CN114129399B - Online moment generator for passive training of exoskeleton robot - Google Patents
Online moment generator for passive training of exoskeleton robot Download PDFInfo
- Publication number
- CN114129399B CN114129399B CN202111442157.6A CN202111442157A CN114129399B CN 114129399 B CN114129399 B CN 114129399B CN 202111442157 A CN202111442157 A CN 202111442157A CN 114129399 B CN114129399 B CN 114129399B
- Authority
- CN
- China
- Prior art keywords
- moment
- exoskeleton robot
- online
- value
- 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
- 238000012549 training Methods 0.000 title claims abstract description 18
- 230000008878 coupling Effects 0.000 claims abstract description 42
- 238000010168 coupling process Methods 0.000 claims abstract description 42
- 238000005859 coupling reaction Methods 0.000 claims abstract description 42
- 230000005021 gait Effects 0.000 claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 27
- 230000002068 genetic effect Effects 0.000 claims abstract description 19
- 230000033001 locomotion Effects 0.000 claims abstract description 16
- 210000003127 knee Anatomy 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 8
- 230000007246 mechanism Effects 0.000 claims abstract description 4
- 230000005484 gravity Effects 0.000 claims description 35
- 210000002414 leg Anatomy 0.000 claims description 33
- 238000012937 correction Methods 0.000 claims description 24
- 230000001133 acceleration Effects 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 6
- 210000001624 hip Anatomy 0.000 claims description 6
- 210000004394 hip joint Anatomy 0.000 claims description 6
- 210000000629 knee joint Anatomy 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 244000309466 calf Species 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 210000001503 joint Anatomy 0.000 claims description 3
- 238000012986 modification Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000010363 phase shift Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 3
- 210000000689 upper leg Anatomy 0.000 claims description 3
- 210000003049 pelvic bone Anatomy 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 description 5
- 238000009396 hybridization Methods 0.000 description 4
- 230000035772 mutation Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 210000003141 lower extremity Anatomy 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007667 floating Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 206010033892 Paraplegia Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 239000011664 nicotinic acid Substances 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 210000002027 skeletal muscle Anatomy 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H3/00—Appliances for aiding patients or disabled persons to walk about
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H1/00—Apparatus for passive exercising; Vibrating apparatus; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
- A61H1/02—Stretching or bending or torsioning apparatus for exercising
- A61H1/0237—Stretching or bending or torsioning apparatus for exercising for the lower limbs
- A61H1/0255—Both knee and hip of a patient, e.g. in supine or sitting position, the feet being moved together in a plane substantially parallel to the body-symmetrical plane
- A61H1/0262—Walking movement; Appliances for aiding disabled persons to walk
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/50—Control means thereof
- A61H2201/5007—Control means thereof computer controlled
Landscapes
- Health & Medical Sciences (AREA)
- Epidemiology (AREA)
- Pain & Pain Management (AREA)
- Physical Education & Sports Medicine (AREA)
- Rehabilitation Therapy (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Orthopedic Medicine & Surgery (AREA)
- Rehabilitation Tools (AREA)
- Manipulator (AREA)
Abstract
The invention discloses an online moment generator for passive training of an exoskeleton robot, which is a mechanical electronic power device mainly composed of a left hip, a left knee, a right hip and a right knee, wherein each joint comprises a direct-current servo motor, a motor encoder, a Hall sensor and a harmonic speed reducing mechanism, the joints drive a coupling rod piece to move, and the coupling rod piece is combined with a binding belt fixing piece to enable a patient and the exoskeleton robot to synchronously move; the online moment generator comprises a motion data acquisition and processing module, a man-machine coupling dynamics model based on a genetic algorithm, an online gait track generation module, an online moment generation module and a motor driving module. The method can enhance the robust characteristic and the anti-interference characteristic of the robot motion in contact with the environment when a patient walks on the floor, effectively protect the operation of a motor, protect harmonic speed reduction equipment for torque output and enable the patient to walk on the floor more comfortably.
Description
Technical Field
The invention relates to an online moment generator for passive training of an exoskeleton robot, and belongs to the robot technology.
Background
The power-assisted exoskeleton robot is a lower limb walking bionic mechanical leg in a wearing mode, takes a person as a center, collects the trend of human body movement through a sensor, gives joint power assistance in the same gait direction as the person in the power assistance aspect, and drives the human body to generate corresponding movement in the walking assistance aspect so as to stimulate corresponding skeletal muscle groups; the traditional exoskeleton robot has a lifting space in terms of the current direct-drive variable-stiffness position control when the patient is driven to walk and train.
Chinese patent application 202010403012.4 proposes a control method, device and system for performing landing judgment by transmitting torque and collecting torque on a powered lower limb exoskeleton, which aims to reduce the number of sensors of the exoskeleton itself and cannot directly drive the device to perform passive rehabilitation exercise with track constraint.
