Self-adaptive axial magnetic field control method and system of robot joint module

Abstract

The invention relates to a self-adaptive axial magnetic field control method and system for a robot joint module, and the method comprises the steps: inputting an input control voltage into a pre-established dynamic mathematical model, and obtaining an axial magnetic field component and a torque current component; constructing a state variable according to the rotor electrical angular velocity, the axial magnetic field component and the torque current component; determining a dynamic equation of the axial magnetic field component and the torque current component changing along with time according to the first matrix parameter, the state variable, the rotor electrical angular velocity, the input control voltage and a plurality of lumped disturbance quantities of different disturbance sources; according to the dynamic equation, the second matrix parameter, the saturation function, the current tracking error and the boundary layer thickness, performing gain normalization on the input control voltage to obtain a target control voltage; and providing the target control voltage to the axial driving motor as a target, and updating a parameter adaptive rate according to the real-time current tracking error and a third matrix parameter of the axial driving motor so as to dynamically adjust the target control voltage by updating the parameter adaptive rate.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and in particular to an adaptive axial magnetic field control method and system for a robot joint module. Background Technology

[0002] With the rapid development of high-precision robotics technology, the requirements for the driving performance of joint modules are increasing. Joint modules using frameless torque motors or magnetic bearings are widely used due to their advantages such as high torque density and low mechanical loss. Their core driving force comes from the precise control of the air gap magnetic field between the motor stator and rotor. Traditional axial magnetic field control methods are usually based on a precise mathematical model of the motor and adopt a field-oriented control strategy with fixed parameters. However, in actual operation, motor parameters will drift due to factors such as temperature rise, magnetic saturation, and mechanical stress, and load disturbances are uncertain, leading to a decrease in the performance of controllers based on the nominal model, manifested as increased torque pulsation, slow dynamic response, or even instability.

[0003] In relevant scenarios, although online parameter identification or disturbance observers can be used for compensation, they do not deeply integrate parameter adaptation with magnetic field axial component control. Furthermore, during dynamic trajectory tracking, the overall ability to suppress multi-source uncertainties is limited, making it difficult to maintain high-precision and high-efficiency magnetic field control under wide speed range and variable load conditions, resulting in low accuracy of robot joint control. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive axial magnetic field control method and system for robot joint modules, which aims to improve the dynamic performance and robustness of robot joint modules by enabling online adjustment of control parameters and adaptive compensation for system uncertainties.

[0005] To achieve the above objectives, a first aspect of this disclosure provides an adaptive axial magnetic field control method for a robot joint module, comprising: The input control voltage of the axial drive motor corresponding to the robot joint module is input into a pre-established dynamic mathematical model to obtain the axial magnetic field component and torque current component output by the dynamic mathematical model. The axial magnetic field component and the torque current component are obtained by decoupling the input control voltage in the rotational direct axis and quadrature axis coordinate system. Based on the obtained rotor electric angular velocity, axial magnetic field component, and torque current component of the axial drive motor, state variables for the axial drive motor are constructed. Based on the first matrix parameters of the axial drive motor, the state variables, the rotor electric angular velocity, the input control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources, determine the dynamic equations for the axial magnetic field component and the torque current component of the axial drive motor as a function of time. Based on the dynamic equation, the second matrix parameters of the axial drive motor, the saturation function, the current tracking error, and the boundary layer thickness, the input control voltage is normalized to obtain the target control voltage. The target control voltage is provided to the axial drive motor as a target, and the parameter adaptive rate is updated based on the real-time current tracking error and the third matrix parameters of the axial drive motor, so as to dynamically adjust the target control voltage by updating the parameter adaptive rate.

[0006] In a preferred embodiment, the first matrix parameters include an inductance matrix, a resistance matrix, an antisymmetric matrix, and a gain matrix. The step of determining the dynamic equations for the axial magnetic field component and the torque current component of the axial drive motor as a function of time, based on the matrix parameters of the axial drive motor, the state variables, the rotor electrical angular velocity, the input control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources, includes: Based on the resistance matrix and the state variables, determine the voltage drop parameters of the motor windings of the axial drive motor caused by the resistance. Based on the rotor electric angular velocity, the inductance matrix, the antisymmetric matrix, and the state variables, determine the kinetic electromotive force in the rotating coordinate system; The actual applied control voltage is determined based on the gain matrix and the input control voltage. Based on the voltage drop parameter, the kinetic electromotive force, the applied control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources, the dynamic equations for the axial magnetic field component and the torque current component of the axial drive motor as a function of time are determined.

[0007] In a preferred embodiment, the dynamic equation is constructed using the following formula: L×di / dt=-R×i-ω×J×L×i+K×u-τ_d; Where i is the state variable, i=[i_d,i_q,ω]T, L is the inductance matrix, R is the resistance matrix, J is the antisymmetric matrix, K is the gain matrix, u is the input control voltage, τ_d is the lumped disturbance, T represents matrix transpose, i_d is the axial magnetic field component, i_q is the torque current component, ω is the rotor electric angular velocity, t is time, and d represents the derivative.

[0008] In a preferred embodiment, the second matrix parameters include an inductance matrix, a gain matrix, a first positive definite diagonal gain matrix, and a second positive definite diagonal gain matrix. The step of normalizing the gain of the input control voltage based on the dynamic equation, the matrix parameters, the saturation function, the current tracking error, and the boundary layer thickness to obtain the target control voltage includes: The voltage correction parameters are determined based on the first positive definite diagonal gain matrix and the current tracking error; The residual uncertainty parameter is determined based on the second positive definite diagonal gain matrix and the saturation function, wherein the saturation function is constructed based on the current tracking error and the boundary layer thickness; The linear feedback parameters are determined based on the inductance matrix, the voltage correction parameters, and the residual uncertainty parameters. The feedforward model parameters are determined based on the dynamic equation, and the input control voltage is normalized by the gain of the feedforward model parameters, the linear feedback parameters, and the gain matrix to obtain the target control voltage.

[0009] In a preferred embodiment, the target control voltage Utarget is determined by the following formula: Utarget=K -1 ×[Z+τ_d-L×(λ×e+η×sat(e / φ))]; Where e is the current tracking error, λ is the first positive definite diagonal gain matrix, η is the second positive definite diagonal gain matrix, φ is the boundary layer thickness, sat() is the saturation function, Z is the feedforward model parameter, and K is the gain matrix.

