Exoskeleton Manipulator Control Method

Abstract

The present invention discloses a control method for an exoskeleton manipulator, including: constructing a hand biomechanical model, defining the degrees of freedom of finger joints, and performing joint-by-joint modeling based on the driving dynamics equation of the Bowden cable; deploying high-density sensors on the hand; S3: improving the hand-specific Bouc-Wen model, introducing an interphalangeal coupling term to correct the hysteresis state equation, and identifying the micro-hysteresis parameters through a hierarchical identification algorithm; S4: designing an adaptive cooperative control strategy to achieve a fuzzy mapping from electromyogram signals to joint torques, and optimizing the cooperative tracking error through a multi-joint parameter coupling adaptive law; S5: using a modified dual Kalman filter to dynamically compensate for the mechanical resistance of the hand; S6: optimizing the closed-loop execution and safety strategy. The control method for the exoskeleton manipulator of the present invention can effectively process the mapping relationship between electromyogram signals and the actions of the manipulator and adapt to different operating environments through the improved Bouc-Wen model and the adaptive cooperative control strategy.

Description

Technical Field

[0001] The present invention belongs to the technical field of manipulator control, and particularly relates to a control method for an exoskeleton manipulator. Background Art

[0002] An exoskeleton manipulator is a wearable device that can enhance the functions of the human hand and is widely used in rehabilitation medicine, industrial operations, and the military field. The existing control methods for exoskeleton manipulators have the following problems: the sensor configuration is not perfect enough to obtain the contact force and joint angle information of the hand in real time; the hysteresis model fails to fully consider the multi-joint coupling effect, resulting in large control errors; and there is a lack of an adaptive cooperative control strategy, which cannot effectively handle the mapping relationship between electromyographic signals and the actions of the manipulator. Summary of the Invention

[0003] The present invention provides a control method for an exoskeleton manipulator to solve the above-mentioned technical problems, and specifically adopts the following technical solutions:

[0004] A control method for an exoskeleton manipulator includes the following steps:

[0005] S1: Construct a hand biomechanical model, define the degrees of freedom of finger joints, and perform sub-joint modeling based on the driving dynamics equation of the Bowden cable;

[0006] S2: Deploy high-density sensors on the hand, including a fingertip tactile array, a joint micro encoder, and a surface electromyographic sensor, where the fingertip tactile array is used to measure the contact force distribution, the joint micro encoder is used to measure the joint angle, and the surface electromyographic sensor is used to collect the electromyographic signals of the forearm flexor / extensor muscle groups;

[0007] S3: Improve the hand-specific Bouc-Wen model, introduce an inter-finger coupling term to correct the hysteresis state equation, and identify the micro-hysteresis parameters through a hierarchical identification algorithm;

[0008] S4: Design an adaptive cooperative control strategy to achieve a fuzzy mapping from electromyographic signals to joint torques, and optimize the cooperative tracking error through a multi-joint parameter coupling adaptive law;

[0009] S5: Use a modified dual Kalman filter to dynamically compensate for the mechanical resistance of the hand, including a state filter and a parameter filter, which are respectively used to estimate the joint friction force in real time and update the friction coefficient;

[0010] S6: Optimize the closed-loop execution and safety strategy, including variable impedance control, finger cooperative trajectory optimization, and a safety boundary protection mechanism.

[0011] Further, in step S1, the degrees of freedom of the finger joints are 14, and the dynamic equation of each joint is:

[0012]

[0013] Among them, i = 1, 2, …, 14 represents the joint number, and τ i is the driving torque of the i-th joint, J i is the inertia term, θ i is the joint angle, B i is the damping term, K i is the elastic term, H i (z i ) is the hysteresis term, τ ext,i is the external torque term, F fric,i is the friction term of the i-th joint, which includes the coupling effect of fingertip contact friction and joint bearing friction.