The chinese patent application 201810497005.8 collects the data of each joint movement of a healthy person during typical movements by the driver of the exoskeleton, and processes the data to obtain a reference of the joint movement data of each typical movement. The control mode conversion method of the method comprises the following steps: a state machine based exoskeleton position control method is designed. The track output mode of the method is as follows: paraplegic patients wear walking-assisting exoskeleton, the exoskeleton actively drives the patients to move according to the reference joint track, and meanwhile, the lower limb joint movement of the patients in different states is reproduced through state switching of a state machine. The method only carries out gait data acquisition in the current mode of the driver, and adopts a position control mode in passive training, the rigidity of the position control mode is uncontrollable, friction injury caused by interference of mechanical design can be generated after a patient wears the device for a long time, the harmonic transmission device of the motor can be impacted, and the service life of the exoskeleton device is reduced.
Disclosure of Invention
The invention aims to: aiming at the problems that in the rehabilitation training of the existing exoskeleton robot in the use position mode of a patient, the position mode can cause impact to a harmonic transmission device when the exoskeleton robot walks on the ground, and the position control can cause more useless power consumption of a motor, the power supply time of a battery for walking is shortened, and the like, the invention provides an online moment generator for the active training of the exoskeleton robot.
The technical scheme is as follows: in order to achieve the above purpose, the invention adopts the following technical scheme:
an online moment generator for passive training of an exoskeleton robot is a mechanical electronic power device mainly composed of a left hip, a left knee, a right hip and a right knee, each joint comprises a direct-current servo motor, a motor encoder, a Hall sensor and a harmonic speed reducing mechanism, the joints drive a coupling rod piece to move, and the coupling rod piece is combined with a strap fixing piece to enable a patient and the exoskeleton robot to synchronously move; the online moment generator comprises a motion data acquisition and processing module, a man-machine coupling dynamics model based on a genetic algorithm, an online gait track generation module, an online moment generation module and a motor driving module;
the motion data acquisition and processing module acquires a detection value at the moment i when the exoskeleton robot carries a person to move, wherein the detection value comprises a pulse value A of a motor encoder ij And moment value F of Hall sensor ij Pulse value A based on motor encoder according to the relation between pulse and angle ij Calculating the actual angle value theta ij The method comprises the steps of carrying out a first treatment on the surface of the Where j represents a joint number, j=1 represents a left leg hip joint, j=2 represents a left leg knee joint, j=3 represents a right leg hip joint, and j=4 represents a right leg knee joint;
pulse value of i-moment exoskeleton robot is recorded as A i ＝{A ij Moment value of exoskeleton robot at moment i is F i ＝{F ij The position of the exoskeleton robot at the moment i is
Based onObtaining the corresponding speed ++under the equal sampling time interval through the equivalent differential operation>And acceleration->And->Respectively is theta ij Is to be +.>The actual gait track of the exoskeleton robot at the moment i;
will beAnd F i Inputting the dynamic parameters of the exoskeleton robot into a human-machine coupling dynamic model, and carrying out optimization training on the dynamic parameters of the exoskeleton robot to obtain a human-machine coupling dynamic parameter set S which accords with the current human body;
the online gait track generation module generates a gait track according to the actual gravity center height H of the human body iR And the height H of the gravity center of the standard standing position of the human body i0 Calculating a spatial correction parameter alpha from the deviation between the two in the direction of gravitational acceleration, and using the pair of spatial correction parameters alphaCorrecting to obtain an online gait track predicted value +.>Online location prediction valueOn-line speed predictor +.>On-line acceleration predictor +.>The center of gravity of the human body is the center point of the human body pelvic bone;
will beInput to an online moment generation module, and integrate a human-machine coupling dynamic parameter set S, and corrected by an impedance control system, a gravity online compensation system, a Golgi force centrifugal force and friction online compensation system to obtain a moment control value of the i+1 moment exoskeleton robot, wherein the moment control value is FA i+1 ＝{FA (i+1)j }；
The motor driving module executes the torque control value FA i+1 The exoskeleton robot is driven to move.
Specifically, according to the position of the exoskeleton robotFind the speed +.>And acceleration->The processing method of (2) is a central difference method, and the time sequence of the actual gait track can be obtained according to the boundary constraint of the start and the end of the position, the speed and the acceleration; moment value F of exoskeleton robot i The filtering process is performed using a zero phase shift butterworth low pass filter.
Specifically, a genetic algorithm is adopted to optimize a human-computer coupling dynamics parameter set S, and parameters in the human-computer coupling dynamics parameter set S comprise: coupling member inertia, coupling member centroid, coupling member mass, joint coefficient of dynamic friction, joint coefficient of static friction, and motor inertia.
