CN114750152A - Volunteer compliance auxiliary control method for variable-stiffness exoskeleton - Google Patents
Volunteer compliance auxiliary control method for variable-stiffness exoskeleton Download PDFInfo
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- 210000003127 knee Anatomy 0.000 claims description 4
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- 238000002567 electromyography Methods 0.000 claims description 3
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- 210000003141 lower extremity Anatomy 0.000 description 3
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Abstract
The invention discloses a voluntary compliance auxiliary control method for a variable-stiffness exoskeleton, which comprises the following steps: 1) firstly, substituting a preprocessed surface electromyogram signal into a musculoskeletal model to calculate to obtain the estimated torque and rigidity of a human joint; 2) setting an assist torque and stiffness of the exoskeleton according to the estimated human joint torque and stiffness; 3) the torque and rigidity control of the exoskeleton joints is realized by utilizing the elastic characteristic of the variable-rigidity joints. The control method not only can effectively control the auxiliary torque of the exoskeleton according to the volunteers of the user, but also can adjust the rigidity in real time according to the movement behaviors of the user; the torque assistance method does not need to set a reference track and an additional torque sensor, and meets the assistance requirements of users in different motion modes; the variable stiffness control can improve the adaptability of the exoskeleton to the environment and improve the interaction flexibility of the exoskeleton and a user.
Description
Technical Field
The invention relates to the field of robots, in particular to a volunteer flexible auxiliary control method for a variable-rigidity exoskeleton.
Background
China already enters an aging society, and the population of lower limb movement dysfunction caused by age diseases is increasing. Lower limb motor dysfunction has severely affected their daily lives and placed a tremendous economic and mental burden on their families. Exoskeletal robots are an exciting potential solution that can help motor-handicapped patients resume their daily activities.
The traditional exoskeleton robot is usually driven by a motor with a high reduction ratio in cooperation with a speed reducer, so that accurate position control can be realized. However, high back drive impedances are often caused by high deceleration variations, making it difficult for the user to back drive the exoskeleton robot. Especially in case of sensor failure, it is difficult to ensure user safety. The variable-stiffness exoskeleton researched at present can adapt to complex tasks due to the variable-stiffness characteristic of the exoskeleton, and is favored due to high interactive flexibility and safety.
Meanwhile, conventional exoskeleton control generally employs precise trajectory control, impedance control based on a reference trajectory, and admittance control, which have proven effective in exoskeleton rehabilitation training. However, during the daily walking of the user, the movement track of the user is not fixed, and this preset track type control strategy is not suitable for assisting the daily life of the user. In addition, because the variable stiffness characteristics of the variable stiffness exoskeleton make control more complex, how to effectively improve the performance of the exoskeleton by combining the variable stiffness characteristics of the exoskeleton is also a difficult point in current research. Therefore, further development of voluntary compliance assistance control methods for variable stiffness exoskeletons is needed to achieve daily living assistance for patients with lower limb motor dysfunction.
In the chinese patent application No. 201910574269.3, an exoskeleton coordinated gait control method based on human gait motion coordination characteristics is disclosed, which judges the gait phase of a user by detecting the joint angles and angular velocities of hip joints and knee joints, constructs an ideal knee joint and hip joint coordinated trajectory curve in the swing phase, and allows an exoskeleton to track the ideal curve trajectory by a PD controller to coordinate the gait of the user. But the exoskeleton adopts a fixed preset track and is only suitable for a specific walking scene of a user.
In the chinese patent application No. 201510502174.2, a reduced-order single-joint power-assisted exoskeleton adaptive robust cascade force control method is disclosed, which adopts a cascade force control method, an upper layer controller generates a reference trajectory of an exoskeleton joint by measuring an interaction force sensor, and a lower layer controller realizes tracking of the reference trajectory. The reference track of the exoskeleton can be changed in real time according to the movement of the user, but a certain delay still exists between the signal measured by the interaction force sensor and the movement intention of the user, and additional interaction force exists between the exoskeleton and the user.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a flexible auxiliary control method for a variable-stiffness exoskeleton aiming at the defects of the background technology, wherein the exoskeleton can actively provide torque assistance according to the volunteers of users, adjust the stiffness of joints in real time and realize exercise assistance for daily life of the users.