[0014] Furthermore, in step S2, the fingertip tactile array is a 4*4 piezoresistive sensor, and the calculation formula for the contact force distribution is:

[0015]

[0016] Among them, w j is the normalized weight, P j is the measured value of the j-th sensor, w j ∈[0, 1], and ∑w j = 1.

[0017] Furthermore, in step S3, the improved Bouc-Wen hysteresis state equation is:

[0018]

[0019] Among them, z i represents the hysteresis state variable of the i-th joint, C ij is the coupling coefficient between fingers, which is calibrated through a grasping experiment;

[0020] The micro-hysteresis parameters are identified through a hierarchical identification algorithm. The outer layer uses the PSO optimization algorithm to optimize α i , β i , γ i and n, where α i is the hysteresis stiffness coefficient of the i-th joint, controlling the linear part of the hysteresis, β i is the non-linear hysteresis damping parameter of the i-th joint, affecting the saturation rate, γ i is the non-linear hysteresis shape parameter of the i-th joint, n ∈ [1, 3], which is the order of hysteresis non-linearity;

[0021] The inner layer uses the least squares method to update C ij and F fric,i in real time.

[0022] Furthermore, in step S4, the fuzzy mapping formula from the electromyography signal to the joint torque is as follows:

[0023] where a m is the electromyography activation degree of the m-th channel, a th is the individualized activation threshold, K sEMG = 0.5 N*m / V, representing the electromyography signal-torque conversion gain coefficient, which is determined through a calibration experiment, and θ max,i represents the maximum allowable angle of the i-th joint, to avoid the mapping value mutation caused when the joint angle approaches the limit value.

[0024] Furthermore, in step S4, the definition of the cooperative tracking error is as follows:

[0025]

[0026] where λ = 0.3, w i is the weight coefficient, w i ∝1 / J i , N = 14, θ i is the angle of the i-th joint, θ j is the angle of the j-th joint;

[0027] The improved adaptive algorithm is as follows:

[0028]

[0029] where represents the joint equivalent inertia adjusted in real time, where ^ represents the derivative, γJ is the adaptive gain, used to control the update rate of the inertia parameter, e i is the tracking error of the i-th joint, J nom is the nominal inertia value, initially set according to the biomechanical model, and J max is the maximum inertia value, to prevent parameter overshoot.

[0030] Furthermore, in step S5, the prediction and update equations of the state filter are as follows:

[0031]

[0032] where A i = diag[0.92, 0.85], A i is the state transition matrix, describing the dynamic decay characteristics of the friction state, calibrated through a step response experiment, represents the first derivative of the angle of the i-th joint, represents the first derivative of the angle of the i-th joint at the previous moment, is the friction state vector of the i-th joint, represents the friction state vector of the i-th joint at the previous moment, K f,i represents the Kalman gain matrix of the i-th joint state filter, F tip,i represents the contact force distribution of the i-th fingertip tactile array, , H i is the observation matrix, which maps the friction state to the sensor measurement space, where, = 0.01 rad / s, is the velocity normalization factor, B fric,i is the input coupling matrix, which is the excitation weight of the angular velocity on the friction state.

[0033] Furthermore, in step S5, the update equation of the parameter filter is:

[0034]

[0035] where, σ i is the friction coefficient, is the friction coefficient at the current moment, represents the friction coefficient at the previous moment, K σ,i is the parameter update gain, which controls the parameter convergence rate, is the model sensitivity matrix;

[0036] The compensation torque formula for the fingertip contact force feedforward compensation is:

[0037]

[0038] where, τ comp,i is the compensation torque of the i-th joint, x j is the position coordinate of the fingertip contact point, F tip,j represents the contact force of the j-th sensor in the 4*4 fingertip tactile array, represents the derivative of the i-th joint angle with respect to the j-th sensor in each fingertip tactile array, K tip represents the mapping coefficient, which is used to gain the contact force mapping.