The convergence range of the genetic algorithm refers to the parameters of the single robot, the floating range is obtained according to experience, and the population scale, the maximum genetic times, the individual length, the inheritance rate between generations, the hybridization rate, the mutation rate and the like of the genetic algorithm are selected according to experience so as to balance the identification speed and the identification precision; the genetic algorithm comprises the following steps:
(1) Creating an initial random population which can cover the interval range of parameters to be identified;
(2) Loading the position of the exoskeleton robot and corresponding speed and acceleration data;
(3) Substituting parameters obtained by population iteration into an optimal objective function constructed by using two norms;
(4) Calculating an allocation fitness value based on the ranking;
(5) Selecting a sub-population according to the population, the fitness value and the inheritance rate among generations;
(6) Recombining the sub population according to the hybridization degree, and mutating according to the mutation rate;
(7) Performing decimal conversion on individuals in the sub-population;
(8) Substituting the optimal objective function constructed by the two norms, and calculating an objective function value sequence;
(9) Reinserting the sub-population to generate a new sub-population;
(10) Performing decimal conversion on individuals of the sub-population to finish first-generation calculation and outputting an optimal solution and sequence numbers;
(11) And (3) returning to the step (3) until the iteration of the maximum genetic times is completed, and returning to the optimal parameter set.
Specifically, let exoskeleton robot thigh length be L 1 The calf length is L 2 The method comprises the steps of carrying out a first treatment on the surface of the i moment of the actual height H of the center of gravity of the human body iR And the height H of the gravity center of the standard standing position of the human body i0 The method is respectively calculated by the following steps:
(1) according to the position of the exoskeleton robot at the moment iThe center of gravity heights of the left leg and the right leg are calculated as follows:
LH i0 ＝L 1 ×sin(θ i1 )+L 2 ×sin(θ i1 +θ i2 )
RH i0 ＝L 1 ×sin(θ i3 )+L 2 ×sin(θ i3 +θ i4 )
calculating the height of the actual gravity center of the human body as H iR ＝max(LH iR ,RH iR )；
(2) According to the online position predicted value of the exoskeleton robot at the moment iThe center of gravity heights of the left leg and the right leg are calculated as follows:
calculating the height of the gravity center of the standard standing position of the human body at moment i to be H i0 ＝max(LH i0 ,RH i0 )。
Specifically, when the exoskeleton robot moves, one leg is a supporting leg, and the other leg is a swinging leg; the space correction parameter alpha is obtained through the following process:
(1) if H iR -H i0 > 0: the supporting leg landing height is higher than a preset value, and the step length is increased when the supporting leg is required to flatly pedal the ground at the sole, so that the aim of adjustment is fulfilled; if H iR -H i0 < 0: the supporting leg landing height is lower than a preset value, and the step length is reduced when the supporting leg is required to flatly pedal the ground at the sole, so that the aim of adjustment is fulfilled; whether above or below the preset value, the spatial modification parameters are designed to be
(2) If H iR -H i0 Approximately 0: this condition indicates that the user continues to walk at the original stride,
specifically, the execution of the online moment generation module includes the following execution steps:
(1) The online gait track generation module generates an online gait track prediction value of the exoskeleton robot according to the moment i+1Calculating the joint angle +.1 at time i->And joint angular velocity>Combining the exoskeleton robot rigid body rotation matrix to obtain a minimum correlation matrix Y of the man-machine coupling dynamic parameter set S;
(2) Calculating a moment control value tau=y×s of the exoskeleton robot at the time i+1, and taking the moment control value tau=y×s as an initial value of the moment control value of the exoskeleton robot;
(3) Impedance control regulator receptionCombination->Obtaining the moment correction quantity tau of the impedance control system 1_j Let the position correction coefficient be m p_j The speed correction coefficient is m v_j Moment adjusting value for adjusting joint impedance is +.>Then->Position correction coefficient m p_j And a velocity correction coefficient m v_j Set values based on gait characteristics and system constraints by the system;
(4) The bit is setDevice for placing articlesThe gravity on-line compensation system is brought to calculate compensation moment +.>Speed +.>Calculating compensation moment by using centrifugal force and friction force online compensation system>Calculating the moment control value of the exoskeleton robot at the moment i+1 as +.>
The beneficial effects are that: compared with the prior art, the online moment generator for passive training of the exoskeleton robot has the following advantages: 1. the invention carries out genetic algorithm identification on the dynamic parameters of man-machine coupling, and the obtained equation can meet the requirement of generating the track form of the appointed position, speed and acceleration by controlling the moment during passive training; 2. according to the invention, the mechanical rigidity of man-machine coupling when a patient performs passive training is adjusted through an impedance control strategy, so that the impact on a mechanical harmonic speed reducer is reduced, the output power of a walking electromechanical system is improved, and the service time of equipment is prolonged under the output of rated power of a battery; 3. the invention can better improve the control effect and control bandwidth through on-line feedback compensation of nonlinear factors such as gravity items, golgi force, centrifugal force, friction force and the like.
