CN108324503A - Healing robot self-adaptation control method based on flesh bone model and impedance control - Google Patents

Healing robot self-adaptation control method based on flesh bone model and impedance control Download PDF

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CN108324503A
CN108324503A CN201810218160.1A CN201810218160A CN108324503A CN 108324503 A CN108324503 A CN 108324503A CN 201810218160 A CN201810218160 A CN 201810218160A CN 108324503 A CN108324503 A CN 108324503A
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ipsilateral
muscle
impedance
upper limb
healing robot
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杜义浩
姚文轩
邱石
王浩
杨文娟
张宁宁
谢平
马俊霞
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Yanshan University
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    • 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
    • A61H1/0218—Drawing-out devices
    • 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
    • A61H1/0274—Stretching or bending or torsioning apparatus for exercising for the upper limbs
    • 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
    • A61H1/0274—Stretching or bending or torsioning apparatus for exercising for the upper limbs
    • A61H1/0277—Elbow
    • 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
    • A61H2201/16—Physical interface with patient
    • A61H2201/1657—Movement of interface, i.e. force application means
    • A61H2201/1659—Free spatial automatic movement of interface within a working area, e.g. Robot
    • 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
    • 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
    • A61H2230/00—Measuring physical parameters of the user
    • A61H2230/08—Other bio-electrical signals
    • A61H2230/085—Other bio-electrical signals used as a control parameter for the apparatus

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  • 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)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
  • Manipulator (AREA)

Abstract

A kind of healing robot self-adaptation control method based on flesh bone model and impedance control, acquisition patient's upper limb is good for side and the surface electromyogram signal of Ipsilateral obtains nervous activity model, establish human upper limb flesh bone model, with Kalman filter and Opensim software optimization flesh bone model parameters, it obtains upper limb and is good for side output torque and Ipsilateral output torque, Ipsilateral is obtained by mirror method and it is expected torque, obtains healing robot auxiliary torque；Upper limb mobility is built, carries out degree of fatigue classification using electromyography signal feature, adjustment Torque-adjusting realizes that healing robot auxiliary torque adaptively adjusts；It will obtain after desired value and the joint angles correction value of joint angles tracking practical referring to joint angles, after forward kinematics solution obtains terminal position in input position controller, realize the adaptive Shared control of healing robot, promote human-computer interaction level and the individual adaptability in Rehabilitation training process so that it is more compliant and safe and reliable that healing robot controls process.

Description

Healing robot self-adaptation control method based on flesh bone model and impedance control
Technical field
The present invention relates to robot control field more particularly to a kind of healing robot self-adaptation control methods.
Background technology
Currently, the control method of healing robot is broadly divided into two kinds of passive control and active control.Passive control uses Position control mode is fitted desired trajectory, realizes the Trajectory Tracking Control of healing robot.But passive control mode is only applicable in In Rehabilitation early stage, there are lack human-computer interaction function in individual adaptability difference and rehabilitation course；Active control root It can be divided into two kinds according to the difference of interactive signal：(1) interactive controlling based on force signal feedback.The most commonly used is impedance controls Method processed, it is considered to be best suited for one of the method for healing robot control.It is fixed by healing robot inverse dynamics model Functional relation between amount description end power and end movement trajector deviation, i.e., obtain its end power by force snesor The deviation of end movement track is obtained, and then determines actual motion track and is sent into positioner and realize healing robot Trajectory Tracking Control.But due to that can not establish accurate impedance Control Model, and the impedance parameter in model is fixed not Become, healing robot control is caused to be short of adaptive adjustment capability；(2) interactive controlling based on bioelectrical signals.Human body is given birth to Object electric signal is introduced into healing robot control, wherein what is be most widely used is surface electromyogram signal, specific there are two types of modes： 1) myoelectricity trigger-type：The motion intention of patient is obtained by recognizing myoelectricity feature, triggering healing robot is transported according to desired trajectory It is dynamic.But the online recognition rate of electromyography signal is not high, the action of especially fine Minor articulus, and real-time is unable to get effective guarantor Card；2) electromyography signal continuous feedback formula：Healing robot provides corresponding auxiliary force according to the variation of myoelectricity characteristic value, realizes health The continuous control of multiple robot movement locus, but its reliability and safety can not be effectively ensured, and being susceptible to surprisingly leads to two Secondary injury.