[0039] Furthermore, step S6 is specifically:

[0040] Adjust the impedance parameters according to the stiffness of the grasped object to achieve variable impedance control, and automatically switch to the admittance control mode when a sudden external force is detected;

[0041] Online correct the finger cooperative trajectory based on the contact force distribution to ensure the stability of multi-finger enveloping grasping;

[0042] Set up a safety boundary protection mechanism to ensure the safe operation of the exoskeleton manipulator through the joint angle-torque envelope constraint.

[0043] Furthermore, in step S6, the impedance parameter adjustment formula for variable impedance control is as follows:

[0044]

[0045] where K p is the stiffness parameter, K p0 is the set basic stiffness parameter, is the stiffness change coefficient, B d is the damping parameter, B d0 is the set basic damping parameter, represents the second-order vector norm of F tip ;

[0046] The online correction formula for the finger coordination trajectory is as follows:

[0047]

[0048] where is the real-time reference angle after correction of the i-th joint, θ d,i is the original desired angle of the i-th joint, which comes from the intention trajectory generated by the EMG signal, neighbors is the set of coordinated joints, including the joints of all participating fingers in the envelope grasp, θ d,j is the desired angle of the neighboring joint j associated with joint i, H(z i ) is used to convert the lagged contact state z i into equivalent potential energy, K c is the coordination gain, K h is the lag term gain;

[0049] The joint angle-torque envelope constraint formula for the safety boundary protection mechanism is as follows:

[0050]

[0051] where τ cmd,i is the control torque of the i-th joint in the final output, τ comp,i is the compensation torque of the i-th joint, τ i is the actual torque of the current output, τ max is the maximum output torque limit, τ soft is the buffer target torque when the joint approaches the safety limit, which reduces the joint impact by reducing the output torque, η = sigmoid(|θ i - θ max,i |), to achieve a smooth transition.

[0052] The advantages of the present invention lie in the provided exoskeleton manipulator control method. Through an accurate hand biomechanical model and a high-density sensor configuration, it can obtain the contact force and joint angle information of the hand in real time, improving the control accuracy. Through the improved Bouc-Wen model and the adaptive cooperative control strategy, it can effectively process the mapping relationship between the electromyogram signal and the manipulator movement, adapting to different operating environments.

[0053] The advantages of the present invention also lie in the provided exoskeleton manipulator control method. By using a modified dual Kalman filter to dynamically compensate for the mechanical resistance of the hand, it can estimate and compensate for the joint friction force in real time, improving the control stability. Through variable impedance control and a safety boundary protection mechanism, it can dynamically adjust the impedance parameters according to the stiffness of the grasped object and automatically switch to the admittance control mode when detecting a sudden external force, ensuring the safe operation of the exoskeleton manipulator. Brief Description of the Drawings

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0055] Figure 1 It is a schematic diagram of the exoskeleton manipulator control method of this application. Detailed Embodiments

[0056] The following details the embodiments of the present invention. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention, and should not be construed as a limitation of the present invention.

[0057] As Figure 1 shown, a kind of exoskeleton manipulator control method of this application includes the following steps:

[0058] S1: Construct a hand biomechanical model, define the degrees of freedom of the finger joints, and perform sub-joint modeling based on the driving dynamics equation of the Bowden cable.

[0059] S2: Deploy high-density sensors on the hand, including a fingertip tactile array, a joint micro encoder, and a surface electromyogram sensor. The fingertip tactile array is used to measure the contact force distribution, the joint micro encoder is used to measure the joint angle, and the surface electromyogram sensor is used to collect the electromyogram signals of the forearm flexor / extensor muscle groups. The surface electromyogram sensor uses an 8-channel differential motor and is deployed on the forearm flexor / extensor muscle groups.

[0060] S3: Improve the hand-specific Bouc-Wen model, introduce the interphalangeal coupling term to correct the hysteresis state equation, and identify the micro-hysteresis parameters through a hierarchical identification algorithm.