Drawings
FIG. 1 is a block diagram of a control system of the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings and the specific embodiments.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
As shown in fig. 1, an online torque generator for passive training of an exoskeleton robot is shown, the exoskeleton robot is a mechanical electronic power device mainly composed of a left hip, a left knee, a right hip and a right knee, each joint comprises a direct-current servo motor, a motor encoder, a hall sensor and a harmonic speed reducing mechanism, the joints drive a coupling rod piece to move, and the coupling rod piece is combined with a strap fixing piece to enable a patient and the exoskeleton robot to synchronously move; the online moment generator comprises a motion data acquisition and processing module, a man-machine coupling dynamics model based on a genetic algorithm, an online gait track generation module, an online moment generation module and a motor driving module. The respective modules are specifically described below.
Motion data acquisition and processing module
The motion data acquisition and processing module acquires a detection value at the moment i when the exoskeleton robot carries a person to move, wherein the detection value comprises a pulse value A of a motor encoder ij And moment value F of Hall sensor ij Pulse value A based on motor encoder according to the relation between pulse and angle ij Calculating the actual angle value theta ij The method comprises the steps of carrying out a first treatment on the surface of the Where j denotes a joint number, j=1 denotes a left leg hip joint, j=2 denotes a left leg knee joint, j=3 denotes a right leg hip joint, and j=4 denotes a right leg knee joint.
Pulse value of i-moment exoskeleton robot is recorded as A i ＝{A ij Moment value of exoskeleton robot at moment i is F i ＝{F ij Time i exoskeleton robotThe position isBased on->Adopting a central difference method, obtaining a time sequence of an actual gait track according to the boundary constraints of the start and the end of the position, the speed and the acceleration, and calculating the corresponding speed in equal sampling time intervals>And acceleration->And->Respectively is theta ij Is to be +.>The actual gait trajectory of the exoskeleton robot at time i.
Moment value F of exoskeleton robot i The filtering process is performed using a zero phase shift butterworth low pass filter.
(II) human-machine coupling dynamics model
Will beAnd F i Inputting the dynamic parameters of the exoskeleton robot into a human-machine coupling dynamic model, and carrying out optimization training on the dynamic parameters of the exoskeleton robot to obtain a human-machine coupling dynamic parameter set S which accords with the current human body; optimizing a man-machine coupling dynamics parameter set S by adopting a genetic algorithm, wherein parameters in the man-machine coupling dynamics parameter set S comprise: coupling member inertia, coupling member centroid, coupling member mass, joint coefficient of dynamic friction, joint coefficient of static friction, and motor inertia. The convergence range of the genetic algorithm refers to the parameters of the single robot and takes the floating range according to experience, and the genetic algorithmThe population scale, the maximum genetic times, the individual length, the inter-generation genetic rate, the hybridization rate, the mutation rate and the like are selected according to experience so as to achieve balance on the identification speed and the identification precision; the genetic algorithm comprises the following steps:
(1) Creating an initial random population which can cover the interval range of parameters to be identified;
(2) Loading the position of the exoskeleton robot and corresponding speed and acceleration data;
(3) Substituting parameters obtained by population iteration into an optimal objective function constructed by using two norms;
(4) Calculating an allocation fitness value based on the ranking;
(5) Selecting a sub-population according to the population, the fitness value and the inheritance rate among generations;
(6) Recombining the sub population according to the hybridization degree, and mutating according to the mutation rate;
(7) Performing decimal conversion on individuals in the sub-population;
(8) Substituting the optimal objective function constructed by the two norms, and calculating an objective function value sequence;
(9) Reinserting the sub-population to generate a new sub-population;
(10) Performing decimal conversion on individuals of the sub-population to finish first-generation calculation and outputting an optimal solution and sequence numbers;
(11) And (3) returning to the step (3) until the iteration of the maximum genetic times is completed, and returning to the optimal parameter set.
