A feedforward admittance control method for rope-driven exoskeletons

Abstract

This invention relates to a feedforward-admittance control method for rope-driven exoskeletons, ensuring the control performance of the exoskeleton system during non-rhythmic human movements. The invention utilizes an inertial measurement unit and a motor encoder to model the human-machine coupled kinematics; by fusing tension information from a tension sensor measuring the drive rope, it models and identifies the human-machine coupled stiffness; using the kinematic and stiffness models of the human-machine coupling, it designs the feedforward term of the control method; it designs an iterative learning strategy to estimate the critical length of the drive rope; and it designs an admittance control algorithm, fusing the feedforward term to form a feedforward-admittance control method, achieving precise control of the rope-driven exoskeleton under non-rhythmic human movements. This invention addresses the problem of decreased control performance of exoskeleton robots when facing irregular human movement patterns, such as rapid acceleration and deceleration, expanding the application scenarios of exoskeleton robots.

Description

Technical Field

[0001] This invention relates to the field of exoskeleton robots and control systems, and specifically to a feedforward-admittance control method suitable for rope-driven exoskeletons. Background Technology

[0002] Rope-driven exoskeleton robots, as a type of exoskeleton robot, use a motor to rotate and drive a rope wheel to contract the driving rope, thereby generating tension on the driving rope and providing more compliant, biomimetic linear assistance to the target human joints. They are an important component of assistive robots and high-end assistive equipment. However, in order to play the expected role in the human-machine interaction process, the precise control capability of rope-driven exoskeleton robots is the key to determining their assistive functionality and is also a prerequisite for providing the desired assistance to the movement of patients, workers, and special operations personnel. Therefore, research on precise control methods for rope-driven exoskeletons is of great significance.

[0003] Unlike the control methods for independent robots such as autonomous vehicles, exoskeleton robots must be worn by the user at all times and move in tandem. Therefore, in addition to considering adaptability to the environment, the control must also adapt to human movement. Especially considering the low bandwidth of the exoskeleton itself due to the flexible drive cables and some flexible materials, achieving a high-precision, highly synchronized cable-driven exoskeleton robot control method is a major challenge. Human movement can be divided into rhythmic movement, such as regular walking, and non-rhythmic movement, such as sudden stops, rapid acceleration and deceleration, and transitions from rest to movement and from movement to rest. While there are existing technologies for controlling rhythmic human movement, such as iterative learning control, there is a lack of control methods for exoskeleton robots that address non-rhythmic human movement. Strictly speaking, non-rhythmic motion is more representative of daily human life, and any motion that lacks periodicity or repeatability can be considered non-rhythmic. Therefore, even if a person walks on flat ground, if the walking speed, stride frequency, stride length, and other characteristics change between different time-phase cycles, then this walking on flat ground can also be considered a non-rhythmic motion. Because non-rhythmic motion lacks obvious periodicity, current exoskeleton control methods that utilize historical control cycle data, such as iterative learning control cycle-by-cycle, are no longer applicable, and open-loop curve tracking control is even less desirable. Therefore, it is necessary to invent new solutions to address the problem of precise control of rope-driven exoskeleton robots for non-rhythmic motion. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a feedforward-admittance control method suitable for cable-driven exoskeletons. Without utilizing historical control information, it can ensure the precise control performance of cable-driven exoskeletons under non-rhythmic human movements, effectively promoting the practical application of exoskeleton robots in daily life.

[0005] The technical solution adopted by this invention to achieve the above objectives is: a feedforward admittance control method suitable for rope-driven exoskeletons, which performs the following steps to realize the modeling and control of the rope-driven exoskeleton in non-rhythmic motion states, the method including the following steps:

[0006] By utilizing the joint angles of the human body and the length of the drive rope, kinematic modeling of the human-machine coupler is achieved; by utilizing the length and tension of the drive rope, stiffness modeling and identification of the human-machine coupler are achieved; and by utilizing the kinematic and stiffness models of the human-machine coupler, feedforward terms for robot control are constructed.

[0007] An iterative learning strategy is used to estimate the critical length of the drive rope in the feedforward term online; admittance control is used to obtain the change in drive rope length for the current desired tension from the perspective of feedback control; the length control quantity of the drive rope is calculated using the critical length and the change in drive rope length; and proportional-integral-damped control is used to convert the length control quantity of the drive rope into a speed control quantity.

