阅读说明：本技术 一种膝-踝-趾动力型下肢假肢的解耦控制方法 (Decoupling control method for knee-ankle-toe power type lower limb prosthesis ) 是由 耿艳利 武正恩 王希瑞 王倩 陈玲玲 宣伯凯 于 2021-05-31 设计创作，主要内容包括：本发明公开了一种膝-踝-趾动力型下肢假肢的解耦控制方法,属于下肢假肢技术领域,采用膝踝趾下肢假肢模型为被控对象,建立不同步态阶段的下肢假肢模型,设计基于控制法则分解的解耦方法对膝踝趾下肢假肢关节进行解耦,使用自适应迭代控制策略对解耦后的假肢模型进行控制,本发明确定了膝踝趾下肢假肢模型的解耦控制律和解耦系统的结构,采用逆系统反馈的形式,将原系统的各阶输出或状态向量反馈,重构为具有标准积分形式的线性系统,将原控制对象内部存在的耦合关系通过内部逆的形式抵消,实现解耦。解决了下肢假肢关节间的交连耦合影响,提高了三关节系统的可控性和稳定性,有效降低了被控对象的复杂程度,缩短了控制时间,达到了快速收敛要求。(The invention discloses a decoupling control method of a knee-ankle-toe power type lower limb prosthesis, which belongs to the technical field of lower limb prostheses, adopts a knee-ankle-toe lower limb prosthesis model as a controlled object, establishes a lower limb prosthesis model in different dynamic stages, designs a decoupling method based on control rule decomposition to decouple a knee-ankle-toe lower limb prosthesis joint, and controls the decoupled prosthesis model by using a self-adaptive iterative control strategy. The cross coupling influence among the lower limb artificial limb joints is solved, the controllability and the stability of a three-joint system are improved, the complexity of a controlled object is effectively reduced, the control time is shortened, and the requirement of rapid convergence is met.)

1. A decoupling control method of a knee-ankle-toe dynamic type lower limb prosthesis is characterized by comprising the following steps: the method comprises the following steps:

step 1, collecting angle information of each joint of a lower limb by using a VICON MX three-dimensional gait analysis system, directly calculating the hip-knee-ankle joint angle by using a model, creating a lower limb experimental model, and obtaining a relative position diagram of each mark point of a foot, wherein the joint angle can be obtained by calculating position information of three points, namely HEE, TOE and TT;

step 2, carrying out gait division according to the walking mode of the knee-ankle-toe power type lower limb artificial limb;

step 3, establishing different lower limb artificial limb models at different stages, and decoupling the knee, ankle and toe lower limb artificial limb joints by using a decoupling method based on control rule decomposition;

step 4, designing a lower limb prosthesis controller, designing a self-adaptive iterative learning control strategy by taking the decoupled prosthesis dynamic model as a controlled object, and realizing the rapid tracking of the knee-ankle-toe three-joint angle of the lower limb prosthesis;

step 5, establishing an MATLAB model of a control system platform;

and 6, setting MATLAB model parameters and carrying out simulation analysis to obtain angle tracking and tracking errors of the three knee-ankle-toe joints after decoupling, and observing error convergence conditions when the tracking errors of the three joints reach control precision to obtain a decoupled three-joint angle error convergence diagram.

2. The decoupled control method of a knee-ankle-toe powered lower limb prosthesis of claim 1, wherein: in step one, calculating the relative displacement x of the HEE from the positions of the three markers, and knowing the distance l from the HEE to the TOE, then:

3. the decoupled control method of a knee-ankle-toe powered lower limb prosthesis of claim 2, wherein: in step 2, the gait cycle of the lower limb movement is divided into two stages by analyzing the angle change rule of each joint of the leg and the stress point of the foot: a support period and a swing period, and modeling and controlling are respectively carried out aiming at different stages.

4. The decoupled control of a knee-ankle-toe powered lower limb prosthesis of claim 3, wherein: in the third step 3, in the swing period, the sole is not in contact with the ground, and the hip joint can be regarded as a fixed point to establish a kinetic equation.