(III) on-line gait track generation module
The online gait track generation module generates a gait track according to the actual gravity center height H of the human body iR And the height H of the gravity center of the standard standing position of the human body i0 Calculating a spatial correction parameter alpha from the deviation between the two in the direction of gravitational acceleration, and using the pair of spatial correction parameters alphaCorrecting to obtain an online gait track predicted value +.>Online location prediction value +.>On-line speed predictor +.>On-line acceleration predictor +.>
(3.1) setting the thigh length of the exoskeleton robot to L 1 The calf length is L 2 The method comprises the steps of carrying out a first treatment on the surface of the i moment of the actual height H of the center of gravity of the human body iR And the height H of the gravity center of the standard standing position of the human body i0 The method is respectively calculated by the following steps:
(1) according to the position of the exoskeleton robot at the moment iThe center of gravity heights of the left leg and the right leg are calculated as follows:
LH i0 ＝L 1 ×sin(θ i1 )+L 2 ×sin(θ i1 +θ i2 )
RH i0 ＝L 1 ×sin(θ i3 )+L 2 ×sin(θ i3 +θ i4 )
calculating the height of the actual gravity center of the human body as H iR ＝max(LH iR ,RH iR )；
(2) According to the online position predicted value of the exoskeleton robot at the moment iThe center of gravity heights of the left leg and the right leg are calculated as follows:
calculating the height of the gravity center of the standard standing position of the human body at moment i to be H i0 ＝max(LH i0 ,RH i0 )。
(3.2) when the exoskeleton robot moves, one leg is a supporting leg, and the other leg is a swinging leg; the space correction parameter alpha is obtained through the following process:
(1) if H iR -H i0 > 0: the supporting leg landing height is higher than a preset value, and the step length is increased when the supporting leg is required to flatly pedal the ground at the sole, so that the aim of adjustment is fulfilled; if H iR -H i0 < 0: the supporting leg landing height is lower than a preset value, and the step length is reduced when the supporting leg is required to flatly pedal the ground at the sole, so that the aim of adjustment is fulfilled; whether above or below the preset value, the spatial modification parameters are designed to be
(2) If H iR -H i0 Approximately 0: this condition indicates that the user continues to walk at the original stride,
(IV) on-line moment generating module
Will beInput to an online moment generation module, and integrate a human-machine coupling dynamic parameter set S, and corrected by an impedance control system, a gravity online compensation system, a Golgi force centrifugal force and friction online compensation system to obtain a moment control value of the i+1 moment exoskeleton robot, wherein the moment control value is FA i+1 ＝{FA (i+1)j }. The method specifically comprises the following steps:
(4.1) the online gait track generation module predicts the value according to the online gait track of the exoskeleton robot at the moment i+1Calculating the joint angle +.1 at time i->And joint angular velocity>Combining the exoskeleton robot rigid body rotation matrix to obtain a minimum correlation matrix Y of the man-machine coupling dynamic parameter set S;
(4.2) calculating a moment control value tau=y×s of the exoskeleton robot at the time i+1, and taking the value as an initial value of the moment control value of the exoskeleton robot;
(4.3) impedance control regulator receptionCombination->Obtaining the moment correction quantity tau of the impedance control system 1_j Let the position correction coefficient be m p_j The speed correction coefficient is m v_j Moment adjusting value for adjusting joint impedance is +.>Then->Position correction coefficient m p_j And a velocity correction coefficient m v_j Set values based on gait characteristics and system constraints by the system;
(4.4) the position is to beThe gravity on-line compensation system is brought to calculate compensation moment +.>Speed of the speedOnline compensation system for centrifugal force and friction force brought into Golgi force to calculate compensation forceMoment->Calculating the moment control value of the exoskeleton robot at the moment i+1 as +.>
The impedance control system, the gravity online compensation system and the coriolis force centrifugal force and friction force online compensation system are obtained by adopting a general calculation method, wherein the impedance control system, the gravity online compensation system and the coriolis force centrifugal force and friction force online compensation system are existing mature technologies.
(V) Motor drive Module
The motor driving module executes the torque control value FA i+1 The exoskeleton robot is driven to move.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be appreciated by persons skilled in the art that the above embodiments are not intended to limit the invention in any way, and that all technical solutions obtained by means of equivalent substitutions or equivalent transformations fall within the scope of the invention.