[0010] In a preferred embodiment, the third matrix parameters include a positive definite adaptive gain matrix and a regression matrix. The step of updating the parameter adaptive rate based on the real-time acquired current tracking error and the third matrix parameters of the axial drive motor includes: Based on the product of the transpose of the regression matrix and the real-time current tracking error, the correction parameter for the parameter adaptive rate is determined. Based on the positive definite adaptive gain matrix and the correction parameter, the parameter vector to be estimated is determined, and the positive definite adaptive gain matrix is ​​used as a preset learning strategy to adjust and filter the correction parameter. The parameter adaptive rate is updated by integrating the parameter vector to be estimated onto the current parameter adaptive rate.

[0011] In a preferred embodiment, the parameter adaptation rate d(θ_hat) / dt is updated using the following formula: d(θ_hat) / dt = -Γ×βT×e; Where θ_hat is the parameter vector to be estimated, Γ is the positive definite adaptive gain matrix, and β is the regression matrix.

[0012] In a preferred embodiment, providing the target control voltage to the axial drive motor as a target includes: Based on the desired motion trajectory of the robot joints, the desired electromagnetic torque is obtained through inverse dynamics calculation; Based on the maximum torque-current ratio or field weakening control strategy, the desired axial magnetic field and desired torque current are calculated according to the desired electromagnetic torque. The desired axial magnetic field and the desired torque current are input into the reference model to obtain the axial reference magnetic field and torque reference current output by the reference model. The reference model is a second-order linear system: G_ref(s)=ω_n2 / (s2 + 2×ζ×ω_n×s+ω_n2), where ω_n is the natural frequency, ζ is the damping ratio, and s is the Laplace operator. The axial magnetic field error in the current tracking error is determined based on the axial reference magnetic field and the axial magnetic field component, and the torque current error in the current tracking error is determined based on the torque reference current and the torque current component. With the goal of minimizing the axial magnetic field error and the torque current error, the direct-axis voltage and quadrature-axis voltage in the target control voltage are adjusted.

[0013] A second aspect of this disclosure provides an adaptive axial magnetic field control system for a robot joint module, the system comprising: The input module is configured to input the input control voltage of the axial drive motor corresponding to the robot joint module into a pre-established dynamic mathematical model to obtain the axial magnetic field component and torque current component output by the dynamic mathematical model. The axial magnetic field component and the torque current component are obtained by decoupling the input control voltage in the rotational direct axis and quadrature axis coordinate system. The construction module is configured to construct state variables for the axial drive motor based on the obtained rotor electric angular velocity, axial magnetic field component, and torque current component of the axial drive motor. The determination module is configured to determine the dynamic equations of the axial magnetic field component and the torque current component of the axial drive motor as a function of time, based on the first matrix parameters of the axial drive motor, the state variables, the rotor electric angular velocity, the input control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources. The gain normalization module is configured to normalize the input control voltage based on the dynamic equation, the second matrix parameters of the axial drive motor, the saturation function, the current tracking error, and the boundary layer thickness to obtain the target control voltage. The update module is configured to provide the target control voltage to the axial drive motor as a target, and update the parameter adaptive rate based on the real-time acquired current tracking error and the third matrix parameters of the axial drive motor, so as to dynamically adjust the target control voltage by updating the parameter adaptive rate.

[0014] A third aspect of this disclosure provides an electronic device, comprising: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method of any one of the first aspects.

[0015] This invention provides an adaptive axial magnetic field control method and system for robot joint modules. Compared with existing technologies, it has the following advantages: By decoupling control, the dynamic coupling effect between the magnetic field and torque is eliminated, significantly improving control accuracy and response speed. Simultaneously, the dynamic mathematical model can adapt to different motor parameters, enhancing system robustness. State variable construction enables comprehensive quantification of motor states. Compared to traditional single-variable control, this method can simultaneously optimize the magnetic field, torque, and speed, improving overall system performance. The dynamic equations introduce lumped disturbances, enabling explicit disturbance modeling and compensation, improving anti-interference capabilities. Furthermore, the unified equation form facilitates expansion to multi-disturbance scenarios, enhancing system adaptability. Gain normalization combined with a saturation function eliminates chattering while maintaining control accuracy and improving system robustness. The boundary layer thickness is adjustable to adapt to different accuracy requirements. Parameter adaptive rate enables online optimization of control parameters, significantly improving system adaptability. This forms a complete closed loop of "decoupling-modeling-compensation-adaptation," meeting the needs of high-dynamic, high-precision scenarios. This axial magnetic field control method allows for online adjustment of control parameters and adaptive compensation for system uncertainties, improving the dynamic performance and robustness of robot joint modules.

[0016] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating an adaptive axial magnetic field control method for a robot joint module, as shown in the embodiment of the specification.

[0018] Figure 2 A block diagram of an adaptive axial magnetic field control system for a robot joint module is shown in the embodiment of the specification.

[0019] Figure 3 This is a block diagram of an adaptive axial magnetic field control device for another robot joint module, as shown in the embodiment of the specification. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] This disclosure provides an adaptive axial magnetic field control method for a robot joint module. Figure 1 This is a flowchart illustrating an adaptive axial magnetic field control method for a robot joint module according to an embodiment. The method includes: In step S11, the input control voltage of the axial drive motor corresponding to the robot joint module is input into a pre-established dynamic mathematical model to obtain the axial magnetic field component and torque current component output by the dynamic mathematical model. The axial magnetic field component and the torque current component are obtained by decoupling the input control voltage in the rotational direct axis and quadrature axis coordinate system. The dynamic mathematical model is a set of mathematical equations based on the physical characteristics of the motor, which can be used to describe the mapping relationship between input voltage and magnetic field and current. The rotating direct-axis and quadrature-axis coordinate system (i.e., the dq coordinate system) converts AC quantities in the three-phase stationary coordinate system into DC quantities in the rotating coordinate system, achieving decoupled control of the magnetic field and torque. The axial magnetic field component is the component of the motor's magnetic field in the axial direction, directly affecting the flux linkage. The torque current component is the current component directly related to the motor torque and can determine the output torque.