[0061] S4: Design an adaptive cooperative control strategy to achieve the fuzzy mapping from EMG signals to joint torques, and optimize the cooperative tracking error through the multi-joint parameter coupling adaptive law.

[0062] S5: Use a modified dual Kalman filter to dynamically compensate for the hand mechanical resistance, including a state filter and a parameter filter, which are used to estimate the joint friction force in real time and update the friction coefficient respectively.

[0063] S6: Optimize the closed-loop execution and safety strategy, including variable impedance control, finger cooperative trajectory optimization, and safety boundary protection mechanism.

[0064] In the embodiment of the present application, in step S1, the degree of freedom of the finger joint is 14, and the dynamic equation of each joint is:

[0065]

[0066] where i = 1, 2, …, 14 represents the joint number, τ i is the driving torque of the i-th joint, J i is the inertia term, θ i is the joint angle, B i is the damping term, K i is the elastic term, H i (z i ) is the hysteresis term, τ ext,i is the external torque term, F fric,i is the friction term of the i-th joint, which includes the coupling effect of fingertip contact friction and joint bearing friction.

[0067] In the embodiment of the present application, in step S2, the fingertip tactile array is a 4*4 piezoresistive sensor, the sampling rate is 1 kHz, and the calculation formula of the contact force distribution is:

[0068]

[0069] where w j is the normalized weight, P j is the measurement value of the j -th sensor, w j ∈[0, 1], ∑w j = 1.

[0070] In the embodiment of the present application, in step S3, introduce the fingertip coupling term, and the improved Bouc-Wen hysteresis state equation is:

[0071]

[0072] Among them, z i represents the hysteresis state variable of the i-th joint, and C ij is the interphalangeal coupling coefficient, which is calibrated through a grasping experiment.

[0073] The designed finger micro-motion excitation signal has an amplitude of ±5° and a frequency of 0.5 - 10 Hz. The micro-hysteresis parameters are identified through a hierarchical identification algorithm. The outer layer uses the PSO optimization algorithm to optimize α i , β i , γ i and n. α i is the hysteresis stiffness coefficient of the i-th joint, controlling the linear part of the hysteresis. β i is the non-linear hysteresis damping parameter of the i-th joint, affecting the saturation rate. γ i is the non-linear hysteresis shape parameter of the i-th joint. n ∈ [1, 3] is the order of the hysteresis non-linearity.

[0074] The inner layer uses the least squares method to update C ij and F fric,i in real time.

[0075] In the implementation manner of this application, in step S4, the fuzzy mapping formula from the EMG signal to the joint torque is:

[0076]

[0077] Among them, a m is the EMG activation degree of the m-th channel (RMS calculation window 10 ms), a th is the individualized activation threshold (adaptively calibrated when first used). K sEMG = 0.5 N*m / V represents the EMG signal - torque conversion gain coefficient, which is determined through a calibration experiment. θ max,i represents the maximum allowable angle of the i-th joint, avoiding the mapping value mutation caused when the joint angle approaches the limit value.

[0078] In the implementation manner of this application, in step S4, the definition of the cooperative tracking error is:

[0079]

[0080] Among them, λ = 0.3, w i is the weight coefficient, w i ∝ 1 / J i , N = 14, θ i is the angle of the i-th joint, θ j is the angle of the j-th joint.

[0081] The improved adaptive algorithm is as follows:

[0082]

[0083] where, represents the joint equivalent inertia for real-time adjustment, where ^ represents the derivative, γJ is the adaptive gain used to control the update rate of the inertia parameter, e i is the tracking error of the i-th joint, J nom is the nominal inertia value, initially set according to the biomechanical model, J max is the maximum inertia value.

