CN111360815B - Human-computer interaction motion control method based on electromyographic signals and joint stress - Google Patents
Human-computer interaction motion control method based on electromyographic signals and joint stress Download PDFInfo
- Publication number
- CN111360815B CN111360815B CN201811599328.4A CN201811599328A CN111360815B CN 111360815 B CN111360815 B CN 111360815B CN 201811599328 A CN201811599328 A CN 201811599328A CN 111360815 B CN111360815 B CN 111360815B
- Authority
- CN
- China
- Prior art keywords
- leg
- exoskeleton robot
- joint
- exoskeleton
- ankle joint
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/1607—Calculation of inertia, jacobian matrixes and inverses
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/0006—Exoskeletons, i.e. resembling a human figure
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/1638—Programme controls characterised by the control loop compensation for arm bending/inertia, pay load weight/inertia
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/1651—Programme controls characterised by the control loop acceleration, rate control
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Automation & Control Theory (AREA)
- Manipulator (AREA)
Abstract
The embodiment of the invention discloses a man-machine interaction control method based on electromyographic signals and joint stress. The control method comprises the following steps: establishing a leg kinematics model of an exoskeleton robot, the legs including a first leg and a second leg; designing motion controller models of the exoskeleton robots in different stages on the basis of the leg kinematics model; on the basis of the kinematic model, a controller model for predicting human body movement intention by electromyographic signals is designed; and respectively adopting a controller model for predicting human motion intention by adopting electromyographic signals to correspondingly control the motion of the first leg or the motion of the second leg according to the different stages of the first leg and the second leg. The control method has the advantages of simple realization process, less number of parameters to be adjusted in the control process and capability of realizing the universality of different electromyographic signals among different individuals.
Description
Technical Field
The invention relates to the technical field of rehabilitation engineering, in particular to a man-machine interaction motion control method based on electromyographic signals and joint stress.
Background
The exoskeleton walking-assisting robot is a high-tech achievement integrating multiple disciplinary knowledge such as human body information detection, robot automatic control, neural engineering and the like. The robot design and research aim at realizing a preset human motion function by adopting a machine motion auxiliary technology, and the complete function of the robot is realized by three key nodes of intention generation, intention identification and feedback stimulation. The control process of an exoskeleton walking robot can be described as: first generating an athletic intent by the wearer; then capturing the movement intention of the person by a sensor on the robot, and classifying and quantifying the movement intention of the person by utilizing various recognition algorithms; and finally, adjusting each joint motor of the robot by a joint controller of the robot according to the recognition result of the human motion intention, thereby realizing the function of assisting the human motion. The human exoskeleton and the robot exoskeleton are two different dynamic systems, and when a person wears the exoskeleton to walk, the person and the exoskeleton need to cooperate with each other to achieve the purpose of walking together. Therefore, human-machine collaboration needs to be divided into two parts: one of them is the control method of the exoskeleton robot, and the other is to transmit the motion intention of the person to the exoskeleton.
Currently, methods for controlling joints of exoskeleton walking robots include a phase sequence control method, a control method based on a predetermined gait, and a sensitivity amplification control method. The phase sequence control method comprises the steps of firstly dividing the activities (such as walking, standing, climbing stairs and the like) of the lower limbs of a human body into different phase stages, setting phase transition time for switching of adjacent phases, then setting auxiliary force which should be applied by the robot aiming at different phases, determining the phase stage of the current activity of the robot by a controller according to feedback information of a sensor in the running process of the robot, and then applying influence on the human body according to the auxiliary force which is set in advance to realize the function of providing assistance for the activities of the lower limbs of the human body. The phase sequence control method sets the magnitude of the auxiliary force aiming at different stages of different activities of the lower limbs of the human body, and can realize various auxiliary functions; however, each phase assist force parameter is complex to set and difficult to implement, and the set assist force is relatively fixed and lacks flexibility, and needs to be customized for different users. According to the control method based on the preset gait, after the movement intention of the person is obtained, the auxiliary moment is adjusted according to the deviation of the current joint activity state and the predefined reference joint angle and the predefined reference angular velocity, and if the current robot activity state is consistent with the predefined reference state, the auxiliary force is zero. The control method based on the preset gait avoids the limitation that the phase sequence control method sets a fixed auxiliary torque for each phase, and can dynamically adjust the size of the auxiliary torque according to the running state of the robot; however, the requirement on the design of the reference track is high, one fixed reference track is difficult to adapt to the auxiliary operation of different users under different environmental conditions, and the auxiliary force cannot be dynamically adjusted according to the walking desire of the users in the movement process. The sensitivity amplification control method defines the transfer function of human applied force to the exoskeleton output as a sensitivity function, and aims to maximize the sensitivity function through the design of the controller, so that the action of the exoskeleton can be changed with small force. The sensitivity amplification control method does not need to install a sensor between human and machines, can still control the exoskeleton to move along with a wearer, but strictly depends on the accuracy of an inverse dynamics model of the exoskeleton.