[0022] In this embodiment, the input control voltage is converted into a DC quantity in the dq coordinate system after Clark transformation (from three-phase stationary to two-phase stationary coordinate system) and Park transformation (from two-phase stationary to rotating coordinate system). The dynamic mathematical model establishes a linear relationship between voltage, magnetic field, and current using motor parameters (such as inductance and resistance), decoupling the axial magnetic field component (Id) and the torque current component (Iq). For example, the input voltages Ud and Uq correspond to the control channels for the magnetic field and torque, respectively. The model separates the two through matrix operations (such as Ud = R×Id + L×dId / dt + ω×L×Iq) to avoid cross-coupling. Here, R is the resistance, and L is the inductance of the axial magnetic field component (Id) channel.

[0023] It can be explained that R×Id is used to represent the voltage consumed by the resistor (proportional to the current). Ld×Id / dt is used to represent the resistance of the inductor to the change of current (dynamic response term). ω×L×Iq is used to represent the back electromotive force generated by the rotor rotation (related to the torque current Iq and angular velocity ω).

[0024] After decoupling and simplification in the dq coordinate system, the three-phase AC quantities are converted into DC quantities through coordinate transformation, and the magnetic field (Id) and torque (Iq) channels are decoupled. At this point, in the dynamic equation of the Id channel, L×dId / dt becomes the core term describing the change of the magnetic field component with time.

[0025] In step S12, state variables for the axial drive motor are constructed based on the obtained rotor electric angular velocity, axial magnetic field component, and torque current component of the axial drive motor. In this embodiment of the disclosure, state variables are the smallest set of variables used to describe the dynamic characteristics of the system, typically including current, flux linkage, and velocity. The rotor electrical angular velocity is the electrical angular velocity of the motor rotor's rotation, reflecting the relationship between the actual rotational speed and the number of pole pairs.

[0026] In this disclosure, the state variable vector X = [Id, Iq, ω] can be constructed with the axial magnetic field component (Id), the torque current component (Iq), and the rotor electric angular velocity (ω) as the core. T Among them, Id and Iq are directly related to the magnetic field and torque, while ω reflects the dynamic response speed. For example, by monitoring Id and Iq in real time, it can be determined whether the magnetic field is saturated or the torque is overloaded; ω is used for feedback control to correct speed deviations.

[0027] In step S13, based on the first matrix parameters of the axial drive motor, the state variables, the rotor electric angular velocity, the input control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources, the dynamic equations for the axial magnetic field component and the torque current component of the axial drive motor as a function of time are determined. The first matrix parameter is the inherent parameter matrix of the motor, such as the resistance matrix R and the inductance matrix L. The lumped disturbance is a comprehensive variable that quantifies external disturbances such as friction, load changes, and temperature drift.

[0028] In this disclosure, based on the state variable X and the first matrix parameters (R, L), combined with the input voltage U and the lumped disturbance d, the dynamic equation is derived as: dX / dt = AX + BU + Dd, where A, B, and D are coefficient matrices. For example, the dynamic equation for Id is: dId / dt = (1 / L)(-RId + ωL×Iq + Ud + d_Id), where d_Id is the disturbance of the magnetic field channel. The dynamic equation can be used to describe the changes of the magnetic field and torque components over time and explicitly includes the disturbance term.

[0029] In step S14, the input control voltage is normalized by gain according to the dynamic equation, the second matrix parameters of the axial drive motor, the saturation function, the current tracking error and the boundary layer thickness to obtain the target control voltage. The second matrix parameter is a parameter matrix related to the control algorithm, such as the switching gain in sliding mode control. The saturation function is a nonlinear function used to limit the amplitude of the control quantity to avoid system oscillation. The boundary layer thickness is the width of the smoothing region of the saturation function, balancing control accuracy and chattering.

[0030] In this embodiment, a sliding mode control law can be designed based on the dynamic equation: U = -KX - ηsat(S / φ), where K is the second matrix parameter (feedback gain), η is the switching gain, S is the sliding surface (e.g., S = e + λ×∫e, where e is the error), and φ is the boundary layer thickness. The saturation function sat(S / φ) restricts S within [-φ, φ], eliminating chattering. Gain normalization, by adjusting K and η, keeps U stable under different operating conditions. For example, increasing K at high speeds improves response speed, while decreasing η at low speeds reduces overshoot.

[0031] In step S15, the target control voltage is provided to the axial drive motor as a target, and the parameter adaptive rate is updated according to the real-time current tracking error and the third matrix parameters of the axial drive motor, so as to dynamically adjust the target control voltage by updating the parameter adaptive rate.

[0032] The third matrix parameter is an update matrix related to the parameter adaptation rate, such as the learning rate matrix. The parameter adaptation rate is an algorithm that dynamically adjusts control parameters based on the error, achieving system self-optimization.

[0033] In this embodiment, the current tracking error e = I_ref - I_actual (where I_ref is the target current and I_actual is the actual current) is calculated in real time. Combined with the third matrix parameter Γ, the adaptive rate is updated: Θ_dot = -ΓeX, where Θ is a control parameter (such as K or η). For example, if the Id error remains positive, Θ_dot adjusts K_d (the Id channel gain) to increase Ud and reduce the error. The parameter update is related to the error and state variables, achieving closed-loop self-optimization.

[0034] The above technical solution eliminates the dynamic coupling effect between the magnetic field and torque through decoupling control, significantly improving control accuracy and response speed. Simultaneously, the dynamic mathematical model can adapt to different motor parameters, enhancing system robustness. State variable construction enables comprehensive quantification of motor states. Compared to traditional single-variable control, this method can simultaneously optimize the magnetic field, torque, and speed, improving overall system performance. The dynamic equations introduce lumped disturbances, enabling explicit disturbance modeling and compensation, improving anti-interference capabilities. Furthermore, the unified equation form facilitates expansion to multi-disturbance scenarios, enhancing system adaptability. Gain normalization combined with a saturation function eliminates chattering while ensuring control accuracy and improving system robustness. The boundary layer thickness is adjustable to adapt to different accuracy requirements. Parameter adaptive rate enables online optimization of control parameters, significantly improving system adaptability. Combining the aforementioned steps forms a complete closed loop of "decoupling-modeling-compensation-adaptation," meeting the needs of high-dynamic, high-precision scenarios. This allows for online adjustment of control parameters and adaptive compensation for system uncertainties in axial magnetic field control, improving the dynamic performance and robustness of robot joint modules.