In conclusion currently also lacking a kind of better healing robot self-adaptation control method.
Invention content
Present invention aims at providing, a kind of raising human-computer interaction is horizontal, control process is reliably submissive, individual adaptability is strong The healing robot self-adaptation control method based on flesh bone model and impedance control.
To achieve the above object, the method for the invention includes the following steps：
Step 1, the electromyography signal of side and Ipsilateral is good for using myoelectricity collecting device acquisition patient's upper limb；It is set using capturing movement The standby joint angles signal for obtaining upper limb and being good for side and Ipsilateral；By analyzing electromyography signal and joint angles signal, it is defeated to obtain strong side Go out torque τHWith Ipsilateral output torque τS；
Step 2, by mirror method by being good for side output torque τHIt is τ ' to obtain Ipsilateral desired output torqueH, then patient can be obtained Required auxiliary force τassist=τ 'H-τS, i.e. healing robot desired output torque.
Step 3, Ipsilateral electromyography signal feature is extracted：Root-mean-square value RMS characterization muscle contribution rates ωj；Ipsilateral electromyography signal Muscle activity degree α is calculatedj(t)；Muscle contribution rate ωjWith muscle activity degree αj(t) upper limb mobility η is calculated；It will be upper Limb mobility η introduces impedance parameter and obtains impedance equation：Damping term coefficient BdWith stiffness term COEFFICIENT Kd；
Step 4, electromyography signal feature is extracted：Frequency of average power MPF and average instantaneous frequency MIF are for degree of fatigue point Grade simultaneously introduces Torque-adjusting τ in step 2 mirror image linkc, Ipsilateral desired output torque τ ' is finely tuned according to level of fatigueH；
Step 5, according to Ipsilateral output torque τSWith Ipsilateral desired output torque τ 'HDeviation, i.e. auxiliary force needed for patient τassistThe auxiliary of patient is adaptively given with healing robot, then patient's Active Compliance Control healing robot can be achieved.
Further, in step 1, upper limb electromyography signal is acquired by myoelectricity collecting device, obtains myoelectricity after pretreatment Signal amplitude ej(t), wherein j is jth block muscle.For the sake of simplicity, muscle number j in flesh bone model in the omission present invention, and calculate Obtain nervous activity model u (t).It can by contraction of muscle kinetic model and known human parameters and muscle activity degree α (t) Obtain muscular force Fmt(t).Coordinate the muscle tendon length l obtained by Opensim softwaresmt(t) and desired torque τdesiredCarry out lmt (t) it is expected expression argument estimate to obtain with the relationship of joint angles θ (t) (to avoid parameter from obscuring, joint angles θ (t) herein Q as in impedance equation), and to γ0、γ1, A and muscle maximum spontaneous contractions power Fmax, tendon total length lt(t), flesh is fine Tie up optimization lengthParameter optimization is carried out, optimal flesh bone model parameter and strong side output torque τ are obtainedHWith Ipsilateral output torque τS；
Further, in step 3, Ipsilateral electromyography signal feature is extracted：Root-mean-square value RMS is simultaneously normalized, and is used for Characterize muscle contribution rate ωj；Its muscle activity degree α is calculated using Ipsilateral electromyography signalj(t)；Utilize muscle contribution rate ωjAnd flesh Meat mobility αj(t) upper limb mobility η is calculated, and then adjusts impedance equation coefficient；
If impedance equation is：
In formula, qd, q be respectively healing robot reference locus and actual path；BdFor damped coefficient；KdFor rigidity system Number；
Upper limb mobility η is introduced into impedance equation parameter, and then realizes the adaptive adjustment of impedance parameter；
The impedance parameter BdAnd KdIt indicates as follows：
Bd=sig (λB·η)·B0
Kd=sig (λK·η)·K0
In formula, λBAnd λKRespectively damping term and stiffness term gain coefficient, B0And K0For initial impedance coefficient, BdAnd KdTo repair Impedance factor after just, sig (*) is sigmoid functions, and makes B as clip functionsdAnd KdVariation range is
It realizes and impedance parameter is adaptively adjusted according to upper limb mobility η.