The invention adopts the following technical scheme for solving the technical problems:
a voluntary compliance assistance control method for a variable stiffness exoskeleton, comprising the steps of:
step S1, acquiring and executing a preset control program;
step S2, acquiring surface electromyographic signals of the medial femoral muscle, the lateral femoral muscle, the semimembraneous muscle and the semitendinous muscle of the user, filtering, substituting the processed electromyographic signals into a musculoskeletal model, and calculating to obtain the torque and the rigidity of the joint of the user;
step S3, generating the auxiliary torque and rigidity of the exoskeleton of the joint according to the torque and rigidity of the joint of the user;
step S4, adopting the estimated active participation degree of the user to self-adaptively adjust the reference rigidity of the exoskeleton of the joint according to the set exoskeleton auxiliary torque of the joint;
step S5, tracking and controlling the auxiliary torque of the exoskeleton of the joint by a torque controller without a torque sensor according to the set exoskeleton reference track of the joint; according to the set reference rigidity of the exoskeleton of the joint, the closed-loop PID control is adopted to realize the tracking control of the exoskeleton rigidity of the joint;
step S6, repeating the steps S2-S5 until the auxiliary task is completed, adjusting the auxiliary torque of the exoskeleton in real time, and enabling the auxiliary torque of the exoskeleton to be consistent with the direction of the torque required by the joints of the user, so that the exercise assistance of the daily life of the user is realized; and adjusting the rigidity of the exoskeleton of the joint in real time to ensure the optimal rigidity of the exoskeleton of the joint, so that the adaptability of the exoskeleton to the environment is improved.
Preferably, the method for estimating the joint torque and stiffness of the user according to the user surface myoelectric signal in step S2 specifically includes:
establishing a musculoskeletal model of a human joint:
wherein ,Fi mtIs muscle tendon force, eta is the angle between muscle and bone, Fi aIs an active muscular force, Fi pIs a passive muscle force;
the active and passive muscle forces are:
wherein ,fa(li) and fp(li) Is a function related to muscle length,/iIs normalized muscle length, viIs normalizing the muscleMeat speed, fv(vi) Is a function related to the normalized muscle velocity, λi(t) is the processed surface electromyography signal,
is the maximum muscle force;
muscle to joint torque:
wherein ,
is the torque of the ith muscle to the joint,
is the muscle force of the ith muscle, riIs the lever arm of the ith muscle;
synthesizing joint torque:
wherein the subscript Extensor represents the torque of the Extensor, Flexor represents the torque of the Flexor, σ1Expressing the proportional coefficient, σ, of the extensor muscle2A scale factor representing the flexor;
calculating joint stiffness:
wherein k1 and k2Is a constant;
preferably, the exoskeleton assistance torque and stiffness method for generating the joints in step S3 is as follows:
assist torque of the joint exoskeleton:
Tref＝ωτjoint (7)
wherein ω is an auxiliary proportionality coefficient;
stiffness of the articular exoskeleton:
wherein ,Kθ(Kjoint) Is the set stiffness of the exoskeleton knee of the joint, Kθ，minIs the minimum stiffness, K, of the exoskeleton of the jointθ，maxIs the maximum stiffness of the exoskeleton of the joint,
is the maximum human joint stiffness in walking, KjointIs the human joint stiffness estimated in real time.
Preferably, in step S4, the torque-sensorless torque control and stiffness control method for the exoskeleton includes:
in the torque-sensorless torque control method, the feedback torque is estimated from the exoskeleton stiffness and elastic deflection angle:
Tes＝Kθθ (9)
wherein θ is the elastic deflection angle of the exoskeleton of the joint due to the external load;
the torque control method of the torque-free sensor adopts closed-loop PID control, the motor works in a speed mode, and the control input is as follows:
wherein ,e1(t) error of reference torque and estimated torque, Kp1，Ki1 and Kd1Is a parameter of the PID controller.
Preferably, the exoskeleton stiffness control method adopts PID closed-loop control, the motor works in a speed mode, and the control input is as follows:
wherein ,e2(t) error of reference and estimated stiffness, Kp2，Ki2 and Kd2Is a parameter of the PID controller.