[0035] In a preferred embodiment, the first matrix parameters include an inductance matrix, a resistance matrix, an antisymmetric matrix, and a gain matrix. In step S13, determining the dynamic equations for the axial magnetic field component and the torque current component of the axial drive motor as a function of time, based on the matrix parameters of the axial drive motor, the state variables, the rotor electrical angular velocity, the input control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources, includes: In step S131, the voltage drop parameter of the motor winding of the axial drive motor caused by the resistance is determined according to the resistance matrix and the state variable. The resistance matrix (R-matrix) describes the resistance distribution of each phase winding of the motor. It can be a diagonal matrix (such as R_d, R_q) and reflects the resistance's opposition to current in the dq coordinate system. State variables are physical quantities during motor operation, such as the axial magnetic field component Id and the torque current component Iq, used to characterize the current state of the system.

[0036] In this embodiment, the voltage drop caused by the resistance is derived from Ohm's law (U=IR). In the dq coordinate system, the product of the resistance matrix R and the state variables (Id, Iq) directly generates the voltage drop component: UR_d = R_d × Id, UR_q = R_q × Iq. R_d and R_q may differ due to factors such as winding temperature and skin effect. By monitoring Id and Iq in real time and combining this with a pre-calibrated resistance matrix, the resistance voltage drop can be accurately calculated.

[0037] In step S132, the kinetic electromotive force in the rotating coordinate system is determined based on the rotor electric angular velocity, the inductance matrix, the antisymmetric matrix, and the state variables. Among them, the back-EMF is the induced electromotive force generated when the rotor rotates and cuts the magnetic field. Its magnitude is related to the rotor's electric angular velocity (ω) and current. The inductance matrix (L-matrix) can describe the inductance distribution in the dq coordinate system (such as L_d, L_q) and is used to reflect the energy storage effect of current change on the magnetic field. The antisymmetric matrix (Skew-SymmetricMatrix) is used to simplify cross-coupling terms (such as the interaction between ωL_qI_q and ωL_dI_d).

[0038] In this embodiment of the disclosure, the kinetic electromotive force consists of two parts: Rotational component of magnetic field: When the rotor rotates, the d-axis magnetic field generates an induced electromotive force (ωL_dI_d) on the q-axis, and the q-axis magnetic field generates a reverse electromotive force (-ωL_qI_q) on the d-axis.

[0039] Antisymmetric matrix simplification: using antisymmetric matrices Multiplying by the state variables (Id, Iq) gives a unified representation of the cross-coupling effect: ; We obtain: UEMFd=−ωLqIq,UEMFq=ωLdId.

[0040] In step S133, the actual applied control voltage is determined based on the gain matrix and the input control voltage. Among them, the gain matrix (K-matrix) is the proportional coefficient matrix in the control algorithm, which is used to map the input control voltage (such as PID output) to the actual motor terminal voltage; the input control voltage (U_in) is the original voltage signal output by the controller, which needs to be adjusted by gain to match the actual needs of the motor.

[0041] In this embodiment, the actual control voltage needs to consider the matching between the controller output range and the motor voltage limit. The gain matrix K is determined through calibration, for example: U ctrl = K × Uin Here, K may include coefficients such as proportional (K_p) and integral (K_i) to compensate for the voltage drop across the resistor and the electromotive force. For example, if U_in = 10V and K = 1.2, the actual voltage is 12V to overcome the line voltage drop and the back electromotive force of the inductor.

[0042] In step S134, based on the voltage drop parameter, the kinematic electromotive force, the applied control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources, the dynamic equations for the axial magnetic field component and the torque current component of the axial drive motor as a function of time are determined.

[0043] In a preferred embodiment, the dynamic equation is constructed using the following formula: L×di / dt=-R×i-ω×J×L×i+K×u-τ_d; Where i is the state variable, i=[i_d,i_q,ω]T, L is the inductance matrix, R is the resistance matrix, J is the antisymmetric matrix, K is the gain matrix, u is the input control voltage, τ_d is the lumped disturbance, T represents matrix transpose, i_d is the axial magnetic field component, i_q is the torque current component, ω is the rotor electric angular velocity, t is time, and d represents the derivative.

[0044] The above technical solution constructs a high-precision dynamic equation by analyzing the resistance voltage drop, kinetic electromotive force, control voltage, and disturbance in steps, thereby improving dynamic response capabilities. It can accurately compensate for resistance and inductance effects, reducing Id / Iq tracking errors and improving the accuracy of magnetic field and torque control. Lumped disturbance estimation and compensation make the system insensitive to disturbances such as temperature and magnetic saturation. Furthermore, the combination of dynamic equations and adaptive control reduces voltage margin, lowers energy consumption, and extends motor life. It is applicable to high-dynamic, high-precision scenarios such as robot joints and electric vehicle drives.

[0045] In a preferred embodiment, the second matrix parameters include an inductance matrix, a gain matrix, a first positive definite diagonal gain matrix, and a second positive definite diagonal gain matrix. In step S14, the step of normalizing the gain of the input control voltage based on the dynamic equation, the matrix parameters, the saturation function, the current tracking error, and the boundary layer thickness to obtain the target control voltage includes: In step S141, the voltage correction parameters are determined based on the first positive definite diagonal gain matrix and the current tracking error; The first positive definite diagonal gain matrix is ​​a diagonal matrix whose diagonal elements are all positive real numbers. It is used to amplify the weight of the current tracking error, ensuring the speed and stability of error correction. The current tracking error (eid, eiq) is the deviation between the actual current (Id, Iq) and the reference value (Id_ref, Iq_ref), reflecting the control accuracy. The voltage correction parameter (ΔU_corr) is a compensation voltage generated by weighting the error, used to offset the current deviation.

[0046] In this embodiment of the disclosure, the voltage correction parameter is calculated using the following formula: Where K1 = diag(k11, k12), k11 and k12 are adjustable gain coefficients, calibrated according to the dynamic response requirements of the motor. For example, if k11 = 5, e id =0.2A, then the d-axis correction voltage is 1V. This parameter directly affects the control voltage, quickly reducing current tracking error and improving system rigidity.