Compared with prior art, the method for the present invention has the following advantages that：
1, directly according to patient's Ipsilateral desired output torque τ 'HWith Ipsilateral actual output torque τSDeviation τassistIt gives auxiliary It helps, i.e., needs to provide auxiliary force by patient；
2, the impedance parameter adaptively adjusted with ipsilateral upper limb mobility η is built, and then promotes Rehabilitation and trained Human-computer interaction in journey is horizontal；
3, according to the degree of fatigue fine tuning Torque-adjusting τ of patient's upper limb in rehabilitation trainingcSo that healing robot Individual adaptability enhances, while it is more compliant and safe and reliable to control process.
Description of the drawings
Fig. 1 is the control principle drawing of the method for the present invention.
Fig. 2 is the surface myoelectric distribution of electrodes schematic diagram of the method for the present invention.
Fig. 3 is the flesh bone model principle schematic of the method for the present invention.
Fig. 4 is the flesh bone model torque prediction curve of the method for the present invention.
Drawing reference numeral：2-1 is the long flesh electrode position of the bicipital muscle of arm, 2-2 is the long flesh electrode position of the triceps muscle of arm, and 4-1 is Opensim softwares actual torque, 4-2 are that flesh bone model predicts torque.
Specific implementation mode
The present invention will be further described below in conjunction with the accompanying drawings：
The method of the invention includes the following steps：
In step 1, upper limb surface electromyogram signal is acquired using Delsys four-point silver bar electrode equipments, and through 25Hz high passes Electromyography signal amplitude e (t) is obtained after filtering, rectification and 3Hz low-pass filtering, nerve is obtained after being inputted second order recursive filter Motility model u (t).Muscular force F is obtained by contraction of muscle kinetic model and muscle activity degree α (t)mt(t)。
The physiological parameters such as upper limb joint angle and weight, upper limb length are inputted into Opensim softwares, predict muscle tendon Length lmt(t) and desired torque τdesired, and then establish lmt(t) it is expected expression formula and estimated using Kalman filter progress parameter Meter, obtainsWith θ (t).With desired torque τdesiredWith actual torque τactualCarry out loss function minimum, optimization γ0、γ1, A and muscle maximum spontaneous contractions power Fmax, tendon total length lt(t), muscle fibre optimization lengthEtc. parameters, obtain Optimal flesh bone model parameter.By can then respectively obtain strong side output torque τ in line computation torqueHWith Ipsilateral output torque τS。
Step 2, by mirror method by being good for side output torque τHIt is τ ' to obtain Ipsilateral desired output torqueH, then patient can be obtained Required auxiliary force τassist=τ 'H-τS, i.e. healing robot desired output torque.
Step 3, upper limb surface electromyogram signal is acquired using Delsys four-point silver bar electrode equipments, and extracts Ipsilateral myoelectricity Signal characteristic：Root-mean-square value RMS is simultaneously normalized, for characterizing muscle contribution rate ωj；It is calculated using Ipsilateral electromyography signal Muscle activity degree αj(t)；Utilize muscle contribution rate ωjWith muscle activity degree αj(t) upper limb mobility η is calculated；
Upper limb mobility η is introduced into impedance equation parameter, and then realizes the adaptive adjustment of impedance parameter；
If impedance equation is：
In formula, qd, q be respectively healing robot reference locus and actual path；BdFor damped coefficient；KdFor rigidity system Number；
Upper limb mobility η is introduced into impedance equation parameter, and then realizes the adaptive adjustment of impedance parameter；
The impedance parameter BdAnd KdIt indicates as follows：
Bd=sig (λB·η)·B0
Kd=sig (λK·η)·K0
In formula, λBAnd λKRespectively damping term and stiffness term gain coefficient, B0And K0For initial impedance coefficient, BdAnd KdTo repair Impedance factor after just, sig (*) is sigmoid functions, and makes B as clip functionsdAnd KdVariation range is
It realizes and impedance parameter is adaptively adjusted according to upper limb mobility η.