At present, electromyographic signals are used as a way for acquiring motion information of lower limbs of a human body, the change of the electromyographic signals during a large amount of leg activities is usually collected, a pattern recognition model is built, and the model is optimized through a large amount of training, so that the purpose of predicting the human motion intention according to the electromyographic signals is achieved. However, the prediction model obtained by the simple training based on the electromyographic signals is difficult to have universality, because the muscle strength of different parts of each leg is different, the coordination modes of different muscle tissues are different during activities, and the training model obtained without any prior knowledge is difficult to have universality.
Therefore, in order to solve the problems of the existing joint control method for the exoskeleton walking-assisted robot, a man-machine interaction control method for coordinating the movement between the user and the robot, which can adjust the exoskeleton robot and realize the universality of electromyographic signals, needs to be provided in the walking process.
Disclosure of Invention
Aiming at the problems of the existing joint control method of the exoskeleton walking-aid robot, the embodiment of the invention provides a man-machine interaction control method based on electromyographic signals and joint stress. The man-machine interaction control method based on the electromyographic signals and the joint stress is simple in implementation process, the number of parameters needing to be adjusted in the control process is small, and the universality of the electromyographic signals can be realized.
The embodiment of the invention provides a man-machine interaction control method based on electromyographic signals and joint stress, which comprises the following specific scheme: a man-machine interaction control method based on electromyographic signals and joint stress comprises the following steps: establishing a leg kinematics model of an exoskeleton robot, the leg comprising a first leg and a second leg; designing motion controller models of the exoskeleton robots in different stages on the basis of the leg kinematics model; on the basis of the kinematic model, a controller model for predicting human body movement intention by electromyographic signals is designed; and respectively adopting a controller model for predicting human motion intention by adopting electromyographic signals to correspondingly control the motion of the first leg or the motion of the second leg according to the different stages of the first leg and the second leg.
Preferably, the phases comprise a support phase and a swing phase.
Preferably, the leg kinematics model of the first leg and the leg kinematics model of the second leg are the same.
Preferably, the leg kinematics model is a relationship between the ankle joint end velocity and the joint angular velocity of the exoskeleton robot, and the relationship is expressed by the following formula:
wherein, A y ＝L u sin(θ 1 )+L d sin(θ 1 -θ 2 )，A z ＝-L u cos(θ 1 )-L d cos(θ 1 -θ 2 )，A y Indicating the coordinate of the ankle joint in the Y-axis, A z Indicating the coordinate of the ankle joint in the Z-axis, L u Is the thigh length, L, of the exoskeleton robot d The length of the lower leg of the exoskeleton robot, theta 1 Angle of rotation, θ, for the exoskeleton robot to lift the leg forward at the hip joint 2 Is the bending angle of the knee joint of the exoskeleton robot.
Preferably, a Jacobian matrix of the leg of the exoskeleton robot can be derived according to the leg kinematics model, and the expression of the Jacobian matrix is as follows:
wherein the content of the first and second substances,j is the Jacobian matrix, L u Is the thigh length, L, of the exoskeleton robot d Is the shank length, θ, of the exoskeleton robot 1 Angle of rotation, θ, for the exoskeleton robot to lift the leg forward at the hip joint 2 For the angle of knee joint bending of exoskeleton robot
Preferably, the motion controller model of the support phase is a desired angular velocity of support of the leg joint of the exoskeletal robot in the support phase, the desired angular velocity of support being expressed by the following equation:
wherein, the first and the second end of the pipe are connected with each other,
desired angular velocities for support of leg joints of the exoskeleton robot; tau is h The interaction torque between the human body and the exoskeleton robot is borne by hip joints and knee joints of the exoskeleton robot; j is a Jacobian matrix which is obtained by the derivation of a leg kinematics model; a. the 0 Reference coordinates for the ankle joint of the exoskeleton robot during the support phase; a is the current desired velocity of the ankle of the exoskeleton robot; k is 1 Is a rigidity coefficient for adjusting the restoring force applied to the ankle joint when the ankle joint deviates from the reference posture; k is 2 Is the elastic coefficient used to adjust the current desired velocity level of the ankle joint.
Preferably, the motion controller model of the swing phase is a swing desired angular velocity of a leg joint of the exoskeletal robot in the swing phase, the swing desired angular velocity being expressed by the following equation:
wherein, the first and the second end of the pipe are connected with each other,
pendulum for leg joint of exoskeleton robotA dynamic desired angular velocity; tau is h The moment is the interaction moment between the human body and the exoskeleton robot borne by hip joints and knee joints of the exoskeleton robot; j is a Jacobian matrix which is obtained by the derivation of a leg kinematics model; a. the r Reference coordinates of the ankle joint of the exoskeleton robot in a swing stage; a is the current desired speed of the ankle joint of the exoskeleton robot; k 1 Is a rigidity coefficient for adjusting the restoring force applied to the ankle joint when the ankle joint deviates from the reference posture; k is 2 Is the elastic coefficient used to adjust the current desired velocity level of the ankle joint.