[0047] In step S142, the residual uncertainty parameter is determined based on the second positive definite diagonal gain matrix and the saturation function, wherein the saturation function is constructed based on the current tracking error and the boundary layer thickness; Among them, the second positive definite diagonal gain matrix (K2) has a similar structure to K1 and is used to quantify the effects of unmodeled dynamics or parameter uncertainties (such as magnetic saturation and temperature drift) of the system; the saturation function (Sat(·)) is a nonlinear function that limits the amplitude of the error signal by the boundary layer thickness to avoid oscillations caused by high gain; the residual uncertainty parameter is the uncertainty compensation term after the saturation function is processed, reflecting the deviation between the actual system and the model.

[0048] The saturation function is defined as follows: ; Furthermore, the residual uncertainty parameter is determined by the following formula: ; Here, φ is set according to the system noise level (e.g., φ=0.1A). When the error exceeds φ, Sat(·) limits the compensation intensity to prevent overcompensation.

[0049] In step S143, the linear feedback parameters are determined based on the inductance matrix, the voltage correction parameters, and the residual uncertainty parameters. Among them, the inductance matrix (L) describes the inductance distribution (L_d, L_q) in the dq coordinate system, reflecting the energy storage effect of current change on the magnetic field; the linear feedback parameter (U_fb) is a feedback control quantity generated by combining the voltage correction parameter and the residual uncertainty parameter, which is used to directly adjust the motor terminal voltage and suppress current fluctuations.

[0050] The linear feedback parameters are calculated using the following formula: ; The linear feedback parameters can convert the voltage compensation into the rate of change of current (dId / dt, dIq / dt), enabling rapid current tracking through the inductor matrix. For example, if ΔUcorrd = 2V, ΔUuncd = 1V, and Ld = 0.05H, then dId / dt = (2+1) / 0.05 = 60A / s, significantly improving the dynamic response.

[0051] In step S144, the feedforward model parameters are determined according to the dynamic equation, and the input control voltage is normalized according to the feedforward model parameters, the linear feedback parameters, and the gain matrix to obtain the target control voltage.

[0052] Among them, the feedforward model parameters are the control voltages predicted based on the dynamic equations, used to compensate for known disturbances (such as motion electromotive force); the gain matrix maps the linear feedback parameters and feedforward parameters to the actual voltage output; the target control voltage (U_target) is the final control signal after the feedforward and feedback are combined, which directly drives the motor.

[0053] In a preferred embodiment, the target control voltage Utarget is determined by the following formula: Utarget=K -1 ×[Z+τ_d-L×(λ×e+η×sat(e / φ))]; Where e is the current tracking error, λ is the first positive definite diagonal gain matrix, η is the second positive definite diagonal gain matrix, φ is the boundary layer thickness, sat() is the saturation function, Z is the feedforward model parameter, and K is the gain matrix. Z = R×i - ω×J×L×i + K×u.

[0054] The above technical solutions can improve dynamic accuracy, reduce current tracking error, and enhance the accuracy of magnetic field and torque control, meeting the requirements of high-precision applications such as robot joints and electric vehicles. Saturation function and residual uncertainty compensation make the system less sensitive to disturbances such as magnetic saturation and temperature drift, improving stability. Simultaneously, feedforward-feedback integrated control reduces voltage redundancy, lowers energy consumption, and extends motor life.

[0055] In a preferred embodiment, the third matrix parameters include a positive definite adaptive gain matrix and a regression matrix. In step S15, updating the parameter adaptive rate based on the real-time acquired current tracking error and the third matrix parameters of the axial drive motor includes: In step S151, the correction parameter for the parameter adaptive rate is determined based on the product of the transpose of the regression matrix and the real-time current tracking error. The regression matrix describes the dynamic relationship between the system's input and output. Its elements consist of physical parameters such as inductance and resistance, reflecting the mathematical correlation between the current tracking error and the parameters to be estimated. The transpose of the regression matrix is ​​used to map the error signal to the parameter space, realizing the conversion from error to parameter correction. The correction parameter is the product of the current tracking error (eid, eiq) and the regression matrix, used to quantify the impact of parameter deviation on control performance, and is the core correction quantity of adaptive control.

[0056] In step S152, the parameter vector to be estimated is determined according to the positive definite adaptive gain matrix and the correction parameter. The positive definite adaptive gain matrix is ​​used as a preset learning strategy to adjust and filter the correction parameter. Among them, the positive definite adaptive gain matrix Γ is a symmetric positive definite matrix whose elements determine the speed and stability of parameter updates, similar to the learning rate but with the ability to coordinate multiple parameters; the parameter vector to be estimated is the set of currently estimated parameters (such as L_d, R_q, ψ_f, etc.). The correction parameter Δθ is weighted and filtered by Γ to avoid oscillation caused by excessively fast parameter updates or affecting convergence by excessively slow updates.

[0057] In step S153, the parameter adaptive rate is updated by integrating the parameter vector to be estimated onto the current parameter adaptive rate.

[0058] In a preferred embodiment, the parameter adaptation rate d(θ_hat) / dt is updated using the following formula: d(θ_hat) / dt = -Γ×βT×e; Where θ_hat is the parameter vector to be estimated, Γ is the positive definite adaptive gain matrix, and β is the regression matrix.

[0059] In a preferred embodiment, step S15, providing the target control voltage to the axial drive motor as a target, includes: Based on the desired motion trajectory of the robot joints, the desired electromagnetic torque is obtained through inverse dynamics calculation; Among them, the desired motion trajectory is the displacement, velocity and acceleration curve of the robot joint in Cartesian space or joint space, which is used to guide the joint movement; inverse dynamics is a mathematical method that uses known motion trajectories to infer the required joint torque, the core of which is to establish the mapping relationship between kinematic and dynamic equations; the desired electromagnetic torque is the torque that the motor needs to output to drive the joint to achieve the desired motion, and its magnitude is determined by the load inertia, friction and motion acceleration.

[0060] The desired electromagnetic torque is calculated using the following formula: ; Where J is the joint moment of inertia. Let B be the desired angular acceleration and B be the coefficient of friction. For the desired angular velocity, T load This represents the load torque.