Wherein M is₁Is a joint space inertia matrix, H₁Is a coupling matrix of coriolis forces and centripetal forces, G₁Is a gravity matrix, T₁Is a hip joint and knee joint driving moment matrix. Theta₁＝[θ_hip,θ_knee]^T，τ₁＝[τ_hip,τ_knee]^T，τ_hipFor moment of hip joint, τ_kneeThe moment of the knee joint is the moment of the knee joint,

according to the stress condition of the toe joint in the support period stage, the toe joint can be divided into a support early-middle stage and a support final stage,

in the stage of supporting the forepart and the metaphase, the motion of the human body can be regarded as rotating around the ankle joint, the ankle joint is taken as a fixed point, and a kinetic equation is established:

wherein, tau₂Is the knee joint and ankle joint drive moment matrix, theta₂＝[θ_knee,θ_ankle]^T，τ₂＝[τ_knee,τ_ankle]^T，τ_ankleThe moment of the ankle joint;

at the end of support, the motion of the human body can be regarded as rotating around the toe joint, the toe joint is taken as an origin, and a dynamic model is established:

wherein, tau₃Is the knee joint and ankle joint drive moment matrix, theta₃＝[θ_knee,θ_ankle,θ_toe]^T，τ₃＝[τ_knee,τ_ankle,τ_toe]^T，τ_toeIn order to obtain the moment of the toe joint,

the decoupling is divided into two steps: firstly, determining the sufficient condition that the system can be decoupled, namely the decoupling judgment problem, and then determining the decoupling control law and the structure of the decoupling system, namely the specific comprehensive problem of the decoupling system. The inverse system method adopts a feedback form to feed back each order output or state vector of the original system and reconstruct the linear system with a standard integral form, and counteracts the coupling relation existing in the original control object through an internal inverse form to realize decoupling,

starting from multi-body dynamics, selecting a control rule decomposition-based method to decouple the coupling between joints, and selecting an object correction part in the control law of f ═ alpha u + beta as:

error servo partial fixed fetchWhere α and β are inverse models for simplifying the control of nonlinear objects, x_dFor intermediate variables, e is the error between the expected and actual output of the system, so that the end of the support can be obtainedThe effect of the decoupling of the lower limb prosthesis model of the knee-ankle-toe three-joint in the period is three-order diagonal matrix Y, namely, the moment of each joint is only related to the motion angle of the joint.

5. The decoupled control of a knee-ankle-toe powered lower limb prosthesis of claim 4, wherein: in step 4, at the kth iteration of the controlled system, the system model is as follows:

in the formula (6) < theta >_k(t)∈R³，For joint angular displacement, angular velocity and angular acceleration,

defining an angular displacement error as e_k(t) expressed as a desired trajectory θ_d(t) tracking angle with kth iterationIs calculated as follows:

e_k(t)＝θ_d(t)-θ_k(t) (7)

similarly, the angular velocity error is

The adaptive iterative learning controller is designed as follows:

k in formula (8)_P∈R^3×3，K_D∈R^3×3For iterative learning controlled PID gain matrix, Γ is adaptive parameter, v_k(t) is an adaptive term that is,

desired trajectory θ_d(t) the actual tracking angular displacement theta of the lower limb prosthesis can be obtained by measuring the angular displacements of the knee joint, the hip joint and the toe joint through experiments and applying the output moment of the control system to the decoupled lower limb prosthesis dynamic model_k(t)。

6. The decoupled control of a knee-ankle-toe powered lower limb prosthesis of claim 5, wherein: in step 5, a decoupling controller is established under MATLAB/Simulink based on lower limb prosthesis decoupling control of adaptive iterative learning, and the decoupling controller comprises the following parts, namely an adaptive iterative learning controller, a decoupler, a knee ankle and toe lower limb prosthesis model, an expected trajectory input module, a tracking trajectory output module and a control torque module.

7. The decoupled control of a knee-ankle-toe powered lower limb prosthesis of claim 6, wherein: in step 6, parameters of the controller and parameters of the lower limb prosthesis model are set, and the parameters of the controller have PID gain K of iterative learning control_P，K_DAdaptive parameters gamma and control precision gamma; the parameters of the lower limb prosthesis include the total amount and length of the prosthetic socket, the lower leg, the rear sole and the front sole.

Technical Field

The invention belongs to the technical field of lower limb artificial limbs, and particularly relates to a decoupling control method of a knee-ankle-toe power type lower limb artificial limb.