Preferably, the expression of the controller model for predicting human body movement intention by the electromyographic signal is as follows:
wherein the content of the first and second substances,
desired angular velocities for exoskeleton leg joints; tau. h The interaction torque between the human body and the exoskeleton robot is borne by hip joints and knee joints of the exoskeleton robot; f a An auxiliary force item which is increased according to the electromyographic signals; j is a Jacobian matrix which is obtained by the derivation of a leg kinematics model; a. the r Reference coordinates of an ankle joint of the exoskeleton robot in a swing stage; a is the current desired speed of the ankle joint of the exoskeleton robot; k 1 The rigidity coefficient is used for adjusting the restoring force applied to the ankle joint when the ankle joint deviates from the reference posture; k 2 Is the elastic coefficient used to adjust the current desired velocity level of the ankle joint.
Preferably, said τ is h Can be obtained by numerical calculations taken by pressure sensors mounted at the thigh and calf.
Preferably, the exoskeleton robot comprises: the leg part is used for assisting the legs of a person to walk; the motor is connected with the leg part and is used for driving the leg part to move; a wearable part connected with the leg part and the motor and used for fixing the exoskeleton robot to a human body; a foot portion connected to the leg portion for supporting a foot of a person; and the pressure sensor is used for acquiring pressure information of the exoskeleton robot in the walking process.
According to the technical scheme, the embodiment of the invention has the following advantages:
the embodiment of the invention provides a human-computer interaction control method based on electromyographic signals and joint stress, which divides the walking assisting process of an exoskeleton robot into a supporting stage and a swinging stage, so that the control process is easier to realize by only controlling the exoskeleton robot according to the current interaction state of a person and the exoskeleton without establishing a complex dynamic model for the exoskeleton robot and setting the movement track of the exoskeleton in advance. Further, the man-machine interaction control method based on the electromyographic signals and the joint stress provided by the embodiment of the invention has the advantages of less quantity of parameters needing to be adjusted, simple and intuitive modeling process and easiness in understanding. Furthermore, the embodiment of the invention provides a man-machine interaction control method based on electromyographic signals and joint stress, and multiple control effects can be achieved by adjusting control parameters of the same controller. Furthermore, the embodiment of the invention sets a range for detecting the electromyographic signals based on the provided man-machine interaction control method for the electromyographic signals and the joint stress, can avoid the difference of the electromyographic signal detection among different individuals, improves the detection precision and the universality of the movement intention, and enriches the functions of the controller.
Drawings
FIG. 1 is a schematic flow chart and steps of a man-machine interaction control method based on electromyographic signals and joint stress provided by the embodiment of the invention;
FIG. 2 is a schematic diagram of the exoskeleton robot employed in the embodiment of FIG. 1;
FIG. 3 is a schematic representation of an assisted walking posture using the exoskeleton robot of FIG. 2;
FIG. 4 is a block diagram schematically illustrating the structure of a human-computer interaction controller in the embodiment shown in FIG. 1;
FIG. 5 is a schematic block diagram of a structure of a human-computer interaction controller based on an electromyographic signal and joint stress in the embodiment of FIG. 1.
Description of the reference symbols in the drawings:
100. exoskeleton robot 10, wearable part 20, and leg part
21. First leg 23, second leg 30 motor
40 foot 41, first foot 43, second foot
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be implemented in other sequences than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, a schematic flow chart and steps of a human-computer interaction control method based on electromyographic signals and joint stress according to an embodiment of the present invention are provided. In the embodiment, the man-machine interaction control method based on the electromyographic signals and the joint stress comprises four steps.
Step S1: a leg kinematics model of an exoskeleton robot is established, the legs including a first leg and a second leg. The first leg may also be referred to as the left leg and the second leg may also be referred to as the right leg. In this embodiment, the leg kinematics model of the first leg and the leg kinematics model of the second leg are the same.
Referring to fig. 2, exoskeleton robot 100 used in the embodiment of the present invention is schematically configured. Exoskeleton robot 100 comprises legs 20 for assisting walking of human legs, motors 30 connected to legs 20 for driving legs 20 to move, wearing parts 10 connected to legs 20 and motors 30 for fixing exoskeleton robot 100 to human body, feet 40 connected to legs 20 for supporting human feet, and pressure sensors (not shown) for collecting pressure information of exoskeleton robot 100 during walking.
Further, the leg portion 20 includes a first leg portion 21 and a second leg portion 23, and the first leg portion 21 and the second leg portion 23 are identical in structural design. Each leg part comprises a thigh part, a lower leg part, a knee joint connecting the thigh part and the lower leg part, and a hip joint above the thigh part. Preferably, the exoskeleton robot is provided with straps outside the thighs and the shanks. The lower limbs of the human body are fixed on the legs of the exoskeleton robot through the binding bands. The foot 40 includes a first foot 41 and a second foot 43. The first foot 41 is connected to the first leg 21, and an ankle joint is provided at the joint of the first foot and the first leg. The second foot 43 is connected to the second leg 23, and an ankle joint is provided at the connection between the two.