[0061] Based on the maximum torque-current ratio or field weakening control strategy, the desired axial magnetic field and desired torque current are calculated according to the desired electromagnetic torque. Among them, the maximum torque-to-current ratio (MTPA) strategy is a control method that optimizes the distribution of direct-axis current (Id) and quadrature-axis current (Iq) to generate maximum torque per unit current under unsaturated motor core conditions; the field-weakening strategy weakens the magnetic field by applying a negative direct-axis current, thereby extending the high-speed operating range of the motor; and the desired axial magnetic field (Bd) strategy... desired The desired torque current (Iq) is the product of the motor flux linkage and the direct-axis current, reflecting the magnetic field strength; desired The current component that generates torque is the main current component, and its magnitude is proportional to the torque demand.

[0062] Then Id and Iq are solved by the following equation: ; Where p is the pole logarithm, ψ f For permanent magnet flux linkage, L d L q For example, if ψ f =0.1Wb, L d =0.05H, L q =0.1H, T e_desired =0.51 N·m, p=4, then I d ≈-0.5A, I q ≈1.2A. During magnetic weakening control, I_d is negative to weaken the magnetic field.

[0063] The desired axial magnetic field and the desired torque current are input into the reference model to obtain the axial reference magnetic field and torque reference current output by the reference model. The reference model is a second-order linear system: G_ref(s)=ω_n2 / (s2 + 2×ζ×ω_n×s+ω_n2), where ω_n is the natural frequency, ζ is the damping ratio, and s is the Laplace operator. The reference model is a pre-established dynamic model of the motor, used to simulate the motor's behavior under ideal conditions. Its inputs are the desired axial magnetic field and torque current, and its output is the axial reference magnetic field B. d_ref and torque reference current Iq_ref .

[0064] The axial magnetic field error in the current tracking error is determined based on the axial reference magnetic field and the axial magnetic field component, and the torque current error in the current tracking error is determined based on the torque reference current and the torque current component. The current tracking error is the deviation between the actual current and the reference current, and is divided into axial magnetic field error (e_B) and torque current error (e_I). The axial magnetic field component (I_d_actual) is the actual direct-axis current, reflecting the magnetic field adjustment capability; the torque current component (I_q_actual) is the actual quadrature-axis current, which directly generates torque. Therefore, the axial magnetic field error is determined by subtracting the axial reference magnetic field from the axial magnetic field component, and the torque current error is determined by subtracting the torque reference current from the torque current component.

[0065] With the goal of minimizing the axial magnetic field error and the torque current error, the direct-axis voltage and quadrature-axis voltage in the target control voltage are adjusted.

[0066] The target control voltage includes direct-axis voltage and quadrature-axis voltage, which are used to regulate the motor's magnetic field and torque. The purpose of voltage adjustment is to achieve a precise match between the motor output and the desired value by minimizing the axial magnetic field error and torque current error. The adjustment algorithm is usually based on a proportional-integral (PI) controller or model predictive control (MPC) to dynamically calculate the voltage increment based on the error.

[0067] The above technical solution improves control stability by dynamically comparing the reference model with the actual value, suppressing the effects of parameter changes (such as inductance drift caused by temperature) and external disturbances (such as sudden load changes).

[0068] This disclosure also provides an adaptive axial magnetic field control system for a robot joint module. See [link to relevant documentation]. Figure 2 As shown, the system includes: The input module 210 is configured to input the input control voltage of the axial drive motor corresponding to the robot joint module into a pre-established dynamic mathematical model to obtain the axial magnetic field component and torque current component output by the dynamic mathematical model. The axial magnetic field component and the torque current component are obtained by decoupling the input control voltage in the rotational direct axis and quadrature axis coordinate system. The construction module 220 is configured to construct state variables for the axial drive motor based on the obtained rotor electric angular velocity, axial magnetic field component, and torque current component of the axial drive motor. The determination module 230 is configured to determine the dynamic equations of the axial magnetic field component and the torque current component of the axial drive motor as a function of time, based on the first matrix parameters of the axial drive motor, the state variables, the rotor electric angular velocity, the input control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources. Gain normalization module 240 is configured to normalize the input control voltage based on the dynamic equation, the second matrix parameters of the axial drive motor, the saturation function, the current tracking error and the boundary layer thickness to obtain the target control voltage. The update module 250 is configured to provide the target control voltage to the axial drive motor as a target, and update the parameter adaptive rate based on the real-time acquired current tracking error and the third matrix parameters of the axial drive motor, so as to dynamically adjust the target control voltage by updating the parameter adaptive rate.

[0069] In a preferred embodiment, the first matrix parameters include an inductance matrix, a resistance matrix, an antisymmetric matrix, and a gain matrix, and the determining module 230 is configured to: Based on the resistance matrix and the state variables, determine the voltage drop parameters of the motor windings of the axial drive motor caused by the resistance. Based on the rotor electric angular velocity, the inductance matrix, the antisymmetric matrix, and the state variables, determine the kinetic electromotive force in the rotating coordinate system; The actual applied control voltage is determined based on the gain matrix and the input control voltage. Based on the voltage drop parameter, the kinetic electromotive force, the applied control voltage, and the lumped disturbance of the axial drive motor under multiple different disturbance sources, the dynamic equations for the axial magnetic field component and the torque current component of the axial drive motor as a function of time are determined.

[0070] In a preferred embodiment, the dynamic equation is constructed using the following formula: L×di / dt=-R×i-ω×J×L×i+K×u-τ_d; Where i is the state variable, i=[i_d,i_q,ω]T, L is the inductance matrix, R is the resistance matrix, J is the antisymmetric matrix, K is the gain matrix, u is the input control voltage, τ_d is the lumped disturbance, T represents matrix transpose, i_d is the axial magnetic field component, i_q is the torque current component, ω is the rotor electric angular velocity, t is time, and d represents the derivative.