Background

According to the second national disabled people sampling survey, 2412 thousands of people with physical disabilities in China account for 29.07 percent of the total number of the disabled people, 158 thousands of people with lower limb amputees account for about 70 percent of the total number of the amputees, and the number of the lower limb amputees is continuously increased in recent years.

The artificial limb is an important auxiliary tool for amputees, and is manufactured and assembled in order to compensate the limb defects of the amputees and compensate the functions of the limbs. The lower limb artificial limb is an important means for recovering the walking function of the lower limb amputee, and can basically replace the missing function of the human body, so that the lower limb amputee can take care of the life of the lower limb amputee and even can normally participate in some simple social work. The study on the lower limb prosthesis capable of simulating the swing of the lower limb of a normal person needs to apply knowledge in various aspects such as mathematical modeling, motion detection, motion control and the like, and relates to multiple disciplines such as mechatronics, robotics, automation technology, intelligent materials and structures. The research of the lower limb prosthesis not only can solve the production and living problems of amputees, but also can be applied to bionic robots, engineering construction, transportation and intelligent production, and the development of related research in a plurality of subject fields is driven. Therefore, there is an important academic value in developing research on lower limb prostheses capable of simulating normal human walking.

The lower limb prosthesis is a typical multi-input multi-output nonlinear system, one of the joints needs to be controlled, all other joints of the leg need to be locked, and once the joints are unlocked, cross-coupling effects exist among the joints. Thus, during normal motion of the prosthesis, the motion of each joint is affected by the coupling of the other joints. This presents great difficulties in the coordinated control of motion between the various joints of the lower limb prosthesis. Therefore, it is necessary to reduce the cross-coupling effect between the joints of the lower limb prosthesis and to perform decoupling control on each joint of the robot.

At present, intelligent lower limb artificial limb researches are mainly used for researching intelligent knee-ankle joint and ankle-toe joint relevance, and the control method is only limited to fitting angle curves of a normal knee joint and an ankle joint to realize control by applying coordinated control and does not relate to decoupling researches of all joints.

Disclosure of Invention

The invention aims to provide a decoupling control method of a knee-ankle-toe dynamic lower limb prosthesis, which is used for decoupling and controlling three joints of the prosthesis aiming at a knee-ankle-toe lower limb prosthesis structure, can improve the control effect of each joint, reduce the coupling and complexity of a controlled system and shorten the control time so as to meet the requirement of quick convergence.

In order to solve the technical problems, the invention adopts the technical scheme that: a decoupling control method of a knee-ankle-toe power type lower limb prosthesis comprises the following steps:

step 1, collecting angle information of each joint of the lower limb by using a VICON MX three-dimensional gait analysis system,

the hip-knee-ankle joint angle can be directly calculated by the model, because the standard lower limb model only has two mark points on the foot and can not calculate the TOE joint angle, a new lower limb experimental model is created to obtain a relative position diagram of each mark point of the foot, the joint angle can be obtained by calculating the position information of three points of HEE, TOE and TT, the relative displacement x of HEE can be calculated according to the positions of the three mark points, and the distance l from HEE to TOE is known, then:

step 2, carrying out gait division according to the walking mode of the knee-ankle-toe power type lower limb artificial limb,

through analyzing the angle change rule of each joint of the leg and the stress point of the foot, the gait cycle of the lower limb movement is divided into two stages: a support period and a swing period, and modeling and controlling are respectively carried out aiming at different stages.

And 3, establishing different lower limb prosthesis models at different stages, and decoupling the knee, ankle and toe lower limb prosthesis joints by using a decoupling method based on control rule decomposition.

In the swing period, the sole is not contacted with the ground, and the hip joint can be regarded as a fixed point to establish a kinetic equation.

Wherein M is₁Is a joint space inertia matrix, H₁Is a coupling matrix of coriolis forces and centripetal forces, G₁Is a gravity matrix, T₁Is a hip joint and knee joint driving moment matrix. Theta₁＝[θ_hip,θ_knee]^T，τ₁＝[τ_hip,τ_knee]^T，τ_hipFor moment of hip joint, τ_kneeThe moment of the knee joint.

According to the stress condition of the toe joint in the support period stage, the toe joint can be divided into a support early-middle stage and a support final stage.