[0008] The feedback control term, superimposed with the speed control quantity derived from the feedforward term, is input to the driver to achieve final assist control.

[0009] The human joint angle information is measured by placing a rotary encoder at the human joint position, or by measuring and solving using two inertial measurement units of adjacent limbs; the drive rope length information is obtained by online estimation using a motor encoder and rope wheel model information.

[0010] The kinematic model of the human-machine coupling body consists of two parts: one part is the change in the length of the driving rope A(t) caused by the rotational motion of the human joints; the other part is the length value C(t) of the driving rope itself.

[0011] The tension information of the drive rope is measured by a tension sensor at the end of the drive rope. The stiffness identification process is carried out experimentally by having a subject wear the exoskeleton robot, maintain a fixed posture, and simultaneously measure the length and tension information of the drive rope. The stiffness coefficient K of the exoskeleton body is then fitted to obtain the stiffness coefficient K of the exoskeleton body. exo .

[0012] The kinematic and stiffness models of the human-machine coupled body, implemented through feedforward term construction, are represented as follows:

[0013] L(t) = A(t) + B(t) + C(t)

[0014] Where L(t) represents the overall feedforward term for the expected length of the drive rope, that is, the ideal change in the length of the drive rope obtained from prior knowledge; A(t) and C(t), as mentioned above, are feedforward terms related to the kinematic model of the human-machine coupler; B(t) is a feedforward term related to the stiffness model of the human-machine coupler.

[0015] The iterative learning strategy is used to estimate the critical length of the drive rope, where critical refers to the state between tension and slack. Here, P represents the total length of the drive rope, which is the sum of its exposed length and its length within the sleeve. The estimation process is as follows:

[0016]

[0017] in It is the total length of the drive rope in the critical state; the superscript m indicates the m-th gait cycle; t sMF This indicates the moment when the tension in the driving rope is at its maximum during the swing phase of the human lower limbs; P mea F represents the total length of the drive rope as actually measured; mea K represents the actual measured tension in the drive rope. coupled This refers to the stiffness of the human-machine coupling, which is determined by the estimated exoskeleton stiffness K. exo With human joint stiffness K joint The result was obtained through series calculations. Finally, since the length of the drive rope inside the casing is a fixed value, let's assume it's D, then it can be calculated... We obtain the critical length of the exposed drive rope, C(t), in the m-th gait cycle.

[0018] The admittance control, and the change in drive rope length obtained from the admittance control with respect to the desired tension of the current drive rope, are expressed as follows:

[0019]

[0020] Where ΔP is the change in length of the driving rope; F des is the desired assist value; M, B, and K are the three parameters of inertia, damping, and stiffness used to guide the control, which determine the servo compliance of the control process; s represents the complex field.

[0021] The process of converting the position of the drive rope into a velocity quantity through proportional-integral-damping control is as follows:

[0022]

[0023] Where V cmd It is the converted velocity quantity; K p K i K dThese are the coefficients for proportional, integral, and damping, respectively.

[0024] The method uses an inertial unit to collect changes in joint angles, a tension sensor to collect tension at the end of the rope, and a motor encoder to collect changes in the length of the drive rope.

[0025] The present invention has the following beneficial effects and advantages:

[0026] This invention utilizes kinematic and stiffness modeling of the human-machine coupling body to obtain feedforward terms, and then obtains feedback terms through admittance control. By fusing the feedforward and feedback terms, precise control of a cable-driven exoskeleton robot can be achieved in response to non-rhythmic human movements. This method does not utilize historical control information; instead, it combines feedforward and feedback to naturally shield the performance degradation caused by the non-periodic nature of non-stationary motion in conventional control. This method maintains good assist tracking control accuracy even in applications involving sudden stops, varying paces, and varying periods, exhibiting strong adaptability and low environmental requirements, making it more suitable for application in daily life and providing assistance for various human movements and task modes. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the kinematics and stiffness modeling of the human-machine coupling body in this invention;

[0028] Figure 2 This is a diagram illustrating the process of identifying the stiffness coefficient in this invention;

[0029] Figure 3 This is a block diagram of the overall control method in this invention;

[0030] Figure 4 This is a diagram showing the components and tracking status of the control quantities in this invention;

[0031] Figure 5 This is a diagram illustrating the tracking and control effect of the actual assist curve in this invention;