[0071] In a preferred embodiment, the second matrix parameters include an inductance matrix, a gain matrix, a first positive definite diagonal gain matrix, and a second positive definite diagonal gain matrix. The gain normalization module 240 is configured to: The voltage correction parameters are determined based on the first positive definite diagonal gain matrix and the current tracking error; The residual uncertainty parameter is determined based on the second positive definite diagonal gain matrix and the saturation function, wherein the saturation function is constructed based on the current tracking error and the boundary layer thickness; The linear feedback parameters are determined based on the inductance matrix, the voltage correction parameters, and the residual uncertainty parameters. The feedforward model parameters are determined based on the dynamic equation, and the input control voltage is normalized by the gain of the feedforward model parameters, the linear feedback parameters, and the gain matrix to obtain the target control voltage.

[0072] In a preferred embodiment, the target control voltage Utarget is determined by the following formula: Utarget=K -1 ×[Z+τ_d-L×(λ×e+η×sat(e / φ))]; Where e is the current tracking error, λ is the first positive definite diagonal gain matrix, η is the second positive definite diagonal gain matrix, φ is the boundary layer thickness, sat() is the saturation function, Z is the feedforward model parameter, and K is the gain matrix.

[0073] In a preferred embodiment, the third matrix parameters include a positive definite adaptive gain matrix and a regression matrix, and the update module 250 is configured as follows: Based on the product of the transpose of the regression matrix and the real-time current tracking error, the correction parameter for the parameter adaptive rate is determined. Based on the positive definite adaptive gain matrix and the correction parameter, the parameter vector to be estimated is determined, and the positive definite adaptive gain matrix is ​​used as a preset learning strategy to adjust and filter the correction parameter. The parameter adaptive rate is updated by integrating the parameter vector to be estimated onto the current parameter adaptive rate.

[0074] In a preferred embodiment, the parameter adaptation rate d(θ_hat) / dt is updated using the following formula: d(θ_hat) / dt = -Γ×βT×e; Where θ_hat is the parameter vector to be estimated, Γ is the positive definite adaptive gain matrix, and β is the regression matrix.

[0075] In a preferred embodiment, the update module 250 is configured to: Based on the desired motion trajectory of the robot joints, the desired electromagnetic torque is obtained through inverse dynamics calculation; Based on the maximum torque-current ratio or field weakening control strategy, the desired axial magnetic field and desired torque current are calculated according to the desired electromagnetic torque. The desired axial magnetic field and the desired torque current are input into the reference model to obtain the axial reference magnetic field and torque reference current output by the reference model. The reference model is a second-order linear system: G_ref(s)=ω_n2 / (s2 + 2×ζ×ω_n×s+ω_n2), where ω_n is the natural frequency, ζ is the damping ratio, and s is the Laplace operator. The axial magnetic field error in the current tracking error is determined based on the axial reference magnetic field and the axial magnetic field component, and the torque current error in the current tracking error is determined based on the torque reference current and the torque current component. With the goal of minimizing the axial magnetic field error and the torque current error, the direct-axis voltage and quadrature-axis voltage in the target control voltage are adjusted.

[0076] Specific limitations regarding the adaptive axial magnetic field control system for the robot joint module can be found in the limitations of the adaptive axial magnetic field control method for the robot joint module described above, and will not be repeated here. Each module in the aforementioned adaptive axial magnetic field control system for the robot joint module can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0077] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in the foregoing embodiments.

[0078] This disclosure also provides an electronic device, including: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of any of the methods described in the foregoing embodiments.

[0079] Figure 3The adaptive axial magnetic field control device 100 for the robot joint module shown includes a processor 1001 and a memory 1003. The processor 1001 and the memory 1003 are connected, for example, via a bus 1002. Optionally, the adaptive axial magnetic field control device 100 for the robot joint module may further include a communication component, which can be used for data interaction between the device 100 and other devices, such as sending or receiving data. It should be noted that in actual scheduling, the communication component is not limited to one, and the structure of the adaptive axial magnetic field control device 100 for the robot joint module does not constitute a limitation on the embodiments of this application.

[0080] Processor 1001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 1001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0081] Bus 1002 may include a pathway for transmitting information between the aforementioned components. Bus 1002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 1002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0082] The memory 1003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing program code and capable of being read by a computer, without limitation herein.

[0083] The memory 1003 is used to store program code for executing embodiments of the present disclosure, and its execution is controlled by the processor 1001. The processor 1001 is used to execute the program code stored in the memory 1003 to implement the steps shown in the aforementioned embodiments of the adaptive axial magnetic field control method for robot joint modules.

[0084] This disclosure also provides a computer-readable storage medium storing program code. When the program code is executed by a processor, it can implement the steps and corresponding content of the aforementioned embodiments of the adaptive axial magnetic field control method for robot joint modules.

[0085] The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present disclosure, various changes, modifications, substitutions and variations can be made to these embodiments, and all such changes, modifications, substitutions and variations fall within the protection scope of the present disclosure.

[0086] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction, and such combinations should also be considered as part of this disclosure. To avoid unnecessary repetition, this disclosure will not further describe the various possible combinations. The technical scope of this application is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. An adaptive axial magnetic field control method for a robot joint module, characterized in that, The method comprises: inputting an input control voltage of an axial driving motor corresponding to the robot joint module into a pre-established dynamic mathematical model to obtain an axial magnetic field component and a torque current component output by the dynamic mathematical model, the axial magnetic field component and the torque current component being obtained by decoupling the input control voltage in a rotating direct-axis and quadrature-axis coordinate system; constructing a state variable for the axial driving motor according to the rotor electric angular velocity of the axial driving motor, the axial magnetic field component and the torque current component; determining a dynamic equation of the axial magnetic field component and the torque current component of the axial driving motor changing over time according to a first matrix parameter of the axial driving motor, the state variable, the rotor electric angular velocity, the input control voltage and lumped disturbance of the axial driving motor under a plurality of different disturbance sources; performing gain normalization on the input control voltage according to the dynamic equation, a second matrix parameter of the axial driving motor, a saturation function, a current tracking error and a boundary layer thickness to obtain a target control voltage; providing the target control voltage as a target to the axial driving motor, and updating a parameter adaptive rate according to a real-time acquired current tracking error and a third matrix parameter of the axial driving motor to dynamically adjust the target control voltage by updating the parameter adaptive rate.