[0084] In the implementation manner of this application, in step S5, the prediction and update equations of the state filter are as follows:

[0085]

[0086] where A i = diag[0.92, 0.85] (including static friction and dynamic friction states), A i is the state transition matrix, describing the dynamic decay characteristics of the friction state, calibrated through a step response experiment, represents the first derivative of the angle of the i-th joint, represents the first derivative of the angle of the i-th joint at the previous moment, is the friction state vector of the i-th joint (positive and negative are static and dynamic friction components respectively), where the superscript ^ indicates processing at the derivative level. represents the friction state vector of the i-th joint at the previous moment, K f,i represents the Kalman gain matrix of the i-th joint state filter, F tip,i represents the contact force distribution of the i-th fingertip tactile array, , H i is the observation matrix, mapping the friction state to the sensor measurement space, where, = 0.01 rad / s, is the speed normalization factor, B fric,i is the input coupling matrix, which is the excitation weight of the angular velocity on the friction state. In the prediction equation, the predicted friction state vector is derived based on the first derivative of the angle of the i-th joint and the friction state vector at the previous moment (i.e., k-1 ). Then, in the update equation, the updated predicted friction state vector is obtained based on the predicted friction state vector and the force measurement value F tip,i of the i-th fingertip to achieve state filtering.

[0087] In the implementation manner of this application, in step S5, the update equation of the parameter filter is as follows:

[0088]

[0089] Among them, σ i is the friction coefficient, is the friction coefficient at the current moment, represents the friction coefficient at the previous moment, K σ,i is the parameter update gain, controlling the parameter convergence rate, is the model sensitivity matrix.

[0090] The constraint condition is: σ 0,i > 0.1N, σ 1,i ∈ [0.01, 0.1] N·s / rad.

[0091] The compensation torque formula for the fingertip contact force feedforward compensation is:

[0092]

[0093] Among them, τ comp,i is the compensation torque of the i-th joint, x j is the position coordinate of the fingertip contact point, F tip,j represents the contact force of the j-th sensor in the 4*4 fingertip tactile array represents the derivative of the i-th joint angle with respect to the j-th sensor in each fingertip tactile array, K tip represents the mapping coefficient, used to gain the contact force mapping.

[0094] In the embodiment of the present application, step S6 is specifically:

[0095] Adjust the impedance parameters according to the stiffness of the grasped object to achieve variable impedance control, and automatically switch to the admittance control mode when a sudden external force (dF / dt > 100N / s) is detected.

[0096] Online correct the finger collaborative trajectory based on the contact force distribution to ensure the stability of multi-finger enveloping grasping.

[0097] Set up a safety boundary protection mechanism to ensure the safe operation of the exoskeleton manipulator through the joint angle-torque envelope constraint.

[0098] In the embodiment of the present application, in step S6, the impedance parameter adjustment formula for variable impedance control is:

[0099]

[0100] Among them, K p is the stiffness parameter, K p0 is the set basic stiffness parameter, is the stiffness change coefficient, B dis the damping parameter, B d0 is the set basic damping parameter denotes F tip the second-order vector norm of ||x|| n is the standard form of the vector norm, that is, the magnitude of the vector in this vector space. The expression is:

[0101]

[0102] where n is the order and N is the number of elements of the vector, such as F tip Since it is a 4*4 fingertip tactile array, its N = 16

[0103] The online correction formula for the finger cooperation trajectory is:

[0104]

[0105] where is the real-time reference angle after correction of the i-th joint (where the superscript new denotes real-time), θ d,i is the original desired angle of the i-th joint, originating from the intended trajectory generated by the electromyogram signal, neighbors is the set of cooperative joints, including the joints of all participating fingers in the envelope grip, θ d,j is the desired angle of the neighboring joint j associated with joint i

[0106] H(zi) is used to convert the lagged contact state z i into equivalent potential energy, H(z i ) = 0.5k p z i 2 , where k p is the virtual contact stiffness coefficient), K c is the cooperation gain, K h is the lag term gain

[0107] The joint angle-torque envelope constraint formula for the safety boundary protection mechanism is:

[0108]

[0109] where, τ cmd,i is the control torque of the i-th joint of the final output, τ comp,i is the compensation torque of the i-th joint, τ i is the actual torque of the current output, τ max is the maximum output torque limit, τ soft is the buffer target torque when the joint approaches the safety limit, reducing the joint impact by reducing the output torque, η = sigmoid(|θ i -θmax,i |), to achieve a smooth transition.