[0032] Figure 6 This is a graph showing the bandwidth test results of the control method in this invention. Detailed Implementation

[0033] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0035] This invention provides a control method that ensures the control performance of a rope-driven exoskeleton robot during non-rhythmic human movements without utilizing historical control information. The invention utilizes an inertial measurement unit and a motor encoder to model the kinematics of the human-machine coupling; it integrates the tension information of the drive rope measured by a tension sensor to model and identify the stiffness of the human-machine coupling; it designs and forms the feedforward term of the control method using the kinematic and stiffness models of the human-machine coupling; it designs an iterative learning strategy to iteratively estimate the critical length of the drive rope, compensating for the estimation deviation of the drive rope length caused by the movement of the exoskeleton within the body; it designs an admittance control algorithm, integrates the feedforward term to form a feedforward-admittance control method, and achieves precise control of the rope-driven exoskeleton under non-rhythmic human movements based on actuator speed control. This invention solves the problem of decreased control performance of exoskeleton robots when facing irregular human movement patterns, such as rapid acceleration and deceleration, expands the application scenarios of exoskeleton robots, and helps promote the daily application and development of exoskeleton robots.

[0036] Taking a cable-driven exoskeleton robot that assists in ankle joint movement as an example, this robot provides plantar flexion assistance to the human ankle joint during the standing phase, while controlling the drive cable to be in a slack state and not providing assistance during the swinging phase. The specific implementation method is described below. Figure 1 As shown, (L1+L2) represents the total length of the artificial tendon (artificial tendon is defined as the distance between the fixed point of the drive rope's sleeve and the fixed point of the drive rope); F represents the auxiliary pulling force provided by the robot via the drive rope; r represents the equivalent auxiliary lever arm relative to the human joint; φ joint Indicates the angle of a human joint; K exo K sheath K joint K cable and K end Let L1 and L2 represent the stiffness coefficients of the robot's wearable component, the drive cable sleeve, the human joint, the drive cable, and the fixed point at the end of the drive cable, respectively; g(*) and f(*) represent the stiffness coupling functions, respectively. The exoskeleton robot generates assist force by contracting the drive cable to shorten the length of the artificial tendon. By modeling L1 and L2, the relationship between the artificial tendon length and the tension can be determined, thereby obtaining the relationship between the drive cable length and the tension, enabling feedforward control to improve the tracking control performance of the desired assist curve. The length of L1 can be modeled as follows:

[0037]

[0038] Where t and t0 represent the current and starting times of the exoskeleton's operation, respectively; ΔL1 represents the offset, which is the downward displacement of the exoskeleton's worn parts under repeated stretching. Note that the exoskeleton assist F(t) here does not represent a parametric function of time t, but rather the tension of the drive rope at time t. L2 can be modeled as:

[0039]

[0040] Define joint angle φ joint The value is zero when the human body is standing upright. exo and K joint Compared to K sheath K cable and K end It needs to be 1-2 orders of magnitude smaller, so the total length L of the artificial tendon can be expressed as follows:

[0041]

[0042] Where K coupled Human-machine coupling stiffness (K exo ×K joint / (K exo +K joint To solve this formula, we need to solve A(t), B(t), and C(t) separately first.

[0043] ■A(t) represents the change in the length of the artificial tendon caused by human joint movement, where the lever arm r can be obtained by manual measurement; and the angle φ joint It can be measured using two inertial measurement units or one encoder.

[0044] ■B(t) represents the change in artificial tendon length caused by the applied tension of the drive cable, which relates to the stiffness of the exoskeleton robot's wearable parts and the human joints. The stiffness of the human joint is K. joint It can be considered a constant; while the stiffness K of the robot's wearable part can be considered a constant. exo It can be obtained through experiments. For example... Figure 2 As shown, K exo The value is considered to be a fixed value of 123.1 N / cm. As shown in the figure, the exoskeleton body gradually slides down under repeated stretching. This phenomenon is particularly obvious in the first few gaits, and tends to stabilize after 7-10 gaits. This shows that it is necessary to estimate the offset ΔL1 in C(t).