2. The method of claim 1, wherein, The first matrix parameter comprises an inductance matrix, a resistance matrix, an anti-symmetry matrix and a gain matrix, and the determination of the dynamic equation of the axial magnetic field component and the torque current component of the axial driving motor changing over time according to the matrix parameter of the axial driving motor, the state variable, the rotor electric angular velocity, the input control voltage and the lumped disturbance of the axial driving motor under a plurality of different disturbance sources comprises: determining a voltage drop parameter of a motor winding of the axial driving motor caused by resistance according to the resistance matrix and the state variable; determining a motion electromotive force in a rotating coordinate system according to the rotor electric angular velocity, the inductance matrix, the anti-symmetry matrix and the state variable; determining an actually applied control voltage according to the gain matrix and the input control voltage; determining the dynamic equation of the axial magnetic field component and the torque current component of the axial driving motor changing over time according to the voltage drop parameter, the motion electromotive force, the applied control voltage and the lumped disturbance of the axial driving motor under a plurality of different disturbance sources.

3. The method of claim 2, wherein, The dynamic equation is constructed by the following formula: L×di / dt=-R×i-ω×J×L×i+K×u-τ_d; where i is the state variable, i = [i_d, i_q, ω] T , L is the inductance matrix, R is the resistance matrix, J is the skew-symmetric matrix, K is the gain matrix, u is the input control voltage, τ_d is the lumped disturbance, T denotes matrix transpose, i_d is the axial magnetic field component, i_q is the torque current component, ω is the rotor electrical angular velocity, t is time, and d denotes differentiation.

4. The method of claim 1, wherein, The second matrix parameter comprises an inductance matrix, a gain matrix, a first positive definite diagonal gain matrix and a second positive definite diagonal gain matrix, and the gain normalization on the input control voltage to obtain the target control voltage according to the dynamic equation, the matrix parameter, a saturation function, a current tracking error and a boundary layer thickness comprises: determining a voltage correction parameter according to the first positive definite diagonal gain matrix and the current tracking error; determine a residual uncertainty parameter according to the second positive definite diagonal gain matrix and the saturation function, wherein the saturation function is constructed according to the current tracking error and the boundary layer thickness; determine a linear feedback parameter value according to the inductance matrix, the voltage correction parameter and the residual uncertainty parameter; determine a feedforward model parameter value according to the dynamic equation, and perform gain normalization on the input control voltage according to the feedforward model parameter value, the linear feedback parameter value and the gain matrix to obtain the target control voltage.

5. The method of claim 4, wherein, The target control voltage Utarget is determined by the following formula: Utarget = K -1 x [z + t_d - l x (l x e + h x sat (e / f))]; wherein e is the current tracking error, λ is the first positive definite diagonal gain matrix, η is the second positive definite diagonal gain matrix, φ is the boundary layer thickness, sat() is the saturation function, Z is the feedforward model parameter value, and K is the gain matrix.

6. The method according to any one of claims 1-5, characterized in that, The third matrix parameter includes a positive definite adaptive gain matrix and a regression matrix, and updating the parameter adaptive rate according to the third matrix parameter of the axial drive motor and the real-time acquired current tracking error includes: determining a correction parameter for the parameter adaptive rate according to the product of the transpose matrix of the regression matrix and the real-time acquired current tracking error; determining an estimated parameter vector according to the positive definite adaptive gain matrix and the correction parameter, wherein the positive definite adaptive gain matrix is used as a preset learning strategy to adjust the speed and filter the correction parameter; updating the parameter adaptive rate by integrating the estimated parameter vector into the current parameter adaptive rate.

7. The method of claim 6, wherein, The parameter adaptive rate d(θ_hat) / dt is updated by the following formula: d(0_hat) / dt = -Γ x β T x e; wherein θ_hat is the estimated parameter vector, Γ is the positive definite adaptive gain matrix, and β is the regression matrix.

8. The method according to any one of claims 1-5, characterized in that, The target control voltage is provided as a target to the axial drive motor, including: calculating a desired electromagnetic torque through inverse dynamics according to a desired motion trajectory of a robot joint; calculating a desired axial magnetic field and a desired torque current according to the desired electromagnetic torque based on a maximum torque current ratio or a field weakening control strategy; inputting the expected axial magnetic field and the expected torque current into a reference model to obtain an axial reference magnetic field and a torque reference current output by the reference model, wherein the reference model is a second-order linear system: G_ref(s)=ω_n 2 / (s 2 +2×ζ×ω_n×s+ω_n 2 ), wherein ω_n is a natural frequency, ζ is a damping ratio, and s is a Laplace operator; determining an axial magnetic field error in the current tracking error according to the axial reference magnetic field and the axial magnetic field component, and determining a torque current error in the current tracking error according to the torque reference current and the torque current component; adjusting the direct-axis voltage and the quadrature-axis voltage in the target control voltage to minimize the axial magnetic field error and the torque current error.

9. An adaptive axial magnetic field control system for a robot joint module, comprising: The system includes: an input module configured to input an input control voltage of an axial drive motor corresponding to the robot joint module into a pre-established dynamic mathematical model to obtain an axial magnetic field component and a torque current component output by the dynamic mathematical model, wherein the axial magnetic field component and the torque current component are obtained by decoupling the input control voltage in a rotating direct-axis and quadrature-axis coordinate system; a construction module configured to construct a state variable for the axial drive motor according to the rotor electrical angular velocity of the axial drive motor, the axial magnetic field component and the torque current component obtained; a determination module configured to determine a dynamic equation of the axial magnetic field component and the torque current component of the axial drive motor changing over time according to the first matrix parameter of the axial drive motor, the state variable, the rotor electrical angular velocity, the input control voltage and the lumped disturbance of the axial drive motor under a plurality of different disturbance sources; a gain normalization module configured to perform gain normalization on the input control voltage to obtain a target control voltage according to the dynamic equation, the second matrix parameter of the axial drive motor, a saturation function, a current tracking error and a boundary layer thickness; an update module configured to provide the target control voltage as a target to the axial drive motor, and update a parameter adaptive rate according to the current tracking error obtained in real time and the third matrix parameter of the axial drive motor, so as to dynamically adjust the target control voltage by updating the parameter adaptive rate.

10. An electronic device, comprising: comprise: a memory having a computer program stored thereon; a processor configured to execute the computer program in the memory to implement the steps of the method of any one of claims 1-8.

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