The pressure sensor may include a plurality of different pressure sensors disposed at different locations of exoskeleton robot 100. Specifically, the pressure sensor is arranged between a thigh part and a shank part of the exoskeleton robot and is used for acquiring interaction force information of the thigh and the shank of the human body and an exoskeleton of the exoskeleton robot; the pressure sensor is arranged on the foot and used for collecting the pressure of the sole of the foot so as to detect the motion state change information of the human body.
The motor 30 is a dc motor. The motor 30 is adjusted by the lower extremity exoskeleton man-machine interaction control method based on joint stress provided by the embodiment of the invention, so as to provide power assistance for walking of a user wearing the exoskeleton robot 100.
In the embodiment of the present invention, the leg kinematics model of the first leg is the same as the leg kinematics model of the second leg, and therefore, the embodiment of the present invention is only described by using the modeling process of the leg kinematics model of the first leg. Referring to fig. 3, the hip joint rotation center point is set to H, the knee joint rotation center point is set to K, the ankle joint rotation center point is set to a, and the exoskeleton robot thigh length is set to L in the exoskeleton supporting state u The length of the exoskeleton robot shank is set to be L d The rotation angle of the hip joint when lifting the leg forward is set to θ 1 The angle of flexion of the knee joint is set to theta 2 The interaction force in the leg-lifting direction on the thigh is F u1 The interaction force of the thigh in the leg falling direction is F u2 The interaction force of the lower leg in the kicking direction is F d1 The interaction force of the lower leg in the leg bending direction is F d2 The position of the knee joint reference point when the exoskeleton robot lifts the leg is K ', and the position of the ankle joint reference point when the exoskeleton robot lifts the leg is A'.
And establishing a reference coordinate system by taking the point H as an original point, taking the vertical direction as the Z-axis direction and the rotation axis direction of the hip joint as the X-axis direction. At this time, the coordinate of the ankle joint on the Y-axis is as shown in formula 1:
A y ＝L u sin(θ 1 )+L d sin(θ 1 -θ 2 ) (formula 1)
At this time, the coordinate of the ankle joint on the Z-axis is as shown in equation 2:
A z ＝-L u cos(θ 1 )-L d cos(θ 1 -θ 2 ) (formula 2)
According to the formula 1 and the formula 2, the relationship between the ankle joint terminal velocity and the angular velocity of each joint is calculated and obtained as shown in the formula 3:
equation 3 is the leg kinematics model. As can be derived from equation 3, the jacobian matrix for the exoskeleton leg is shown in equation 4:
step S2: and designing motion controller models of the exoskeleton robots in different stages on the basis of the leg kinematics model. In this embodiment, the different phases include a support phase and a swing phase.
In the supporting stage, the reference coordinate of the ankle joint is A 0 (A y0 ,A z0 ) Then the interaction between the person and the exoskeleton robot can be described by equation 5:
wherein, F is the desired interaction force,
for the currently desired velocity of the ankle joint, K 1 Is the coefficient of stiffness, K 2 Is the elastic coefficient. K 1 For adjusting the restoring force applied to the ankle joint when it deviates from the reference attitude, and K 1 ＞0。K 2 For adjusting the current desired velocity of the ankle joint, and K 2 Is greater than 0. The desired interaction force F can be converted to the respective joint by means of a Jacobian matrix (tau) h ＝J T F) In that respect The currently desired speed A can also be converted to the respective joint by means of a Jacobian matrix
Therefore, equation 5 can also be rewritten as equation 6:
wherein, the first and the second end of the pipe are connected with each other,
desired angle of support for exoskeleton leg jointsSpeed; tau is h The interaction moment between a person and the exoskeleton born by the hip joint and the knee joint of the exoskeleton can be calculated by numerical values obtained by pressure sensors arranged at the thigh and the shank; j is a Jacobian matrix which is derived from the exoskeleton kinematics model. Equation 6 is the mathematical model of the support motion controller.
And designing a swing motion controller model of the exoskeleton robot in a swing stage on the basis of the leg kinematics model. Similar to the design process of the above steps, in the swing stage, if the reference coordinate of the ankle joint is A r (A yr ,A zr ) At this time, the mathematical model of the swing motion controller of the exoskeleton robot can be described as shown in equation 7:
wherein, the first and the second end of the pipe are connected with each other,
angular velocities are desired for the oscillations of the exoskeleton robot's leg joints. Equations 7 and 6 are essentially the same controller mathematical model, but with different reference coordinates.
Referring to fig. 4, a schematic block diagram of a structure of a human-computer interaction controller in the embodiment of the present invention. The process of human-computer interaction by a human-computer interaction controller (namely, a motion controller) specifically comprises the following steps: determining reference point position Ar and impedance control parameters K1 and K2 of ankle joint, and obtaining desired angular velocity of exoskeleton through motion controller of formula 6 or formula 7
And performing control, wherein the control process is closed-loop control. In the control process, the man-machine interaction torque tau h And the current state of each joint of the exoskeleton (including reference point a and jacobian matrix J) as closed-loop feedback parameters. The lower controller is used for controlling the lower controller according to the desired angular velocity of the exoskeleton
Controlling the motion of the exoskeleton robot.