[0110] The above has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form. Any technical solutions obtained by using equivalent replacements or equivalent transformations fall within the protection scope of the present invention.

Claims

1. A method for controlling an exoskeleton manipulator, characterized in that: The following steps are involved: S1: Construct a hand biomechanical model, define the degrees of freedom of the finger joints, and perform joint modeling based on the driving dynamics equations of the Bowden cable; S2: Deploy high-density sensors on the hand, including fingertip tactile arrays, joint micro-encoders, and surface electromyography sensors. The fingertip tactile array is used to measure contact force distribution, the joint micro-encoders are used to measure joint angles, and the surface electromyography sensors are used to collect electromyographic signals of the forearm flexor / extensor muscle groups. S3: Improve the hand-specific Bouc-Wen model, introduce inter-finger coupling terms to correct the hysteresis state equation, and identify the micro-hysteresis parameters through a hierarchical identification algorithm; S4: Design an adaptive collaborative control strategy to realize the fuzzy mapping from EMG signals to joint torques, and optimize the collaborative tracking error through the multi-joint parameter coupling adaptive law; S5: A modified dual Kalman filter is used to dynamically compensate for the mechanical resistance of the hand, including a state filter and a parameter filter, which are used to estimate the joint friction force and update the friction coefficient in real time, respectively; S6: Optimize closed-loop execution and safety strategies, including variable impedance control, finger collaborative trajectory optimization, and safety boundary protection mechanism; In step S1, the finger joints have 14 degrees of freedom, and the dynamic equation of each joint is: ， Where i=1,2,…,14 represents the joint number, τ i is the driving torque of the i-th joint, J i is the inertia term, θ i is the joint angle, B i is the damping term, K i is the elastic term, H i (z i ) is the lag term, τ ext,i is the external torque term, F fric,i is the i-th joint friction term, which includes the coupling effect of fingertip contact friction and joint bearing friction; In step S2, the fingertip tactile array is a 4×4 piezoresistive sensor, and the contact force distribution F tip The calculation formula is: ， Among them, w j is the normalized weight, P j is the measurement value of the jth sensor, w j ∈[0,1],∑w j =1; In step S3, the improved Bouc-Wen hysteresis state equation is: ， Among them, z i represents the hysteresis state variable of the i-th joint, C ij is the inter-finger coupling coefficient, which is calibrated through grasping experiments; The micro-hysteresis parameters are identified by a hierarchical identification algorithm, and the outer layer uses the PSO optimization algorithm to optimize α i , β i , γ i and n,α i is the hysteresis stiffness coefficient of the i-th joint, controlling the hysteresis linear part, β i is the nonlinear hysteresis damping parameter of the i-th joint, affecting the saturation rate, γ i is the nonlinear hysteresis shape parameter of the i-th joint, n∈[1,3], is the hysteresis nonlinear order; The inner layer uses the least squares method to update C in real time ij and F fric,i ; In step S5, the prediction and update equations of the state filter are: ， Among them A i = diag[0.92, 0.85],A i is the state transfer matrix, describing the dynamic attenuation characteristics of the friction state, calibrated by step response experiments. represents the first derivative of the angle of the i-th joint, represents the first derivative of the angle of the i-th joint at the previous moment, is the friction state vector of the i-th joint, represents the friction state vector of the i-th joint at the previous moment, K f,i represents the Kalman gain matrix of the i-th joint state filter, F tip,i represents the contact force distribution of the i-th fingertip tactile array, , H i is the observation matrix, which maps the friction state to the sensor measurement space, where , is the velocity normalization factor, B fric,i is the input coupling matrix, which is the excitation weight of the angular velocity on the friction state; In step S5, the update equation of the parameter filter is: ， Among them, σ i is the friction coefficient, is the friction coefficient at the current moment, represents the friction coefficient at the previous moment, K σ,i is the parameter update gain, which controls the parameter convergence rate. is the model sensitivity matrix; The compensation torque formula for fingertip contact force feedforward compensation is: ， Among them, τ comp,i is the compensation torque of the ith joint, x j is the coordinate of the fingertip contact point, F tip,j represents the contact force of the jth sensor in the 4*4 fingertip tactile array, represents the derivative of the i-th joint angle to the j-th sensor in each fingertip tactile array, K tip Represents the mapping coefficient, which is used to gain the contact force mapping.