Claims (3)
1. An online moment generator for passive training of an exoskeleton robot is a mechanical electronic power device mainly composed of a left hip, a left knee, a right hip and a right knee, each joint comprises a direct-current servo motor, a motor encoder, a Hall sensor and a harmonic speed reducing mechanism, the joints drive a coupling rod piece to move, and the coupling rod piece is combined with a strap fixing piece to enable a patient and the exoskeleton robot to synchronously move; the method is characterized in that: the online moment generator comprises a motion data acquisition and processing module, a man-machine coupling dynamics model based on a genetic algorithm, an online gait track generation module, an online moment generation module and a motor driving module;
the motion data acquisition and processing module acquires a detection value at the moment i when the exoskeleton robot carries a person to move, wherein the detection value comprises a pulse value A of a motor encoder ij And moment value F of Hall sensor ij Pulse value A based on motor encoder according to the relation between pulse and angle ij Calculating the actual angle value theta ij The method comprises the steps of carrying out a first treatment on the surface of the Where j represents a joint number, j=1 represents a left leg hip joint, j=2 represents a left leg knee joint, j=3 represents a right leg hip joint, and j=4 represents a right leg knee joint;
pulse value of i-moment exoskeleton robot is recorded as A i ＝{A ij Moment value of exoskeleton robot at moment i is F i ＝{F ij The position of the exoskeleton robot at the moment i is
Based onObtaining the corresponding speed ++under the equal sampling time interval through the equivalent differential operation>And acceleration-> And->Respectively is theta ij Is to be +.>The actual gait track of the exoskeleton robot at the moment i;
will beAnd F i Inputting the dynamic parameters of the exoskeleton robot into a human-machine coupling dynamic model, and carrying out optimization training on the dynamic parameters of the exoskeleton robot to obtain a human-machine coupling dynamic parameter set S which accords with the current human body;
the online gait track generation module generates a gait track according to the actual gravity center height H of the human body iR And the height H of the gravity center of the standard standing position of the human body i0 Calculating a spatial correction parameter alpha from the deviation between the two in the direction of gravitational acceleration, and using the pair of spatial correction parameters alphaCorrecting to obtain an online gait track predicted value +.>Online location prediction valueOn-line speed predictor +.>On-line acceleration predictor +.>The center of gravity of the human body is the center point of the human body pelvic bone; the space correction parameter alpha is obtained through the following process:
(1) if H iR -H i0 > 0: the supporting leg landing height is higher than a preset value, and the step length is increased when the supporting leg is required to flatly pedal the ground at the sole, so that the aim of adjustment is fulfilled; if H iR -H i0 < 0: the supporting leg landing height is lower than a preset value, and the step length is reduced when the supporting leg is required to flatly pedal the ground at the sole, so that the aim of adjustment is fulfilled; whether above or below the preset value, the spatial modification parameters are designed to be
(2) If H iR -H i0 Approximately 0: indicating that the user continues to walk in the original stride,
will beInput to an online moment generation module, and integrate a human-machine coupling dynamic parameter set S, and corrected by an impedance control system, a gravity online compensation system, a Golgi force centrifugal force and friction online compensation system to obtain a moment control value of the i+1 moment exoskeleton robot, wherein the moment control value is FA i+1 ＝{FA (i+1)j }；
The execution of the online moment generation module comprises the following execution steps:
(1) The online gait track generation module generates an online gait track prediction value of the exoskeleton robot according to the moment i+1Calculating the joint angle +.1 at time i->And joint angular velocity>Combining the exoskeleton robot rigid body rotation matrix to obtain a minimum correlation matrix Y of the man-machine coupling dynamic parameter set S;
(2) Calculating a moment control value tau=Y×S of the exoskeleton robot at the moment i+1 as an initial value of the moment control value of the exoskeleton robot;
(3) Impedance control regulator receptionCombination->Obtaining the moment correction quantity tau of the impedance control system 1_j Let the position correction coefficient be m p_j The speed correction coefficient is m v_j ThenPosition correction coefficient m p_j And a velocity correction coefficient m v_j Set values based on gait characteristics and system constraints by the system;
(4) Position is toThe gravity on-line compensation system is brought to calculate compensation moment +.>Speed +.>Calculating compensation moment by using centrifugal force and friction force online compensation system>Calculating the moment control value of the exoskeleton robot at the moment i+1 as +.>
The motor driving module executes the torque control value FA i+1 Driving the exoskeleton robot to move;
let exoskeleton robot thigh length L 1 The calf length is L 2 The method comprises the steps of carrying out a first treatment on the surface of the i moment of the actual height H of the center of gravity of the human body iR And the height H of the gravity center of the standard standing position of the human body i0 The method is respectively calculated by the following steps:
(1) according to the position of the exoskeleton robot at the moment iThe center of gravity heights of the left leg and the right leg are calculated as follows:
LH iR ＝L 1 ×sin(θ i1 )+L 2 ×sin(θ i1 +θ i2 )
RH iR ＝L 1 ×sin(θ i3 )+L 2 ×sin(θ i3 +θ i4 )
calculating the height of the actual gravity center of the human body as H iR ＝max(LH iR ,RH iR )；
(2) According to the online position predicted value of the exoskeleton robot at the moment iThe center of gravity heights of the left leg and the right leg are calculated as follows:
calculating the height of the gravity center of the standard standing position of the human body at moment i to be H i0 ＝max(LH i0 ,RH i0 )。
2. The online moment generator for passive training of an exoskeleton robot of claim 1, wherein: according to the boundary constraint of the start and the end of the position, the speed and the acceleration, the time sequence of the actual gait track can be obtained; moment value F of exoskeleton robot i The filtering process is performed using a zero phase shift butterworth low pass filter.