In the stage of supporting the forepart and the metaphase, the motion of the human body can be regarded as rotating around the ankle joint, the ankle joint is taken as a fixed point, and a kinetic equation is established:

wherein, tau₂Is the knee joint and ankle joint drive moment matrix, theta₂＝[θ_knee,θ_ankle]^T，τ₂＝[τ_knee,τ_ankle]^T，τ_ankleThe moment of the ankle joint.

At the end of support, the motion of the human body can be regarded as rotating around the toe joint, the toe joint is taken as an origin, and a dynamic model is established:

wherein, tau₃Is the knee joint and ankle joint drive moment matrix, theta₃＝[θ_knee,θ_ankle,θ_toe]^T，τ₃＝[τ_knee,τ_ankle,τ_toe]^T，τ_toeIs the toe joint moment.

The decoupling problem of the system is researched and divided into two steps: firstly, determining the sufficient condition that the system can be decoupled, namely the decoupling judgment problem, and then determining the decoupling control law and the structure of the decoupling system, namely the specific comprehensive problem of the decoupling system. The inverse system method adopts a feedback form, each order output or state vector of the original system is fed back and reconstructed into a linear system with a standard integral form, and the coupling relation existing in the original control object is counteracted in an internal inverse form to realize decoupling.

Starting from multi-body dynamics, decoupling coupling between joints by selecting a decomposition method based on a control rule, and selecting an object correction part in the control rule of f ═ alpha u + beta as:

'error servo' partial fixed fetchWherein α and β are 'inverse models' for simplifying the control of the nonlinear object. x is the number of_dFor the intermediate variable, e is the error of the system expected and actual outputs. Therefore, the decoupled effect of the final-stage supporting knee-ankle-toe three-joint lower limb prosthesis model is a third-order diagonal matrix Y, namely, each joint moment is only related to the motion angle of the joint.

And 4, designing a lower limb prosthesis controller, designing a self-adaptive iterative learning control strategy by taking the decoupled prosthesis dynamic model as a controlled object, and realizing the rapid tracking of the knee-ankle-toe three-joint angle of the lower limb prosthesis.

And when the controlled system iterates at the kth time, the system model is as follows:

in the formula (6) < theta >_k(t)∈R³，The angular displacement, angular velocity and angular acceleration of the joint.

Defining an angular displacement error as e_k(t) expressed as a desired trajectory θ_d(t) tracking angle with kth iterationIs calculated as follows:

e_k(t)＝θ_d(t)-θ_k(t) (7)

similarly, the angular velocity error is

The adaptive iterative learning controller is designed as follows:

k in formula (8)_P∈R^3×3，K_D∈R^3×3For iterative learning controlled PID gain matrix, Γ is adaptive parameter, v_k(t) is an adaptation term.

Desired trajectory θ_d(t) the actual tracking angular displacement theta of the lower limb prosthesis can be obtained by measuring the angular displacements of the knee joint, the hip joint and the toe joint through experiments and applying the output moment of the control system to the decoupled lower limb prosthesis dynamic model_k(t)。

And 5, establishing an MATLAB model of the control system platform.

The decoupling control method is characterized in that a decoupling controller is established under MATLAB/Simulink based on the lower limb prosthesis decoupling control of adaptive iterative learning, and comprises the following parts, namely the adaptive iterative learning controller, a decoupler, a knee ankle and toe lower limb prosthesis model, an expected track input module, a tracking track output module and a control torque module.

And 6, setting parameters of the MATLAB model and carrying out simulation analysis.

Setting parameters of a controller and parameters of a lower limb prosthesis model, wherein the parameters of the controller have PID gain K of iterative learning control_P，K_DAdaptive parameters gamma and control precision gamma; the parameters of the lower limb prosthesis include the total amount and length of the prosthetic socket, the lower leg, the rear sole and the front sole.

Through simulation result analysis of the controller, angle tracking and tracking errors of the three knee-ankle-toe joints after decoupling can be obtained. And when the tracking errors of the three joints reach the control precision, observing the error convergence condition to obtain a decoupled three-joint angle error convergence diagram.

Compared with the prior art, the invention has the beneficial effects that:

1. a new lower limb experimental model is created, and toe joint angle information which cannot be obtained in the traditional model can be calculated through the relative positions of all mark points of the foot.