[0045] ■C(t) represents the critical length of the artificial tendon, i.e., the critical state between tension and relaxation. Without considering ΔL1, this value is only related to the tension of the drive rope and the angle of the human joint. However, due to the existence of ΔL1, the value of C(t) is variable even under fixed tension and angle. Considering that ΔL1 changes slowly, we assume it is constant over one gait cycle; therefore, the length P of the drive rope when the artificial tendon is in the critical state can be estimated. base As follows (note that in the specific implementation, L represents the length of the artificial tendon, and P represents the total length of the drive rope):

[0046]

[0047] Where the superscript m represents the m-th gait cycle; the subscript mea represents the measured value; P represents the total length of the drive rope; t SMF This indicates the moment of maximum tension in the drive rope during the swing phase. Selecting the moment of maximum tension during the swing phase for critical length estimation serves two purposes: firstly, to reduce the control computational load during the standing phase; and secondly, because the drive rope tension during the swing phase is relatively low but still taut, which reduces the impact of stiffness estimation errors on the critical length estimation of the artificial tendon. Since the difference between the drive rope length and the artificial tendon length is constant, C can be obtained simultaneously.

[0048] During the swing phase, position feedback control is used to release the drive rope, and the issued drive rope release command can be expressed as follows:

[0049]

[0050] in This represents the swing phase in the m-th gait cycle, the drive rope length command issued by the controller, and the drive rope will then be fixed at that length until the end of the swing phase; F SW This indicates the desired maximum tension in the oscillating phase, which can be selected from 3N to 5N; F SMF δ represents the maximum tension in the actual measured oscillating phase. SW The coefficient determines the update rate of the drive rope length command in the swing phase. A larger coefficient value results in a more drastic update; a value between 0.6 and 0.8 can be selected. Therefore, the drive rope length command in the swing phase can be updated gradually, allowing the maximum drive rope tension in the swing phase to gradually converge to the set desired value F. SW .

[0051] During the standing phase, in the rising stage of the desired assist curve, if the desired force value is less than 5N, it is set to 5N to allow the drive rope to reach tension as quickly as possible. Then, estimated parameters A, B, and C are used, where A and B are used for feedforward speed control to assist tension tracking; and C is used for feedback admittance control to assist drive rope pretensioning. Admittance control uses the tension deviation to generate the position correction amount ΔP as follows:

[0052]

[0053] Where M, B, and K are admittance parameters that determine the dynamic compliance of the force control process. The desired length command of the drive rope can be expressed as follows:

[0054] P cmd (t)=max(P base -r·φ joint P mea )+ΔP

[0055] The function max(*) represents taking the larger value; P base -r·φ joint Used to achieve pretensioning of the drive rope. A position controller based on proportional-integral-damped injection can be employed to generate position commands P. cmd (t) corresponds to the speed command V cmd (t) is as follows:

[0056]

[0057] Where K p K i K d These are the coefficients for proportional, integral, and damping, respectively; s represents the complex domain. Introducing feedforward speed control, the final speed command V... fin (t) is as follows:

[0058]

[0059] Where dφ DF (t) / dt represents the joint angular velocity; F des (t) represents the desired boost curve function. The driver is set to speed mode via the final instruction V. fin The data is sent to the driver to achieve assist curve tracking control of the exoskeleton robot.

[0060] The overall block diagram of this control method is as follows: Figure 3As shown, for the control system of an exoskeleton robot, it is necessary to first use sensors, such as inertial measurement units, to collect information data about the user's body and the exoskeleton robot, and then generate an assist curve corresponding to the current scene based on the gait activity recognition results. The assist curve is sampled using the control frequency as the sampling frequency to obtain the desired assist point F. des Then, it is input into the control system as the desired command. The method of this invention first obtains the speed command V for the current desired assist point through admittance control and proportional-integral-damping control. cmd Meanwhile, the feedforward control obtains the feedforward term dL / dt based on the currently known information and the prior model, where L is the length of the artificial tendon mentioned earlier; the feedback term and the feedforward term are fused to generate the final instruction V. fin The data is then input into the driver to achieve tracking control of the desired assist point. This process is a separate loop, usually with a frequency of 500Hz or 1000Hz, to achieve precise control of the entire assist curve and complete the assistance for human movement.