Step S3: and designing a controller model for predicting human motion intention by electromyographic signals on the basis of the kinematic model.
In the swing stage, after the ankle joint reaches the reference point, the controller plays a role in actually blocking when the ankle joint falls to the ground and is converted into the support stage, and although the ankle joint is not large, the ankle joint needs to be pressed down by the wearer with force to achieve the aim. At this time, the electromyographic signal is detected, the movement intention of the wearer to be stepped on is identified according to the change of the electromyographic signal, and the controller applies additional auxiliary force. If a user needs to step over a higher obstacle, the ankle joint needs to be continuously lifted upwards after reaching a reference point, the controller plays a role of blocking at the moment, the movement intention of the wearer to lift the leg is identified according to the change of the electromyographic signals, the controller applies additional auxiliary force, and the flexibility of movement assistance is greatly improved.
In the supporting stage, if the distance of the coordinate of the ankle joint deviating from the reference coordinate is small, the electromyographic signal is not detected. If the ankle joint is located near a swing stage reference point, namely the supporting leg is in a bow-step posture, then the supporting leg has two activity choices, one is kept in an original state, the other is returned to a supporting leg reference posture state, the leg electromyographic signals are detected at the moment, the two behavior intentions can be accurately identified, then additional auxiliary force is applied by the controller according to the prediction result of the electromyographic signals, and the supporting leg can be accurately and flexibly assisted in the activity states of normal walking, stair climbing, stopping and the like.
In summary, the detection of the electromyographic signals when the ankle joint is in the vicinity of the swing leg reference point is performed with different assistance applied by the controller according to different activity phases. Therefore, the range is limited for the recognition of the movement intention, the recognition accuracy and the universality are improved, the controller is more flexible, the walking aid control system can be suitable for various walking aid scenes, and the walking aid control system has more functions.
Therefore, a specific expression of the controller model for predicting the human body movement intention by the electromyographic signal can be as shown in equation 8:
wherein, the first and the second end of the pipe are connected with each other,
desired angular velocities for exoskeleton leg joints; tau is h The moment is the interaction moment between the human body and the exoskeleton robot borne by hip joints and knee joints of the exoskeleton robot; f a An auxiliary force item which is increased according to the electromyographic signals; j is a Jacobian matrix which is obtained by the derivation of a leg kinematics model; a. the r Reference coordinates of an ankle joint of the exoskeleton robot in a swing stage; a is the current desired speed of the ankle joint of the exoskeleton robot; k 1 Is a rigidity coefficient for adjusting the restoring force applied to the ankle joint when the ankle joint deviates from the reference posture; k is 2 Is the elastic coefficient used to adjust the current desired velocity level of the ankle joint.
Step S4: and according to the different stages of the first leg and the second leg, correspondingly controlling the motion of the first leg or the motion of the second leg by respectively adopting a controller model for predicting the human motion intention by adopting an electromyographic signal.
Referring to fig. 3, a schematic diagram of the exoskeleton robot performing the walking assistance posture in the embodiment of the present invention is shown. If a human body wears the exoskeleton robot to walk, a first leg is taken first, and then the first leg is a swing leg and a second leg is a supporting leg in a starting stage. The control procedure at this stage can be described as follows:
the reference point of the ankle joint of the first leg is point A' in FIG. 3, and the coordinate of the point is set as A r (A yr ,A zr ) Then equation 8 is adopted as the controller of the exoskeleton left leg at this stage, and at this time, the human-computer interaction torque τ is h At zero, the virtual spring damping between the current point of the ankle joint and the reference point will produce a desired rate of motion under the influence of the controller, causing the first leg of the exoskeleton to have a tendency to move toward the reference point. Subsequently, the man-machine interaction moment τ h Will gradually increase, and if the wearer does not want to move, the resulting final state is τ h ＝J T K 1 (A r -a) when the desired speed of movement output by the controller is zero, but there is always a tendency to move towards the reference point. If the wearer wants to take the first leg, the interaction moment τ h Will drop and the controller will generate a desired velocity toward the reference point and the bottom level controller will control the movement of the exoskeleton left leg until the first leg reaches the reference point. When the ankle joint of the first leg reaches a specified reference point, the damping effect of the virtual spring is weakened, the movement intention of a wearer for lifting or dropping the leg can be judged according to the electromyographic signals, the controller provides additional auxiliary force, and the wearer can operate the first leg of the exoskeleton robot through man-machine interaction torque to complete the following actions until the movement state of the first leg is switched from the swinging stage to the supporting stage. When the first leg is just switched from the swing phase to the support phase, the ankle joint reference point coordinates become a 0 (A y0 ,A z0 ) The effect of the virtual spring damping is enhanced, the movement intention of the first leg to keep still or return to the reference point can be judged according to the electromyographic signals, and additional auxiliary force is provided by the controller to complete the next movement.