3. The online moment generator for passive training of an exoskeleton robot of claim 1, wherein: optimizing a man-machine coupling dynamics parameter set S by adopting a genetic algorithm, wherein parameters in the man-machine coupling dynamics parameter set S comprise: coupling member inertia, coupling member centroid, coupling member mass, joint coefficient of dynamic friction, joint coefficient of static friction, and motor inertia.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111442157.6A CN114129399B (en) | 2021-11-30 | 2021-11-30 | Online moment generator for passive training of exoskeleton robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111442157.6A CN114129399B (en) | 2021-11-30 | 2021-11-30 | Online moment generator for passive training of exoskeleton robot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114129399A CN114129399A (en) | 2022-03-04 |
| CN114129399B true CN114129399B (en) | 2024-04-12 |
Family
ID=80389791
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111442157.6A Active CN114129399B (en) | 2021-11-30 | 2021-11-30 | Online moment generator for passive training of exoskeleton robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114129399B (en) |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105213153A (en) * | 2015-09-14 | 2016-01-06 | 西安交通大学 | Based on the lower limb rehabilitation robot control method of brain flesh information impedance |
| CN105963100A (en) * | 2016-04-19 | 2016-09-28 | 西安交通大学 | Patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method |
| CN106110587A (en) * | 2016-08-11 | 2016-11-16 | 上海交通大学 | Lower limb exoskeleton rehabilitation system based on man-computer cooperation and method |
| CN108748147A (en) * | 2018-06-01 | 2018-11-06 | 清华大学深圳研究生院 | A kind of control system and method for ectoskeleton mechanical arm |
| CN108785997A (en) * | 2018-05-30 | 2018-11-13 | 燕山大学 | A kind of lower limb rehabilitation robot Shared control method based on change admittance |
| CN108888473A (en) * | 2018-05-22 | 2018-11-27 | 哈尔滨工业大学 | Joint of lower extremity based on wearable walk-aiding exoskeleton moves reproducing method |
| CN110215648A (en) * | 2019-06-28 | 2019-09-10 | 华中科技大学 | Ectoskeleton based on body gait motor coordination characteristic coordinates gait control method |
| CN110303471A (en) * | 2018-03-27 | 2019-10-08 | 清华大学 | Assistance exoskeleton control system and control method |
| CN110695959A (en) * | 2019-08-27 | 2020-01-17 | 成都锦江电子系统工程有限公司 | Exoskeleton robot and control system thereof |
| CN110711114A (en) * | 2019-10-17 | 2020-01-21 | 河北工业大学 | Exoskeleton active disturbance rejection control method based on dynamic model |
| CN111281743A (en) * | 2020-02-29 | 2020-06-16 | 西北工业大学 | Self-adaptive flexible control method for exoskeleton robot for upper limb rehabilitation |
| CN111546315A (en) * | 2020-05-28 | 2020-08-18 | 济南大学 | Robot flexible teaching and reproducing method based on human-computer cooperation |
| CN111904795A (en) * | 2020-08-28 | 2020-11-10 | 中山大学 | Variable impedance control method for rehabilitation robot combined with trajectory planning |
| CN112220650A (en) * | 2020-12-09 | 2021-01-15 | 南京伟思医疗科技股份有限公司 | Online step generation control system for exoskeleton robot contralateral training |
| CN112618283A (en) * | 2020-12-21 | 2021-04-09 | 南京伟思医疗科技股份有限公司 | Gait coordination power-assisted control system for exoskeleton robot active training |
| CN113230094A (en) * | 2021-05-10 | 2021-08-10 | 深圳市迈步机器人科技有限公司 | Single-leg exoskeleton robot and control method thereof |
| CN113305812A (en) * | 2021-06-29 | 2021-08-27 | 上海大学 | Bionic lower limb exoskeleton configuration and human-computer coupling gait trajectory optimization method thereof |
-
2021
- 2021-11-30 CN CN202111442157.6A patent/CN114129399B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105213153A (en) * | 2015-09-14 | 2016-01-06 | 西安交通大学 | Based on the lower limb rehabilitation robot control method of brain flesh information impedance |
| CN105963100A (en) * | 2016-04-19 | 2016-09-28 | 西安交通大学 | Patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method |
| CN106110587A (en) * | 2016-08-11 | 2016-11-16 | 上海交通大学 | Lower limb exoskeleton rehabilitation system based on man-computer cooperation and method |
| CN110303471A (en) * | 2018-03-27 | 2019-10-08 | 清华大学 | Assistance exoskeleton control system and control method |
| CN108888473A (en) * | 2018-05-22 | 2018-11-27 | 哈尔滨工业大学 | Joint of lower extremity based on wearable walk-aiding exoskeleton moves reproducing method |