[0061] To verify the effectiveness of the above method, an experiment was conducted using a cable-driven exoskeleton robot designed to assist ankle plantar flexion movements. In the experiment, admittance parameters M = 0.03, B = 0.01, and K = 1.20; the equivalent assist arm r = 0.075 m; and the desired assist curve was given as a piecewise Gaussian function curve, with the lower leg swing angle θ as the independent variable. The desired curve can be expressed as follows:

[0062]

[0063] The parameters of the two Gaussian curves were chosen as follows: A = 150N, μ = 12.434°, σ1 = 9.031°, and σ2 = 5.775°. These two Gaussian curves were connected at their peak values ​​to form the complete desired curve. All control process data was recorded online in a file on a PCM3365 industrial computer, and subsequent data processing, such as gait period segmentation, plotting, and error calculation, was performed in Matlab. It should be noted that this experiment aims to verify whether the control performance of the proposed control method can be guaranteed for non-rhythmic motion when the control method does not use historical gait period information (historical gait period information for non-rhythmic motion is not relevant).

[0064] Because non-rhythmic movements lack continuity, the amount of data that can be collected in exoskeleton control verification experiments focusing solely on non-rhythmic activities is very limited. However, the essential reason for distinguishing between rhythmic and non-rhythmic movements in the control method itself depends on whether the control method utilizes historical periodic data. If the control method's rules are based on historical periodic data, then it is necessary to distinguish between rhythmic and non-rhythmic movements. Therefore, we can assume that since the proposed feedforward-admittance control method does not utilize historical periodic data, verifying that the proposed control method can adapt to rhythmic movements also proves that the method can adapt to non-rhythmic movements (because without utilizing historical gait periodic data, rhythmic and non-rhythmic movements are considered the same type of movement by the controller). Therefore, during the experiment, subjects were asked to walk on a treadmill at their most comfortable speed (4.8 km / h), and no movement hindrances were imposed on the subjects during the walking process.

[0065] By final instruction V fin As can be seen from the composition formula, it can include three parts: the feedback control quantity generated by admittance control (hereinafter referred to as FB), the feedforward quantity generated by joint motion (hereinafter referred to as FF_DF), and the feedforward quantity generated by the desired assist curve and stiffness model (hereinafter referred to as FF_FO). Each part is as follows: Figure 4 As shown above (the error bars in the figure represent the standard deviation of 10 gait cycle data), after the speed command curve is sent to the driver, the driver controls the motor to perform speed tracking, forming the actual speed curve, as shown below. Figure 4 As shown below, compared to the feedback-generated commands (FB), the feedforward commands (FF_DF, FF_FO) are more advanced. For example, around 50% of the gait cycle, the feedforward value (FF_DF) can advance the speed command, corresponding to the human joint movement, pre-stretching the steel wire, so that the exoskeleton can provide sufficient and rapid assistance to the human body during the pre-swing phase. Meanwhile, the feedforward value (FF_FO), during the assist curve tracking process, modifies the speed command in advance based on the rate of change of the desired curve, reducing tracking delay. The curves corresponding to the speed commands in the above figure are as follows... Figure 5 As shown, even without utilizing historical gait cycle data, precise tracking control of the assist curve can be achieved solely using the kinematic and stiffness models of the human-machine coupling body. By collecting data from 10 consecutive gait cycles, the performance of the control method was calculated. The root mean square error of the tracking control for the entire curve was 0.309N ± 3.360N; the root mean square error of the tracking control for the peak force was 1.307N ± 0.830N.

[0066] In addition to verifying the tracking control performance of the control method, the bandwidth of the control method was also evaluated. The experimental scenario for the bandwidth test involved instructing a subject to wear an exoskeleton robot on their right leg, maintaining the leg in a pre-swing posture, and then the control method issued sinusoidal assist commands (20N at the trough, 120N at the peak). The frequency of the sine curve increased from 0.1Hz to 20Hz in 0.1Hz increments, with each frequency phase lasting 1 second. Fourier transforms were performed on the collected expected and actual force data using Matlab, and Bode plots were generated. Figure 6 As shown, the control method has a bandwidth of 11.0 Hz and a phase delay of 150.3° without relying on historical periodic data, which can meet the application requirements of rope-driven exoskeleton robots.

[0067] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should be considered within the scope of protection of the present invention.