In the first leg swing stage, the second leg is the support stage, the reference point of the ankle joint of the second leg is the point A in fig. 3, and the coordinate of the point is set as A 0 (A y0 ,A z0 ) Then equation 8 is used as the controller for the second leg of the phase. Since the current second leg ankle joint is at the reference point and the second leg ankle joint will always move around the reference point during the support phase, the effect of the virtual spring damping is weak and the reference speed generated by the controller is dominated by the human-machine interaction moment of the second leg. When the first leg is transited from the swing leg to the supporting leg, the second leg also moves to the maximum amplitude of the supporting leg, and a certain movement trend is accumulated, so that the second leg cannot generate large sudden change in the transition process from the supporting leg to the swing leg, and the transition stability is ensured.
By repeating the cycle, the motion controller provided by the embodiment of the invention realizes the function of assisting the exoskeleton walk-assisting robot in the motion of the wearer by switching the reference points between the first leg supporting stage and the second leg supporting stage and the swinging stage.
Referring to fig. 5, the structural schematic block diagram of the human-computer interaction controller based on the electromyographic signals and the joint stress in the embodiment of the invention is shown. The process of human-computer interaction of a controller model (namely a motion controller) for predicting human motion intention by electromyographic signals is as follows: determining reference point position Ar of ankle joint and impedance control parameters K1 and K2, and calculating desired angular velocity of exoskeleton by motion controller of formula 8
And performing control, wherein the control process is closed-loop control. In the control process, the man-machine interaction torque tau h And the auxiliary force Fa increased according to the electromyographic signals and the current state of each joint of the exoskeleton (comprising a reference point A and a Jacobian matrix J) are used as closed-loop feedback parameters. The lower controller is used for controlling the lower controller according to the desired angular velocity of the exoskeleton
And controlling the motion of the exoskeleton robot.
The man-machine interaction control method based on the electromyographic signals and the joint stress divides the walking process of the exoskeleton robot assisted by the human into two stages: one is the support phase and one is the swing phase. The same motion controller is used for the support phase and the swing phase, but the control parameters of the motion controller are different.
In the supporting stage (including single-leg support and double-leg support), with H-K-a in fig. 3 as a reference posture, an impedance controller is designed, a virtual spring damper is added between the current posture and the reference posture of the exoskeleton robot, so that when the leg of the exoskeleton robot deviates from the reference posture, the controller can provide an acting force returning to the reference posture for the exoskeleton robot, and the acting force and an interaction force between the human and the exoskeleton robot are differentiated to jointly complete the motion adjustment of the leg of the exoskeleton robot.
Compared with the existing control method, the man-machine interaction control method based on the electromyographic signals and the joint stress provided by the embodiment of the invention is simpler to realize, does not need to establish a complex dynamic model for the exoskeleton robot, does not need to set the motion track of the exoskeleton in advance, and only controls the exoskeleton robot according to the current interaction state of the human and the exoskeleton.
Furthermore, compared with the existing control method, the man-machine interaction control method based on the electromyographic signals and the joint stress provided by the embodiment of the invention has the advantages that the number of parameters required to be adjusted is small, and the method is simple, intuitive and easy to understand. In practical operation, the method for controlling the human-computer interaction motion of the lower extremity exoskeleton based on the joint stress provided by the embodiment of the invention only needs to adjust three parameters: a. the r For the reference posture position of the ankle joint in the interactive motion control process, the function of the parameter is that the current position of the ankle joint and the reference posture position A r A virtual spring damper is arranged, so that the farther the ankle joint deviates from the position, the larger the auxiliary force is exerted; k 1 The rigidity coefficient is adjusted by the proportion of the influence of the deviation between the current position of the ankle joint and the reference position and the interaction moment on the expected speed of the exoskeleton; k is 2 Is a damping coefficient which regulates the effect of the relevant parameters on the desired movement velocity of the exoskeleton, the larger the damping coefficient, the smaller the influence of the relevant parameters.
Furthermore, the human-computer interaction control method based on the electromyographic signals and the joint stress provided by the embodiment of the invention is realized by changing K 1 The control effect of the follow-up can be realized by setting to zero. Will K 1 Assistance with respect to the reference point can be achieved by setting to a certain value. By adjusting the control parameters, the same controller can achieve multiple control effects.