| CN108785997A (en) * | 2018-05-30 | 2018-11-13 | 燕山大学 | A kind of lower limb rehabilitation robot Shared control method based on change admittance |
| CN108748147A (en) * | 2018-06-01 | 2018-11-06 | 清华大学深圳研究生院 | A kind of control system and method for ectoskeleton mechanical arm |
| CN110215648A (en) * | 2019-06-28 | 2019-09-10 | 华中科技大学 | Ectoskeleton based on body gait motor coordination characteristic coordinates gait control method |
| CN110695959A (en) * | 2019-08-27 | 2020-01-17 | 成都锦江电子系统工程有限公司 | Exoskeleton robot and control system thereof |
| CN110711114A (en) * | 2019-10-17 | 2020-01-21 | 河北工业大学 | Exoskeleton active disturbance rejection control method based on dynamic model |
| CN111281743A (en) * | 2020-02-29 | 2020-06-16 | 西北工业大学 | Self-adaptive flexible control method for exoskeleton robot for upper limb rehabilitation |
| CN111546315A (en) * | 2020-05-28 | 2020-08-18 | 济南大学 | Robot flexible teaching and reproducing method based on human-computer cooperation |
| CN111904795A (en) * | 2020-08-28 | 2020-11-10 | 中山大学 | Variable impedance control method for rehabilitation robot combined with trajectory planning |
| CN112220650A (en) * | 2020-12-09 | 2021-01-15 | 南京伟思医疗科技股份有限公司 | Online step generation control system for exoskeleton robot contralateral training |
| CN112618283A (en) * | 2020-12-21 | 2021-04-09 | 南京伟思医疗科技股份有限公司 | Gait coordination power-assisted control system for exoskeleton robot active training |
| CN113230094A (en) * | 2021-05-10 | 2021-08-10 | 深圳市迈步机器人科技有限公司 | Single-leg exoskeleton robot and control method thereof |
| CN113305812A (en) * | 2021-06-29 | 2021-08-27 | 上海大学 | Bionic lower limb exoskeleton configuration and human-computer coupling gait trajectory optimization method thereof |
Non-Patent Citations (2)
| Title |
|---|
| 基于人体运动能力的下肢外骨骼支撑相阻抗自调整控制方法研究;陈建华;李烨;王琦;穆希辉;兵工学报(第006期) * |
| 陈建华 ; 李烨 ; 王琦 ; 穆希辉.基于人体运动能力的下肢外骨骼支撑相阻抗自调整控制方法研究.兵工学报.2020,(第006期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114129399A (en) | 2022-03-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109568089A (en) | A kind of trailing type lower limb recovery robot by training paces system | |
| Yan et al. | Review of assistive strategies in powered lower-limb orthoses and exoskeletons | |
| CN111604890B (en) | Motion control method suitable for exoskeleton robot | |
| CN104027218B (en) | A kind of healing robot control system and method | |
| CN103040586A (en) | External skeleton robot for exercising lower limbs and exercise control method thereof | |
| CN102058464A (en) | Motion control method of lower limb rehabilitative robot | |
| EP3509558B1 (en) | Mobile weight-bearing powered orthosis device | |
| Al Rezage et al. | Fuzzy PID control of lower limb exoskeleton for elderly mobility | |
| Seo et al. | The development of two mobile gait rehabilitation systems | |
| CN112025682B (en) | Flexible exoskeleton robot control method and system based on muscle cooperation theory | |
| Wu et al. | Adaptive cooperative control of a soft elbow rehabilitation exoskeleton based on improved joint torque estimation | |
| Mun et al. | Development and evaluation of a novel overground robotic walker for pelvic motion support | |
| CN116766197A (en) | Hip joint exoskeleton power-assisted control method | |
| Wang et al. | Synchronized walking coordination for impact-less footpad contact of an overground gait rehabilitation system: NaTUre-gaits | |
| CN206183606U (en) | Low limbs ectoskeleton robot | |
| Mohamad et al. | Online gait generator for lower limb exoskeleton robots: Suitable for level ground, slopes, stairs, and obstacle avoidance | |
| Yang et al. | A centaur system for assisting human walking with load carriage | |
| CN114129399B (en) | Online moment generator for passive training of exoskeleton robot | |
| Li et al. | Development and evaluation of a lumbar assisted exoskeleton with mixed lifting tasks by various postures | |
| Kamnik et al. | Rehabilitation robot cell for multimodal standing-up motion augmentation | |
| He et al. | Review of Power-Assisted Lower Limb Exoskeleton Robot | |
| CN116531225A (en) | Rehabilitation training device, rehabilitation training control method and storage medium | |
| Wierciak et al. | Orthotic Robot as a Mechatronic System | |
| CN106377838A (en) | Functional muscle electrical stimulation-driven exoskeleton walking aiding system | |
| Wang et al. | Dynamics analysis and simulation of a new 6-DOF lower limb rehabilitation 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 |