Step 4, electromyography signal feature is extracted：Frequency of average power MPF and average instantaneous frequency MIF are for degree of fatigue point Grade simultaneously introduces Torque-adjusting τ in step 2 mirror image linkc, Ipsilateral desired output torque τ ' is finely tuned according to level of fatigueH, i.e. τ 'H= τH-τc；
Step 5, according to Ipsilateral output torque τSWith Ipsilateral desired output torque τ 'HDeviation, i.e. auxiliary force needed for patient τassistThe auxiliary of patient is adaptively given with healing robot, then patient's Active Compliance Control healing robot can be achieved.
Embodiment 1：
In conjunction with Fig. 1, for the control principle drawing of the present invention.Using typical double-closed-loop control structure.A is that position controls mould Block, B are location-based impedance control module, and C is impedance parameter update module.First, Delsys four-points silver bar electricity is utilized Pole equipment acquisition patient's upper limb is good for the surface electromyogram signal of side and Ipsilateral and carries out myoelectricity feature extraction, while passing through capturing movement Equipment acquires joint angles q ', and the data for acquiring certain time are used for model training：Flesh bone model is built based on electromyography signal, Muscle of upper extremity tendon length l is obtained using Opensim softwaresmt(t) unknown parameters such as, and with Kalman filter estimate its with Relationship between joint angles θ (t) further obtains actual torque by Opensim softwares and optimizes the other ginsengs of flesh bone model Number；Secondly, upper limb mobility is built based on muscle activity degree and muscle contribution degree, and is used for real-time update impedance parameter, simultaneously Degree of fatigue classification is carried out based on electromyography signal feature, and then adjusts Torque-adjusting τc, realize healing robot auxiliary torque τassistAdaptive adjustment；Finally, desired value q joint angles trackeddWith joint angles correction amount qeIt is compared, obtains reality Border refers to joint angles q, and after forward kinematics solution obtains terminal position in input position controller, realizes the essence to position q Really tracking, and then realize the adaptive Shared control of healing robot.
In conjunction with Fig. 2, the muscle that upper limb elbow joint completes flexion and extension is supported mainly to have the bicipital muscle of arm (long flesh, and brevis), the upper arm Oar flesh, the triceps muscle of arm (transversus, long flesh, musculus lateralis interni), anconeus etc. reduce system control and prolong while to ensure flesh bone model precision When, choose the long flesh of the bicipital muscle of arm and the long flesh of the triceps muscle of arm as electromyographic signal collection point, 2-1,2-2 institute in specific location such as Fig. 2 Show.
In conjunction with Fig. 3：It is flesh bone model principle schematic of the invention.Wherein, CE, PE, SE are respectively to shrink module, put down Row elastic module (passive module) and tendon；
In conjunction with Fig. 4, for the flesh bone model torque prediction curve of the present invention.4-1 is the actual forces that Opensim softwares obtain Square value, 4-2 are that flesh bone model predicts moment values.Normal form is acted in figure is：Right upper arm is motionless perpendicular to the ground, and right forearm is bent and stretched about 90 ° extremely parallel to the ground.Circulation experiment intercepts about 5s data.
Detailed process is as follows：
Upper limb surface electromyogram signal is acquired using Delsys myoelectricity collecting devices, and through 25Hz high-pass filterings, rectification and 3Hz Electromyography signal amplitude e (t) is obtained after low-pass filtering, and nervous activity model u (t) is obtained such as after being inputted second order recursive filter Under：
U (t)=ae (t-d)-b0·u(t-1)-b1·u(t-2) (1)
In formula, d postpones for electromyography signal, b0=γ0+γ1、b1=γ0-γ1、|γ0| ＜ 1, | γ1| ＜ 1, to avoid being System generates oscillation, enables | a-b0-b1|=1.
The non-linear relation that muscle activity degree α (t) and nervous activity model u (t) can be obtained by formula (1) is：
In formula,Characterize nonlinear degree.
Muscular force F is calculated by contraction of muscle kinetic modelmt(t)：
Fmt(t)=Ft(t)=Fmax·[f(l)·f(v)·α(t)+fp(l)]·cos(φ(t)) (3)
In formula, FmaxFor muscle maximum spontaneous contractions power, f (l), fp(l) be respectively CE and PE modules fascicle length letter Number, f (v) are the muscle fibers contract speed of CE modules, and φ (t) is pinniform angle, can be obtained by following formula：
In formula, φ0For in muscle fibre optimization lengthWhen pinniform angle angle, lm(t) it is length of the muscle fibre in t moment, It can be obtained by following formula,
In formula, lmt(t) it is muscle tendon length, lt(t) it is the total length of tendon,For in muscle activity degree α (t) Optimal fascicle length, can be obtained by following formula,
In formula, λ is that the percentage of optimal fascicle length changes.