Furthermore, the man-machine interaction control method based on the electromyographic signals and the joint stress sets a range for detecting the electromyographic signals, can avoid the difference of the electromyographic signal detection among different individuals, improves the detection precision and the universality of the movement intention, and enriches the functions of the controller.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A man-machine interaction control method based on electromyographic signals and joint stress is characterized by comprising the following steps:
establishing a leg kinematics model of an exoskeleton robot, the legs including a first leg and a second leg;
designing motion controller models of the exoskeleton robots in different stages on the basis of the leg kinematics model;
on the basis of the kinematic model, a controller model for predicting human motion intention by electromyographic signals is designed;
according to the different stages of the first leg and the second leg, a controller model which respectively adopts electromyographic signals to predict the human motion intention correspondingly controls the motion of the first leg or the motion of the second leg, wherein the stages comprise a supporting stage and a swinging stage;
the motion controller model of the support phase is a support desired angular velocity of a leg joint of the exoskeletal robot in the support phase, the support desired angular velocity being expressed as follows:
wherein the content of the first and second substances,
desired angular velocities for support of leg joints of the exoskeleton robot; tau. h The moment is the interaction moment between the human body and the exoskeleton robot borne by hip joints and knee joints of the exoskeleton robot; j is a Jacobian matrix which is obtained by the derivation of a leg kinematics model; a. the 0 Reference coordinates of the ankle joint of the exoskeleton robot in a supporting stage; a is the current desired speed of the ankle joint of the exoskeleton robot; k 1 The rigidity coefficient is used for adjusting the restoring force applied to the ankle joint when the ankle joint deviates from the reference posture; k is 2 Is an elastic coefficient for adjusting the current expected speed of the ankle joint;
the motion controller model of the swing phase is a swing desired angular velocity of a leg joint of the exoskeletal robot in the swing phase, and the swing desired angular velocity is expressed by the following formula:
wherein the content of the first and second substances,
desired angular velocities for the oscillations of the leg joints of the exoskeleton robot; tau is h The moment is the interaction moment between the human body and the exoskeleton robot borne by hip joints and knee joints of the exoskeleton robot; j is a Jacobian matrix which is obtained by the derivation of a leg kinematics model; a. the r Reference coordinates of an ankle joint of the exoskeleton robot in a swing stage; a is the current desired speed of the ankle joint of the exoskeleton robot; k is 1 The rigidity coefficient is used for adjusting the restoring force applied to the ankle joint when the ankle joint deviates from the reference posture; k is 2 Is used for adjusting the current expected speed of the ankle joint to be highA small elastic coefficient;
the expression of the controller model for predicting human motion intention by the electromyographic signals is as follows:
wherein the content of the first and second substances,
desired angular velocities for exoskeleton robot leg joints; tau. h The moment is the interaction moment between the human body and the exoskeleton robot borne by hip joints and knee joints of the exoskeleton robot; f a An auxiliary force item added according to an electromyographic signal; j is a Jacobian matrix which is obtained by the derivation of a leg kinematics model; a. the r Reference coordinates of the ankle joint of the exoskeleton robot in a swing stage; a is the current desired velocity of the ankle of the exoskeleton robot; k is 1 The rigidity coefficient is used for adjusting the restoring force applied to the ankle joint when the ankle joint deviates from the reference posture; k 2 Is the elastic coefficient used to adjust the current desired velocity level of the ankle joint.
2. The human-computer interaction control method based on the electromyographic signals and the joint stress as recited in claim 1, wherein the leg kinematics model of the first leg and the leg kinematics model of the second leg are the same.
3. The human-computer interaction control method based on electromyographic signals and joint stress as claimed in claim 1, wherein the leg kinematics model is a relationship between an ankle joint terminal velocity and each joint angular velocity of the exoskeleton robot, and the relational expression is as follows:
wherein A is y ＝L u sin(θ 1 )+L d sin(θ 1 -θ 2 )，A z ＝-L u cos(θ 1 )-L d cos(θ 1 -θ 2 )，A y Indicating the coordinate of the ankle joint in the Y-axis, A z Indicating the coordinate of the ankle joint in the Z-axis, L u Is the thigh length, L, of the exoskeleton robot d The length of the lower leg of the exoskeleton robot, theta 1 Angle of rotation, theta, for lifting the leg forward of the exoskeleton robot's hip joint 2 Is the angle at which the knee joint of the exoskeleton robot bends.
4. The human-computer interaction control method based on the electromyographic signals and the joint stress as claimed in claim 3, wherein a Jacobian matrix of the leg of the exoskeleton robot can be derived according to the leg kinematics model, and the expression of the Jacobian matrix is as follows:
wherein J is Jacobian matrix, L u Is the thigh length, L, of the exoskeleton robot d Is the shank length, θ, of the exoskeleton robot 1 Angle of rotation, θ, for the exoskeleton robot to lift the leg forward at the hip joint 2 For the angle of knee joint bending of exoskeleton robot
5. The human-computer interaction control method based on electromyographic signals and joint stress as claimed in claim 1, wherein the τ is h Can be obtained by numerical calculations taken by pressure sensors mounted at the thigh and calf.