2. The artificial limb decoupler with the knee, ankle and toe joints is designed, a complex three-joint motor model is simplified, the coupling of the system is reduced, and the controlled system is more controllable.

3. According to the characteristic that the lower limb walking is periodically repeated, the self-adaptive iterative learning controller is used, and the control moment meeting the control precision requirement can be obtained more quickly through the iterative process.

Drawings

The advantages and realisation of the invention will be more apparent from the following detailed description, given by way of example, with reference to the accompanying drawings, which are given for the purpose of illustration only, and which are not to be construed in any way as limiting the invention, and in which:

FIG. 1 is a diagram showing the relative positions of the marks on the foot according to the present invention

FIG. 2 is a diagram of the expected angular trajectory of the three ankle and toe joints of the present invention

FIG. 3 is a diagram of a MATLAB/simulink control system model according to the present invention

FIG. 4 is a diagram of knee joint angle tracking according to the present invention

FIG. 5 is an ankle joint angle tracking chart of the present invention

FIG. 6 is a diagram of toe joint angle tracking according to the present invention

FIG. 7 is a diagram of convergence of tracking errors of three knee, ankle and toe joints according to the present invention

Detailed Description

The invention will be further described with reference to the following examples and figures:

as shown in fig. 1 to 7, a decoupling control method of a knee-ankle-toe power type lower limb prosthesis comprises the following steps:

step 1, collecting angle information of each joint of the lower limb by using a VICON MX three-dimensional gait analysis system.

The hip-knee-ankle joint angle can be directly calculated by the model, and because the standard lower limb model only has two mark points on the foot and can not calculate the TOE joint angle, a new lower limb experimental model is created to obtain the relative positions of all the mark points of the foot, and as shown in figure 1, the joint angle can be obtained by calculating the position information of three points of HEE, TOE and TT. From the positions of the three markers, the relative displacement x of the HEE can be calculated, and the distance l from the HEE to the TOE is known, then:

and 2, carrying out gait division according to the walking mode of the knee-ankle-toe power type lower limb artificial limb.

Through analyzing the angle change rule of each joint of the leg and the stress point of the foot, the gait cycle of the lower limb movement is divided into two stages: a support period and a swing period, and modeling and controlling are respectively carried out aiming at different stages.

And 3, establishing different lower limb prosthesis models at different stages, and decoupling the knee, ankle and toe lower limb prosthesis joints by using a decoupling method based on control rule decomposition.

In the swing period, the sole is not contacted with the ground, and the hip joint can be regarded as a fixed point to establish a kinetic equation:

wherein M is₁Is a joint space inertia matrix, H₁Is a coupling matrix of coriolis forces and centripetal forces, G₁Is a gravity matrix, T₁Is a hip joint and knee joint driving moment matrix. Theta₁＝[θ_hip,θ_knee]^T，τ₁＝[τ_hip,τ_knee]^T，τ_hipFor moment of hip joint, τ_kneeThe moment of the knee joint.

According to the stress condition of the toe joint in the support period stage, the toe joint can be divided into a support early-middle stage and a support final stage.

In the stage of supporting the forepart and the metaphase, the motion of the human body can be regarded as rotating around the ankle joint, the ankle joint is taken as a fixed point, and a kinetic equation is established:

wherein, tau₂Is the knee joint and ankle joint drive moment matrix, theta₂＝[θ_knee,θ_ankle]^T，τ₂＝[τ_knee,τ_ankle]^T，τ_ankleThe moment of the ankle joint.

At the end of support, the motion of the human body can be regarded as rotating around the toe joint, the toe joint is taken as an origin, and a dynamic model is established:

wherein, tau₃Is the knee joint and ankle joint drive moment matrix, theta₃＝[θ_knee,θ_ankle,θ_toe]^T，τ₃＝[τ_knee,τ_ankle,τ_toe]^T，τ_toeIs the toe joint moment.

The decoupling problem of the system is researched and divided into two steps: firstly, determining the sufficient condition that the system can be decoupled, namely the decoupling judgment problem, and then determining the decoupling control law and the structure of the decoupling system, namely the specific comprehensive problem of the decoupling system. The inverse system method adopts a feedback form, each order output or state vector of the original system is fed back and reconstructed into a linear system with a standard integral form, and the coupling relation existing in the original control object is counteracted in an internal inverse form to realize decoupling.