Claims

1. A feedforward admittance control method suitable for rope-driven exoskeletons, characterized in that, The following steps are used to model and control the non-rhythmic motion state of a rope-driven exoskeleton: By utilizing the joint angles of the human body and the length of the drive rope, kinematic modeling of the human-machine coupler is achieved; by utilizing the length and tension of the drive rope, stiffness modeling and identification of the human-machine coupler are achieved; and by utilizing the kinematic and stiffness models of the human-machine coupler, feedforward terms for robot control are constructed. An iterative learning strategy is used to estimate the critical length of the drive rope in the feedforward term online; admittance control is used to obtain the change in drive rope length for the current desired tension from the perspective of feedback control; the length control quantity of the drive rope is calculated using the critical length and the change in drive rope length; and proportional-integral-damped control is used to convert the length control quantity of the drive rope into a speed control quantity. The feedback control term, superimposed with the speed control quantity derived from the feedforward term, is input to the driver to achieve final assist control.

2. The feedforward admittance control method for a rope-driven exoskeleton according to claim 1, characterized in that, The human joint angle information is measured by placing a rotary encoder at the human joint position, or by measuring and solving using two inertial measurement units of adjacent limbs; the drive rope length information is obtained by online estimation using a motor encoder and rope wheel model information.

3. The feedforward-admittance control method for a rope-driven exoskeleton according to claim 1, characterized in that, The kinematic model of the human-machine coupling body consists of two parts: one part is the change in the length of the driving rope A(t) caused by the rotational motion of the human joints; the other part is the length value C(t) of the driving rope itself.

4. The feedforward admittance control method for a rope-driven exoskeleton according to claim 1, characterized in that, The tension information of the drive rope is measured by a tension sensor at the end of the drive rope. The stiffness identification process is carried out experimentally by having a subject wear the exoskeleton robot, maintain a fixed posture, and simultaneously measure the length and tension information of the drive rope. The stiffness coefficient K of the exoskeleton body is then fitted to obtain the stiffness coefficient K of the exoskeleton body. exo .

5. A feedforward admittance control method for a rope-driven exoskeleton according to claim 3, characterized in that, The kinematic and stiffness models of the human-machine coupled body, implemented through feedforward term construction, are represented as follows: L(t) = A(t) + B(t) + C(t) Where L(t) represents the overall feedforward term for the expected length of the drive rope, that is, the ideal change in the length of the drive rope obtained from prior knowledge; A(t) and C(t), as mentioned above, are feedforward terms related to the kinematic model of the human-machine coupler; B(t) is a feedforward term related to the stiffness model of the human-machine coupler.

6. The feedforward-admittance control method for a rope-driven exoskeleton according to claim 1, characterized in that, The iterative learning strategy is used to estimate the critical length of the drive rope, where critical refers to the state between tension and slack. Here, P represents the total length of the drive rope, which is the sum of its exposed length and its length within the sleeve. The estimation process is as follows: in It is the total length of the drive rope in the critical state; the superscript m indicates the m-th gait cycle; t SMF This indicates the moment when the tension in the driving rope is at its maximum during the swing phase of the human lower limbs; r is the equivalent auxiliary lever arm; P mea F represents the total length of the drive rope as actually measured; mea K represents the actual measured tension in the drive rope. coupled This refers to the stiffness of the human-machine coupling, which is determined by the estimated exoskeleton stiffness K. exo With human joint stiffness K joint The results were obtained through series calculations; finally, since the length of the drive rope inside the casing is a fixed value, let's assume it's D, then through calculations... We obtain the critical length of the exposed drive rope, C(t), in the m-th gait cycle.

7. The feedforward admittance control method for a rope-driven exoskeleton according to claim 1, characterized in that, The admittance control, and the change in drive rope length obtained from the admittance control with respect to the desired tension of the current drive rope, are expressed as follows: Where ΔP is the change in length of the driving rope; F des This represents the desired assist value; M, B, and K are the inertia, damping, and stiffness parameters for inductance control, respectively, used to determine the servo compliance of the control process; s represents the complex domain; F mea This indicates the actual measured tension in the drive rope.

8. The feedforward admittance control method for a rope-driven exoskeleton according to claim 1, characterized in that, The process of converting the position of the drive rope into a velocity using proportional-integral-damping control is as follows: Where V cmd It is the converted velocity quantity; K p K i K d These are the coefficients for proportional, integral, and damping, respectively; P mea This indicates the total length of the drive rope that was actually measured.

9. A feedforward admittance control method for a rope-driven exoskeleton according to claim 1, characterized in that, The method uses an inertial unit to collect changes in joint angles, a tension sensor to collect tension at the end of the rope, and a motor encoder to collect changes in the length of the drive rope.

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