2. The exoskeleton manipulator control method according to claim 1, characterized in that: In step S4, the fuzzy mapping formula from electromyographic signal to joint torque is: ， Among them, a m is the myoelectric activation degree of the mth channel, a th is the individual activation threshold, K sEMG =0.5N*m / V, represents the electromyographic signal-torque conversion gain coefficient, which is determined by calibration experiments, θ max,i Represents the maximum allowed angle of the i-th joint, avoiding sudden changes in the mapping value when the joint angle approaches the limit.

3. The exoskeleton manipulator control method according to claim 1, characterized in that: In step S4, the collaborative tracking error is defined as: ， Where λ=0.3, w i is the weight coefficient, w i ∝1 / J i , N=14, θ i is the angle of the i-th joint, θ j is the angle of the jth joint; The improved adaptive algorithm is: ,in, represents the joint equivalent inertia adjusted in real time, where ^ represents the derivative, γJ is the adaptive gain, which is used to control the update rate of the inertia parameters, and e i is the tracking error of the i-th joint, J nom is the nominal inertia value, which is set according to the initial biomechanical model. max It is the maximum inertia value to prevent parameter overshoot.

4. The exoskeleton manipulator control method according to claim 1, characterized in that: Step S6 is specifically as follows: The impedance parameters are adjusted according to the stiffness of the grasped object to achieve variable impedance control, and it automatically switches to the admittance control mode when a sudden external force is detected; The finger collaborative trajectory is corrected online based on the contact force distribution to ensure the stability of multi-finger envelope grasping; A safety boundary protection mechanism is set up to ensure the safe operation of the exoskeleton manipulator through joint angle-torque envelope constraints.

5. The exoskeleton manipulator control method according to claim 4, characterized in that: In step S6, the impedance parameter adjustment formula of the variable impedance control is: ， Among them, K p is the stiffness parameter, K p0 is the set basic stiffness parameter, ∆K is the stiffness variation coefficient, B d is the damping parameter, B d0 is the basic damping parameter set, Indicates F tip The second-order vector norm of ; The online correction formula of finger collaborative trajectory is: ， in, is the corrected real-time reference angle of the i-th joint, θ d,i is the original expected angle of the i-th joint, which comes from the intention trajectory generated by the electromyographic signal, neighbors is the set of collaborative joints, including the joints of all fingers involved in the enveloping grasp, θ d,j is the expected angle of the neighboring joint j associated with joint i, H(z i ) is used to convert the delayed contact state z i Converted into equivalent potential energy, K c is the synergistic gain, K h is the lag term gain; The joint angle-torque envelope constraint formula of the safety boundary protection mechanism is: ， Among them, τ cmd,i is the final output control torque of the i-th joint, τ comp,i is the compensation torque of the i-th joint, τ i is the actual torque of the current output, τ max is the maximum output torque limit, τ soft Buffer target torque when the joint approaches the safety limit, reduce joint impact by reducing output torque, , to achieve a smooth transition.

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