Muscle maximum spontaneous contractions power F in formula (4), (5), (6)max, tendon total length lt(t), muscle fibre optimization lengthMuscle fibre optimization lengthWhen pinniform angle angle φ0, its parameter area can be obtained by human anatomy.
Upper limb flesh bone geometrical model：
In formula, r (t) is the arm of force.
τ=Fmt(t)·r(t) (8)
In formula, τ is torque.
By formula (7), (8) if it is found that muscle of upper extremity tendon length lmt(t) it is known that then known to output torque τ.
Upper limb is obtained using capturing movement equipment and is good for side and Ipsilateral joint angles signal, and by joint angles signal and patient The physiologic informations such as quality, upper limb length input in Opensim softwares, obtain lmt(t) and desired torque τdesired.Muscle tendon is long Spend lmt(t) it is expected that expression formula is：
lmt(t)=c0+c1·θ(t)+c2θ2(t) (9)
In formula, θ (t) is t moment joint angles.
Parameter Estimation is carried out to formula (9) using Kalman filter, obtains estimation parameterThen lmt(t) join Number estimation finishes.By establishing lmt(t) relational expression between θ (t) is to get to can pass through after joint angles θ (t)
It obtainsI.e. healing robot can track patient's upper extremity exercise with real-time online.
The electromyography signal that side and Ipsilateral are good for by upper limb obtains strong side output torque τH preWith Ipsilateral output torque τS pre.If real Border output torque is τactual, with the expectation torque τ obtained by Opensim softwaresdesiredCarry out parameter optimization.Structure loss letter Number：
Optimized parameter γ in flesh bone model can be obtained by minimizing formula (11)0、γ1, A and muscle maximum spontaneous contractions power Fmax, tendon total length lt(t), muscle fibre optimization length
Flesh bone model through parameter optimization can then respectively obtain strong side output torqueWith Ipsilateral output torqueIt can recognize For
Further by being good for side output torque τHCan mirror image obtain Ipsilateral desired output torque be τ 'H, then can obtain auxiliary needed for patient Power-assisted, that is, healing robot output torque is：
τassist=τ 'H-τS (12)
In formula, τ 'H=τH-τc, τcFor Torque-adjusting.
Impedance control is a kind of second order mass-damping-spring model, and equation is expressed as：
Since patient's upper limb active force is smaller, the variation of acceleration can be ignored, only consider the resistance in impedance Control Model Buddhist nun and stiffness term, formula (13) can be reduced to：
Upper limb mobility η is defined as
In formula,For the contribution rate of jth block muscle.
Damping term and stiffness term coefficient adjustment are
Bd=sig (λB·η)·B0 (16)
Kd=sig (λK·η)·K0 (17)
In formula, λBAnd λKRespectively damping term and stiffness term gain coefficient, B0And K0For initial impedance coefficient, BdAnd KdTo repair Impedance factor after just, sig (*) is sigmoid functions, and makes B as clip functionsdAnd KdVariation range is
Extract electromyography signal feature：Frequency of average power MPF and average instantaneous frequency MIF are for characterizing patient fatigue's degree Classification.It is specific as follows：
In formula, P (f) is power spectrum function, and f is frequency.
In formula, MIF (i) is i-th layer of average instantaneous frequency, ai(t) it is the amplitude of i-th of MIF component of electromyography signal, Hilbert transform, which is carried out, for MIF components obtains the instantaneous frequency of electromyography signal.
Pertinent literature shows that the increase with patient fatigue's degree, the MPF and MIF of electromyography signal are gradually reduced.Therefore, Patient fatigue's degree is classified using threshold method.
If MPF0, MIF0For the initial value of patient's myoelectricity characteristic quantity.
The first order：ε1<MPF<MPF0、μ1<MIF<MIF0
The second level：ε2<MPF<ε1、μ2<MIF<μ1
The third level：ε3<MPF<ε2、μ3<MIF<μ2
When MPF and MIF meets threshold condition simultaneously, it is classified as corresponding level of fatigue.