Compared with the prior art, the invention adopting the technical scheme has the following beneficial effects:
1. compared with the traditional motion intention recognition algorithm based on the surface electromyographic signals, the voluntary compliance auxiliary control method for the variable-stiffness exoskeleton provided by the invention has the advantages that the torque and the rigidity of the joints of a human body can be accurately estimated by using the surface electromyographic signals of the muscles related to the joints, all the muscles do not need to be collected, and the algorithm is easier to realize.
2. The volunteer compliance auxiliary control method for the variable-rigidity exoskeleton can be used for directly performing torque assistance according to the movement intention of a user, does not need to set references and tracks, and can be used for movement assistance in variable movement scenes.
3. According to the voluntary compliance auxiliary control method for the variable-stiffness exoskeleton, the elastic characteristics of the exoskeleton joints are used, the auxiliary torque of the exoskeleton is directly estimated, the torque control without a torque sensor is realized, and the cost of the exoskeleton and the size of the required exoskeleton can be reduced.
4. The volunteer compliance auxiliary control method for the variable-stiffness exoskeleton can directly control the stiffness of the exoskeleton in real time, can simulate variable-stiffness behaviors of human joints when being used for adapting to environments and tasks, and improves the adaptability of the exoskeleton to the environments.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of a musculoskeletal model based on surface electromyographic signal driving according to the invention;
FIG. 2 is a torque control block diagram of the torque sensor-less of the present invention;
FIG. 3 is a block diagram of voluntary compliance assist control for the variable stiffness exoskeleton of the present invention;
FIG. 4 is a flow chart of a voluntary compliance assistance control method for the variable stiffness exoskeleton of the present invention;
FIG. 5 is a graph of torque estimation results based on a musculoskeletal model in an embodiment of the present invention;
fig. 6 is a graph showing the result of torque control without a torque sensor in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 4, a voluntary compliance auxiliary control method for a variable stiffness exoskeleton, taking a knee joint as an example, comprises the following steps:
step S1, acquiring and executing a preset control program;
step S2, acquiring surface electromyographic signals of the medial femoral muscle, the lateral femoral muscle, the semimembraneous muscle and the semitendinous muscle of the user, filtering, substituting the processed electromyographic signals into a musculoskeletal model, and calculating to obtain the torque and the rigidity of the knee joint of the user;
step S3, generating auxiliary torque and rigidity of the exoskeleton knee joint according to the torque and rigidity of the user knee joint;
step S4, adjusting the reference rigidity of the exoskeleton knee joint in a self-adaptive manner by adopting the estimated active participation degree of the user according to the set exoskeleton knee joint auxiliary torque;
step S5, tracking and controlling the auxiliary torque of the exoskeleton knee joint by a torque controller without a torque sensor according to the set exoskeleton knee joint reference track; according to the set reference rigidity of the exoskeleton knee joint, closed-loop PID control is adopted to realize tracking control of the exoskeleton knee joint rigidity;
step S6, repeating the steps S2-S5 until the auxiliary task is completed, adjusting the auxiliary torque of the exoskeleton in real time, and enabling the auxiliary torque of the exoskeleton to be consistent with the direction of the required torque of the knee joint of the user, so that the exercise assistance of the daily life of the user is realized; the rigidity of the exoskeleton knee joint is adjusted in real time to ensure the optimal rigidity of the exoskeleton knee joint, so that the adaptability of the exoskeleton to the environment is improved;
the invention relates to a volunteer compliance control method for a variable-rigidity exoskeleton, which mainly comprises a human body joint torque and rigidity estimation algorithm, an exoskeleton joint auxiliary torque and rigidity generation strategy, a sensorless torque control algorithm and exoskeleton joint rigidity closed-loop control which are driven by a surface electromyogram signal and a musculoskeletal model, wherein the method comprises the following steps:
the human body joint torque and stiffness estimation algorithm based on the surface electromyogram signal and the driving of the musculoskeletal model is shown in fig. 1, taking the knee joint of the auxiliary user as an example:
firstly, surface electromyographic signals of the vastus lateralis, vastus medialis, semimembrana muscle and semitendinosus of a user are collected, and the collected surface electromyographic signals are filtered. The filtering order is: firstly, filtering an original surface electromyogram signal by a 10Hz-500Hz band-pass filter, then filtering by a 410Hz high-pass filter, then performing full-wave sorting, filtering by a 50 notch filter, and finally filtering by a 1Hz low-pass filter to obtain a filtered surface electromyogram signal,
the filtered surface electromyographic signals are subjected to nonlinear mapping to obtain muscle activation:
λi(t)＝(eξEi(t)-1)/(eξ-1) (a) wherein Ei(t) represents the filtered surface electromyographic signal, ξ is a nonlinear mapping factor, λi(t) is activation of the muscle;
substituting the activation of the muscle into a musculoskeletal model to calculate torque and stiffness of a human joint, the musculoskeletal model:
wherein ,Fi mtIs muscle tendon force, eta is the angle between muscle and bone, Fi aIs an active muscular force, Fi pIs a passive muscle force;
the active and passive muscle forces are:
wherein ,fa(li) and fp(li) Is a function related to the length of the muscle, liIs normalized muscle length, viIs the normalized muscle velocity, fv(vi) Is a function related to the normalized muscle velocity, λi(t) is the processed surface electromyography signal, Fi maxIs the maximum muscle force;
muscle length and human joint angle
Can be expressed by a cubic polynomial:
wherein 
Is the length of the muscle, c0i、c1i、c2i、c3iIs a coefficient of a polynomial;
the normalized muscle length and the muscle-bone angle η can be expressed as:
η＝arcsin(sin(η0)/li) (d)
wherein is
Optimum muscle length, eta0Is the optimal musculoskeletal angle;
function f relating to muscle lengtha(li) and fp(li) Can be expressed as:
a, B, C, alpha1、α2、β1、β2Is a constant;
the muscle-to-joint torque is expressed as:
wherein ,
is the torque of the ith muscle to the joint, Fi mtIs the muscle force of the ith muscle, riIs the lever arm of the ith muscle;
the lever arm can be calculated as:
synthesizing joint torque:
wherein the subscript Extensor represents the torque of the Extensor, Flexor represents the torque of the Flexor, σ1Expressing the proportional coefficient, σ, of the extensor muscle1A scale factor representing the flexor;
calculating joint stiffness:
wherein k1 and k2Is a constant;
the exoskeleton joint auxiliary torque and rigidity generation strategy specifically comprises the following steps:
assisting torque of exoskeleton knee joint:
Tref＝ωτjoint (7)
where ω is the auxiliary proportionality coefficient;
rigidity of exoskeleton knee joint:
wherein ,Kθ(Kjoint) Is the set stiffness of the exoskeleton knee joint, Kθ，minIs the minimum stiffness, K, of the exoskeleton jointsθ，maxIs the maximum stiffness of the exoskeleton joints,
is the maximum human knee stiffness in walking, KjointIs the human knee stiffness estimated in real time.
The sensorless torque control algorithm, as shown in fig. 2, specifically includes:
according to the elastic deflection angle theta and the rigidity K of the exoskeleton jointθEstimating an actual assistance torque T of an exoskeleton jointes：
Tes＝Kθθ (9)
The estimated exoskeleton joint torque is used as the feedback torque of the torque controller, the torque of the exoskeleton joint is controlled in a closed loop mode through the PID controller, and the driving motor works in a speed mode, wherein a speed control signal can be represented as:
wherein ,e1(t) error of reference torque and estimated torque, Kp1，Ki1 and Kd1Is a parameter of the PID controller;
the exoskeleton joint rigidity closed-loop control method comprises the following specific steps of:
estimating the rigidity of the exoskeleton joint according to the characteristics of the torque T and the elastic deflection angle theta of the exoskeleton joint, wherein the specific method comprises the following steps:
the estimated exoskeleton joint stiffness is used as the feedback stiffness of the torque controller, the stiffness of the exoskeleton joint is subjected to closed-loop control through the PID controller, the motor works in a speed mode, and the control input is as follows:
wherein ,e2(t) error of reference stiffness and estimated stiffness, Kp2，Ki2 and Kd2Is a parameter of the PID controller.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. A voluntary compliance auxiliary control method for a variable stiffness exoskeleton, comprising the steps of:
step S1, acquiring and executing a preset control program;
step S2, acquiring surface electromyographic signals of the medial femoral muscle, the lateral femoral muscle, the semimembraneous muscle and the semitendinous muscle of the user, filtering, substituting the processed electromyographic signals into a musculoskeletal model, and calculating to obtain the torque and the rigidity of the joint of the user;
step S3, generating the auxiliary torque and rigidity of the exoskeleton of the joint according to the torque and rigidity of the joint of the user;
step S4, adopting the estimated active participation degree of the user to self-adaptively adjust the reference rigidity of the exoskeleton of the joint according to the set exoskeleton auxiliary torque of the joint;
step S5, tracking and controlling the auxiliary torque of the exoskeleton of the joint by a torque controller without a torque sensor according to the set exoskeleton reference track of the joint; according to the set reference rigidity of the exoskeleton of the joint, the closed-loop PID control is adopted to realize the tracking control of the exoskeleton rigidity of the joint;
step S6, repeating the steps S2-S5 until the auxiliary task is completed, adjusting the auxiliary torque of the exoskeleton in real time, and enabling the auxiliary torque of the exoskeleton to be consistent with the direction of the torque required by the joints of the user, so that the exercise assistance of the daily life of the user is realized; the rigidity of the exoskeleton of the joints is adjusted in real time to ensure the optimal rigidity of the exoskeleton of the joints, so that the adaptability of the exoskeleton to the environment is improved.
2. The voluntary compliance assistance control method for a variable stiffness exoskeleton of claim 1, wherein the method for estimating the torque and stiffness of the joint of the user from the user surface myoelectric signal in step S2 is specifically:
establishing a musculoskeletal model of a human joint:
wherein ,Fi mtIs muscle tendon force, eta is the angle between muscle and bone, Fi aIs an active muscular force, Fi pIs a passive muscle force;
the active and passive muscle forces are:
wherein ,fa(li) and fp(li) Is a function related to muscle length,/iIs normalized muscle length, viIs the normalized muscle velocity, fv(vi) Is a function related to the normalized muscle velocity, λi(t) is the processed surface electromyography signal,
is the maximum muscle force;
muscle to joint torque:
wherein ,
is the torque of the ith muscle to the joint,
is the muscle force of the ith muscle, riIs the lever arm of the ith muscle;
synthesizing joint torque:
wherein the subscript Extensor represents the torque of the Extensor, Flexor represents the torque of the Flexor, σ1Expressing the proportional coefficient, σ, of the extensor muscle2A scale factor representing the flexor;
calculating joint stiffness:
wherein k1 and k2Is a constant.
3. The method of voluntary compliance assistance control for a variable stiffness exoskeleton of claim 1, wherein the method of generating exoskeleton assistance torque and stiffness for the joints in step S3 is:
assistance torque of the joint exoskeleton:
Tref＝ωτjoint (7)
where ω is the auxiliary proportionality coefficient;
stiffness of the articular exoskeleton:
wherein ,Kθ(Kjoint) Is the set stiffness, K, of the exoskeleton knee of the jointθ，minIs the minimum stiffness, K, of the exoskeleton of the jointθ，maxIs the maximum stiffness of the exoskeleton of the joint,
is the maximum human joint stiffness in walking, KjointIs the human joint stiffness estimated in real time.
4. The voluntary compliance assistance control method for a variable stiffness exoskeleton of claim 1, wherein in step S4, the torque sensor-less torque control and stiffness control method for the exoskeleton is:
in the torque sensorless torque control method, the feedback torque is estimated from the exoskeleton stiffness and the elastic deflection angle:
Tes＝Kθθ (9)
wherein θ is the elastic deflection angle of the exoskeleton of the joint due to the external load;
the torque control method without the torque sensor adopts closed-loop PID control, the motor works in a speed mode, and the control input is as follows:
wherein ,e1(t) error of reference torque and estimated torque, Kp1，Ki1 and Kd1Is a parameter of the PID controller.
5. The voluntary compliance assistance control method for a variable stiffness exoskeleton of claim 1, wherein the exoskeleton stiffness control method employs PID closed loop control, the motor operates in speed mode, and the control inputs are:
wherein ,e2(t) error of reference stiffness and estimated stiffness, Kp2，Ki2 and Kd2Is a parameter of the PID controller.
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