Starting from multi-body dynamics, decoupling coupling between joints by selecting a decomposition method based on a control rule, and selecting an object correction part in the control rule of f ═ alpha u + beta as:

'error servo' partial fixed fetchWherein α and β are 'inverse models' for simplifying the control of the nonlinear object. x is the number of_dFor the intermediate variable, e is the error of the system expected and actual outputs. Therefore, the decoupled effect of the final-stage supporting knee-ankle-toe three-joint lower limb prosthesis model is a third-order diagonal matrix Y, namely, each joint moment is only related to the motion angle of the joint.

And 4, designing a lower limb prosthesis controller, designing a self-adaptive iterative learning control strategy by taking the decoupled prosthesis dynamic model as a controlled object, and realizing the rapid tracking of the knee-ankle-toe three-joint angle of the lower limb prosthesis.

And when the controlled system iterates at the kth time, the system model is as follows:

in the formula (6) < theta >_k(t)∈R³，The angular displacement, angular velocity and angular acceleration of the joint.

Defining an angular displacement error as e_k(t) expressed as a desired trajectory θ_d(t) tracking angle with kth iterationIs calculated as follows:

e_k(t)＝θ_d(t)-θ_k(t) (7)

similarly, the angular velocity error is

The adaptive iterative learning controller is designed as follows:

k in formula (8)_P∈R^3×3，K_D∈R^3×3For iterative learning controlled PID gain matrix, Γ is adaptive parameter, v_k(t) is an adaptation term.

Desired trajectory θ_d(t) is the angular displacement of the knee, hip and toe joints measured by experiments, and as shown in fig. 2, the actual tracking angular displacement θ of the lower limb prosthesis can be obtained by applying the output torque of the control system to the decoupled lower limb prosthesis dynamics model_k(t)。

And 5, establishing an MATLAB model of the control system platform.

The decoupling control method is characterized in that a decoupling controller is established under MATLAB/Simulink based on the lower limb prosthesis decoupling control of adaptive iterative learning, and comprises the following parts, namely the adaptive iterative learning controller, a decoupler, a knee ankle and toe lower limb prosthesis model, an expected track input module, a tracking track output module and a control torque module.

As shown in FIG. 3, the decoupler is decoupled by a control law decomposition method, the knee ankle and toe lower limb prosthesis model is a three-joint dynamic model, and the expected trajectory input module is the angular displacement theta of three joints of the knee ankle and toe_d(t) tracking angular displacement θ of the output of the trajectory module for the prosthesis model_k(t) controlling the torque module to output tau for the controller meeting the control accuracy requirement_k(t)。

And 6, setting parameters of the MATLAB model and carrying out simulation analysis.

Setting parameters of a controller and parameters of a lower limb prosthesis model, wherein the parameters of the controller comprise PID gain of iterative learning controlAnd the adaptive parameter Γ is 10 and the accuracy γ of the control is 0.15 °; the parameters of the lower artificial limb comprise the length l of an artificial limb receiving cavity₁0.42m, mass m₁2.14 kg; length of lower leg l₂0.39m, mass m₂1.84 kg; length of rear sole₃0.14m, mass m₃0.51 kg; front sole length l₄0.07m, mass m₄＝0.25kg。

Through simulation result analysis of the controller, angle tracking and tracking errors of the three knee-ankle-toe joints after decoupling can be obtained.

As shown in fig. 4 to 6, angle tracking maps of knee, ankle and toe joints, respectively, and a tracking error convergence map of three joints, as shown in fig. 7. It can be obviously seen that when the angles of the three joints are tracked, the tracking track is always kept near the expected track, the maximum value of the mean square error of the first iteration is 4.3 degrees, and the tracking error can be kept within 0.15 degrees after several iterations, so that the decoupling control of the knee, ankle and toe lower limb artificial limb is realized, and the effects of stable tracking and quick tracking are realized by using the method.

The embodiments of the present invention have been described in detail, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. The implementation of the steps can be changed, and all equivalent changes and modifications made within the scope of the present invention should be covered by the present patent.