6. The human-computer interaction control method based on the electromyographic signals and the joint stress as claimed in claim 1, wherein the exoskeleton robot comprises:
the leg part is used for assisting the legs of the person to walk;
the motor is connected with the leg part and is used for driving the leg part to move;
a wearable part connected with the leg part and the motor and used for fixing the exoskeleton robot to a human body;
a foot portion connected to the leg portion for supporting a foot of a person;
and the pressure sensor is used for acquiring pressure information of the exoskeleton robot in the walking process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811599328.4A CN111360815B (en) | 2018-12-26 | 2018-12-26 | Human-computer interaction motion control method based on electromyographic signals and joint stress |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811599328.4A CN111360815B (en) | 2018-12-26 | 2018-12-26 | Human-computer interaction motion control method based on electromyographic signals and joint stress |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111360815A CN111360815A (en) | 2020-07-03 |
| CN111360815B true CN111360815B (en) | 2022-07-26 |
Family
ID=71200584
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811599328.4A Active CN111360815B (en) | 2018-12-26 | 2018-12-26 | Human-computer interaction motion control method based on electromyographic signals and joint stress |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111360815B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113317960B (en) * | 2021-05-28 | 2022-06-14 | 复旦大学 | Analysis method for measuring and researching interaction force of wearing exoskeleton |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105963107A (en) * | 2015-03-10 | 2016-09-28 | 株式会社捷太格特 | Swinging joint device, walking assisting device, conveying device, manipulator, and walking-ability assisting device |
| CN106181968A (en) * | 2016-08-17 | 2016-12-07 | 苏冀 | A kind of self adaptation stress class articulation mechanism and there is the ESD of this mechanism |
| CN106695794A (en) * | 2017-02-20 | 2017-05-24 | 苏州晨本智能科技有限公司 | Mobile machine arm system based on surface myoelectric signal and control method of mobile machine arm system |
| CN108283569A (en) * | 2017-12-27 | 2018-07-17 | 北京精密机电控制设备研究所 | A kind of exoskeleton robot control system and control method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7731670B2 (en) * | 2007-02-02 | 2010-06-08 | Honda Motor Co., Ltd. | Controller for an assistive exoskeleton based on active impedance |
-
2018
- 2018-12-26 CN CN201811599328.4A patent/CN111360815B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105963107A (en) * | 2015-03-10 | 2016-09-28 | 株式会社捷太格特 | Swinging joint device, walking assisting device, conveying device, manipulator, and walking-ability assisting device |
| CN106181968A (en) * | 2016-08-17 | 2016-12-07 | 苏冀 | A kind of self adaptation stress class articulation mechanism and there is the ESD of this mechanism |
| CN106695794A (en) * | 2017-02-20 | 2017-05-24 | 苏州晨本智能科技有限公司 | Mobile machine arm system based on surface myoelectric signal and control method of mobile machine arm system |
| CN108283569A (en) * | 2017-12-27 | 2018-07-17 | 北京精密机电控制设备研究所 | A kind of exoskeleton robot control system and control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111360815A (en) | 2020-07-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Quintero et al. | Preliminary experiments with a unified controller for a powered knee-ankle prosthetic leg across walking speeds | |
| Gregg et al. | Virtual constraint control of a powered prosthetic leg: From simulation to experiments with transfemoral amputees | |
| Huang et al. | Hierarchical learning control with physical human-exoskeleton interaction | |
| JP4951732B2 (en) | Drive method, drive control device, and robot | |
| Wei et al. | Synergy-based control of assistive lower-limb exoskeletons by skill transfer | |
| Seo et al. | Adaptive oscillator-based control for active lower-limb exoskeleton and its metabolic impact | |
| Huang et al. | Hierarchical interactive learning for a human-powered augmentation lower exoskeleton | |
| Ka et al. | Minimizing human-exoskeleton interaction force by using global fast sliding mode control | |
| Wang et al. | Model predictive control-based gait pattern generation for wearable exoskeletons | |
| CN103040586A (en) | External skeleton robot for exercising lower limbs and exercise control method thereof | |
| CN111358667B (en) | Method for controlling man-machine interactive motion of lower limb exoskeleton based on joint stress | |
| Zhang et al. | Sensor fusion for predictive control of human-prosthesis-environment dynamics in assistive walking: A survey | |
| Nasr et al. | Myoelectric control of robotic leg prostheses and exoskeletons: A review | |
| CN112405504B (en) | Exoskeleton robot | |
| Yan et al. | A novel adaptive oscillators-based control for a powered multi-joint lower-limb orthosis | |
| CN111360815B (en) | Human-computer interaction motion control method based on electromyographic signals and joint stress | |
| CN111728827B (en) | Power lower limb exoskeleton control method, device and system | |
| Chen et al. | Step length adaptation for walking assistance | |
| Chen et al. | Modeling and control of knee-ankle-toe active transfemoral prosthesis | |
| Talatian et al. | Design and implementation of an intelligent control system for a lower-limb exoskeleton to reduce human energy consumption | |
| Bae et al. | Real-time estimation of lower extremity joint torques in normal gait | |
| Anwar et al. | Adaptive trajectory control to achieve smooth interaction force in robotic rehabilitation device | |
| Ishizuka et al. | Modeling walking behavior of powered exoskeleton based on complex-valued neural network | |
| CN114012742B (en) | Control system of hip joint power assisting device | |
| Hosseini-Zahraei et al. | A Simple OpenSim-Simulink Interface for Cascaded Zero-Force Control of Human-Robot Interaction in a Hip Exoskeleton Robot |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |