undergoes amplitude calibration to ensure that the compensation torque amplitude is comparable to the actual internal resistance. The material internal resistance compensation baseline value output in this step accurately reflects the nonlinear internal resistance characteristics of the flexible material, providing fundamental data for subsequent rate coupling.

[0099] The internal resistance characteristics of flexible materials are closely related to their deformation rate. The modulation effect of the deformation rate on the internal resistance is obtained through deformation rate analysis. In this step, the angular velocity component is considered as an indirect representation of the deformation rate of the flexible connector, and is converted into the actual deformation rate through a geometric mapping relationship. The deformation rate analysis process considers the installation configuration and geometric characteristics of the flexible connector, transforming angular motion into linear deformation. The analysis process introduces the material stiffness-strain relationship, considering the nonlinear change of stiffness under different deformation amounts, making the analysis results more consistent with actual material behavior. The deformation rate analysis distinguishes between the purely elastic deformation region and the viscoelastic deformation region, and adopts corresponding rate analysis models for different regions.

[0100] The analytical process focuses on the directionality of deformation rate, distinguishing between tensile and compressive states, and capturing the heterogeneous response of the material in different deformation directions. The deformation rate coupling factor is expressed as a nonlinear function of the rate, exhibiting high sensitivity in the low-speed region and tending to saturate in the high-speed region, reflecting the frictional and entanglement characteristics of the molecular chains inside the material at different rates. The coupling factor calculation adopts a piecewise continuous method to ensure a smooth transition during rate changes. The deformation rate analysis also considers multi-axis coupling effects, comprehensively taking into account the deformation contributions in the principal and secondary axis directions to form a three-dimensional coupling model. The analytical results are normalized to ensure that the coupling factor has a consistent physical meaning under different working conditions.

[0101] The viscoelastic work equivalent conversion establishes a correspondence between energy dissipation and compensating torque during material deformation, achieving precise energy angular compensation. In this step, the deformation rate coupling factor and the material's internal resistance compensation baseline are correlated through the energy equivalence principle to calculate the equivalent internal resistance compensation torque. The conversion process is based on the power balance equation, equating the energy dissipation rate during deformation to the product of the compensation torque and angular velocity. The viscoelastic work equivalent considers the energy storage and release characteristics of the material during deformation, distinguishing between elastic energy and dissipated energy. Elastic energy corresponds to recoverable energy, compensated by the elastic torque; dissipated energy corresponds to irrecoverable energy loss, compensated by the damping torque.

[0102] The conversion process introduces an energy transfer function to describe the dynamic transformation relationship of energy between the stored and dissipated states. The internal resistance compensation torque data includes phase-dependent and rate-dependent components, which are comprehensively balanced using energy weighting coefficients. The conversion algorithm considers the time delay effect of energy transfer, introducing a time delay term in the torque calculation to simulate the delay in the process of energy transfer from macroscopic deformation to microscopic dissipation. During the viscoelastic work equivalent conversion, the system dynamically evaluates the energy dissipation efficiency and adjusts the conversion parameters according to the current state of the material, adapting to characteristic drift caused by material fatigue and temperature changes. The conversion results undergo energy consistency verification to ensure that the power generated by the compensation torque is equivalent to the actual material dissipation power. The internal resistance compensation torque data output in this step accurately simulates the internal resistance characteristics of flexible materials from an energy perspective, providing an energy basis for subsequent time-temperature equivalent correction.

[0103] The properties of flexible materials are significantly affected by time and temperature. Time-temperature equivalent correction is used to adapt the compensation torque to the actual working environment. In this step, the preset characteristic data of the flexible connectors in the equipment includes the material's performance parameters under different time scales and temperature conditions. This data is typically obtained from material testing or historical operating records. Time-temperature equivalent correction, based on the principle of time-temperature equivalence, unifies the effects of time and temperature onto a single equivalent parameter. The correction process considers the aging effect of the material; as the service time increases, the stiffness and damping characteristics of the flexible material change, and these changes are quantified using a time correction factor. Temperature affects flexible materials by causing stiffness softening and viscosity changes, and this effect is compensated for using a temperature correction factor.

[0104] The correction algorithm employs a hierarchical structure, first performing time-scale correction, then temperature correction, and finally combining the two to form a comprehensive correction effect. During the time-temperature equivalent correction process, the system estimates the temperature state of the flexible connector in real time, calculating the actual operating temperature of the material based on ambient temperature, operating load, and a heat conduction model. The correction process also considers temperature distribution non-uniformity, employing a zoned correction strategy for different temperature regions. The corrected velocity-adaptive compensation component exhibits environmental adaptability, maintaining consistent compensation effects across different temperatures and usage stages.

[0105] The time-temperature equivalent correction also introduces a predictive compensation mechanism, which predicts short-term changes in material properties based on temperature change trends, adjusts compensation parameters in advance, and improves system responsiveness. The velocity-adaptive compensation component output by this step comprehensively considers the dynamic characteristics, energy characteristics, and environmental adaptability of flexible materials, enabling precise adjustment of flexible drag and improving the system's handling quality and consistency in complex environments.

[0106] This embodiment extracts the internal resistance parameters of the flexible material from the angular velocity component, achieving precise quantification of the material's viscoelastic hysteresis damping characteristics and providing a reliable foundation for subsequent compensation. Hysteresis nonlinear compensation is applied to the phase compensation torque vector based on the extracted parameters, effectively overcoming the insufficient compensation problem caused by neglecting the material memory effect in traditional control methods. The flexible deformation rate analysis technique accurately captures the heterogeneous response of the material at different deformation rates, significantly improving the system's adaptability to dynamic conditions. The viscoelastic work equivalent conversion method establishes the correspondence between the deformation process and the compensation torque from an energy perspective, making the compensation more accurate and physically meaningful. The time-temperature equivalent correction mechanism effectively addresses the impact of environmental temperature changes and material aging on system performance, ensuring long-term stable operation. The overall scheme achieves precise adjustment of flexible resistance, improving the control accuracy and consistency of the system under complex conditions, and providing a higher quality operating experience for flexible mechanical systems.

[0107] Reference Figure 2 As shown, the present invention also provides a flexible resistance adjustment servo control system for equipment, applied to any of the above-mentioned flexible resistance adjustment servo control methods, comprising: The acquisition module is used to acquire the resistance torque data and periodic control commands of the target equipment, perform disturbance parameter expansion estimation, and obtain the total disturbance torque data of the system and the equivalent stiffness information of the load. The analysis module is used to acquire servo motor data of the target device, perform multiple feedforward compensations on the total disturbance torque data of the system to obtain disturbance rejection feedforward compensation data, and combine the load equivalent stiffness information to adjust the stiffness reference to obtain the feedforward reference coefficient. The correlation module is used to couple the torque parameters of the anti-disturbance feedforward compensation data based on the feedforward reference coefficient to obtain the stiffness compensation torque data. The processing module is used to construct instructions based on servo motor data, feedforward reference coefficients, and stiffness compensation torque data to obtain servo control instructions.

[0108] This invention provides a flexible resistance adjustment servo control system for equipment. By estimating the total disturbance torque data and equivalent load stiffness information through disturbance parameter expansion, it can more accurately assess the dynamic characteristics of the system under complex operating conditions, thereby improving the accuracy of servo control and providing a more reliable foundation for stable equipment operation. Employing multiple feedforward compensation techniques combined with equivalent load stiffness information for stiffness reference adjustment enables refined adaptation to different load characteristics, helping the system maintain stable response under changing operating conditions and avoiding over- or under-control. Based on the feedforward reference coefficient, torque parameter coupling using disturbance feedforward compensation data ensures efficient system operation under different operating states and load conditions, reducing control delay and oscillation, and improving the overall dynamic performance of the system. By comprehensively analyzing servo motor data and stiffness compensation torque data, more reasonable servo control commands are constructed, and intelligent adjustment strategies achieve optimized operation of the entire system, effectively improving response speed and reducing energy consumption and mechanical wear. By considering the periodic control commands of the target equipment, the control strategy can be flexibly adjusted according to the characteristics and changing needs of different application scenarios, making the system more adaptable to diverse industrial environments and task requirements.

[0109] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0110] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A servo control method for adjusting the flexible resistance of equipment, characterized in that, include: Acquire the resistance torque data and periodic control commands of the target equipment, perform disturbance parameter expansion estimation, and obtain the total disturbance torque data of the system and the equivalent stiffness information of the load. The servo motor data of the target device is obtained, and multiple feedforward compensations are performed on the total disturbance torque data of the system to obtain disturbance rejection feedforward compensation data. The stiffness reference is adjusted in combination with the load equivalent stiffness information to obtain the feedforward reference coefficient. Based on the feedforward reference coefficient, the disturbance rejection feedforward compensation data is coupled with torque parameters to obtain stiffness compensation torque data; The servo motor data is separated into position and velocity parameters to obtain a rotor angular position signal and a rotor angular velocity component. The stiffness compensation torque data is then phase-synchronized based on the rotor angular position signal to obtain a phase compensation torque vector. The phase compensation torque vector is then velocity-coupled based on the angular velocity component to obtain a speed-adaptive compensation component. This speed-adaptive compensation component is then linearly superimposed with the feedforward reference coefficient to obtain disturbance rejection compensation composite torque data. Finally, the periodic control command is reconstructed based on the disturbance rejection compensation composite torque data to obtain a servo control command.

2. The servo control method for adjusting the flexible resistance of equipment according to claim 1, characterized in that, The process of acquiring the resistance torque data and periodic control commands of the target device, performing disturbance parameter expansion estimation, and obtaining the total system disturbance torque and load equivalent stiffness information includes: The target device is subjected to motor torque acquisition and periodic signal monitoring to obtain the resistance torque data and the periodic control command; A disturbance observer model is constructed based on the drag torque data and the periodic control command, and multi-dimensional state variable expansion is performed to obtain the initial value of the total disturbance torque of the system. Based on the initial value of the total disturbance torque of the system and the periodic control command, the load end is dynamically identified and the parameters are converged to obtain the equivalent stiffness information of the load. The initial value of the total disturbance moment of the system and the equivalent stiffness information of the load are iteratively mapped and corrected to obtain the total disturbance moment data of the system.

3. The servo control method for adjusting the flexible resistance of equipment according to claim 1, characterized in that, The process involves acquiring the servo motor data of the target device, performing multiple feedforward compensations on the total disturbance torque data of the system to obtain disturbance rejection feedforward compensation data, and combining this with the load equivalent stiffness information to adjust the stiffness reference and obtain the feedforward reference coefficients, including: The target device is sampled for motor encoding signals and incrementally decoded to obtain the servo motor data; The total disturbance torque of the system is decoupled and separated to obtain the low-frequency disturbance torque component and the high-frequency resonance suppression component. Based on the servo motor data, phase lead compensation is performed on the low-frequency interference torque component to obtain the anti-interference feedforward compensation data; Based on the servo motor data, an energy coupling calculation is performed on the high-frequency resonance suppression component to obtain the resonance frequency adjustment amount; The feedforward reference coefficient is obtained by performing stiffness reference damping mapping based on the resonant frequency adjustment and the equivalent stiffness information of the load.

4. The equipment flexible resistance adjustment servo control method according to claim 3, characterized in that, The step of performing energy coupling calculations on the high-frequency resonance suppression component based on the servo motor data to obtain the resonance frequency adjustment amount includes: The servo motor data is processed for real-time load inertia identification to obtain the equivalent load inertia change rate. The equivalent load inertia change rate and the equivalent load stiffness information are processed to calculate the stiffness inertia matching degree, and a stiffness attenuation factor is generated. The high-frequency resonance suppression component is dynamically notch band adjusted according to the stiffness attenuation factor to obtain the resonance suppression amount. The inherent frequency is calculated based on the servo motor data to obtain the characteristic value of the load resonant frequency. The resonant suppression amount and the load resonant frequency characteristic value are used to extract the dominant frequency energy to obtain the resonant frequency adjustment amount.

5. The servo control method for adjusting the flexible resistance of equipment according to claim 1, characterized in that, The process of coupling the disturbance rejection feedforward compensation data with torque parameters based on the feedforward reference coefficient to obtain stiffness compensation torque data includes: The feedforward reference coefficients are decomposed into flexible stiffness dimensions to obtain axial stiffness gain parameters and radial stiffness compensation coefficients. The deformation direction is corrected based on the radial stiffness compensation coefficient to obtain the deformation suppression compensation torque; Spatial trajectory calculation is performed on the servo motor data to obtain flexible load motion data; The axial stiffness gain parameter and the flexible load motion data are used to perform spatial compensation calculation to obtain the axial stiffness compensation amount. The deformation suppression compensation torque and the axial stiffness compensation amount are fitted using a nonlinear resistance gradient to obtain the stiffness compensation torque data.

6. The servo control method for adjusting the flexible resistance of equipment according to claim 5, characterized in that, The step of performing spatial trajectory calculation on the servo motor data to obtain flexible load motion data includes: The servo motor data is subjected to flexible deformation friction analysis to obtain the rotor friction vector; Based on the rotor friction vector, the target device is subjected to rigid-flexible transmission coupling to obtain the load end trajectory coordinates; Based on the feedforward reference coefficient, the resistance gradient of the load end trajectory coordinates is calculated to obtain the deformation-induced resistance gradient. The deformation-induced resistance gradient is reversed and converted to obtain the resistance-compensated deformation displacement. The load end trajectory coordinates and the resistance compensation deformation displacement are fused in servo space to output the flexible load motion data.

7. The servo control method for adjusting the flexible resistance of equipment according to claim 1, characterized in that, The step of velocity coupling the phase compensation torque vector based on the angular velocity component to obtain a velocity-adaptive compensation component includes: The internal resistance parameters of the flexible material are extracted from the angular velocity components to obtain the viscoelastic hysteresis damping parameters; Based on the viscoelastic hysteresis damping parameters, the phase compensation torque vector is subjected to hysteresis nonlinear compensation to obtain the material internal resistance compensation base value. The deformation rate coupling factor is obtained by performing flexible deformation rate analysis on the angular velocity components. The deformation rate coupling factor and the material internal resistance compensation base value are converted into viscoelastic work equivalent to obtain the internal resistance compensation torque data. The internal resistance compensation torque data is corrected by time-temperature equivalent adjustment based on the preset characteristic data of the flexible connector of the equipment to obtain the speed-adaptive compensation component.

8. A flexible resistance adjustment servo control system for equipment, characterized in that, The servo control method for adjusting the flexible resistance of a device according to any one of claims 1-7 includes: The acquisition module is used to acquire the resistance torque data and periodic control commands of the target device, perform disturbance parameter expansion estimation, and obtain the total disturbance torque data and load equivalent stiffness information of the system. The analysis module is used to acquire the servo motor data of the target device, perform multiple feedforward compensation on the total disturbance torque data of the system to obtain disturbance rejection feedforward compensation data, and combine the load equivalent stiffness information to adjust the stiffness reference to obtain the feedforward reference coefficient. The association module is used to couple the disturbance rejection feedforward compensation data with torque parameters based on the feedforward reference coefficient to obtain stiffness compensation torque data. The processing module performs position and velocity parameter separation on the servo motor data to obtain a rotor angular position signal and a rotor angular velocity component; performs phase synchronization on the stiffness compensation torque data based on the rotor angular position signal to obtain a phase compensation torque vector; performs velocity coupling on the phase compensation torque vector based on the angular velocity component to obtain a speed-adaptive compensation component; linearly superimposes the speed-adaptive compensation component with the feedforward reference coefficient to obtain disturbance rejection compensation composite torque data; and reconstructs the periodic control command based on the disturbance rejection compensation composite torque data to obtain a servo control command.

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