Torque-adjusting τ is provided to the evaluation of patient body situation according to doctorc, suffer from conjunction with above-mentioned tired stage division fine tuning Side desired output torque τ 'H, i.e. τ 'H=τH-τck, k=1,2,3 be level of fatigue.By real-time judgment upper limb level of fatigue, certainly Adapt to adjustment Torque-adjusting τck, prevent patient's secondary injury.
Auxiliary torque τ can be obtained by Torque-adjustingassist。
After being deformed to formula (14)：
The position correction amount q adaptively adjusted with muscle activity level and joint angles is generated by impedance equatione, pass through Desired value q of the position closed loop to position trackingdWith position correction amount qeIt is compared, obtains positive reference locations amount q, and through fortune It is input to positioner after dynamic normal solution, completes the accurate tracking to position q, and then realize the adaptive soft of healing robot Sequence system.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention It encloses and is defined, under the premise of not departing from design spirit of the present invention, technical side of the those of ordinary skill in the art to the present invention The various modifications and improvement that case is made should all be fallen into the protection domain of claims of the present invention determination.

Claims (3)

1. a kind of healing robot self-adaptation control method based on flesh bone model and impedance control, which is characterized in that the side Method includes the following steps：
Step 1, the electromyography signal of side and Ipsilateral is good for using myoelectricity collecting device acquisition patient's upper limb；It is obtained using capturing movement equipment Upper limb is taken to be good for the joint angles signal of side and Ipsilateral；By analyzing electromyography signal and joint angles signal, strong side power output is obtained Square τHWith Ipsilateral output torque τS；
Step 2, by mirror method by being good for side output torque τHIt is τ ' to obtain Ipsilateral desired output torqueH, then can obtain needed for patient Auxiliary force τassist=τ 'H-τS, i.e. healing robot desired output torque；
Step 3, Ipsilateral electromyography signal feature is extracted：Root-mean-square value RMS characterization muscle contribution rates ωj；Ipsilateral electromyography signal calculates To muscle activity degree αj(t)；Muscle contribution rate ωjWith muscle activity degree αj(t) upper limb mobility η is calculated；By upper limb activity Degree η introduces impedance parameter and obtains impedance equation：Damping term coefficient BdWith stiffness term COEFFICIENT Kd；
Step 4, electromyography signal feature is extracted：Frequency of average power MPF and average instantaneous frequency MIF are classified simultaneously for degree of fatigue Torque-adjusting τ is introduced in step 2 mirror image linkc, Ipsilateral desired output torque τ ' is finely tuned according to level of fatigueH, i.e. τ 'H=τH-τc；
Step 5, according to Ipsilateral output torque τSWith Ipsilateral desired output torque τ 'HDeviation, i.e. auxiliary force τ needed for patientassist The auxiliary of patient is adaptively given with healing robot, then patient's Active Compliance Control healing robot can be achieved.
2. the healing robot self-adaptation control method according to claim 1 based on flesh bone model and impedance control, It is characterized in that, in step 1, upper limb electromyography signal is acquired by myoelectricity collecting device, obtains electromyography signal amplitude after pretreatment ej(t), wherein j is jth block muscle；Muscle number j in flesh bone model is omitted, and nervous activity model u (t) is calculated；By flesh Meat Contraction Kinetics model and known human parameters and muscle activity degree α (t) can obtain muscular force Fmt(t)；Cooperation by The muscle tendon length l that Opensim softwares obtainmt(t) and desired torque τdesiredCarry out lmt(t) it is expected expression argument estimation The relationship with joint angles θ (t) is obtained, joint angles θ (t) is the q in impedance equation herein, and to γ0、γ1, A and muscle Maximum spontaneous contractions power Fmax, tendon total length lt(t), muscle fibre optimization lengthParameter optimization is carried out, optimal flesh bone mould is obtained Shape parameter and strong side output torque τHWith Ipsilateral output torque τS。
3. the healing robot self-adaptation control method according to claim 1 based on flesh bone model and impedance control, It is characterized in that：In step 3, Ipsilateral electromyography signal feature is extracted：Root-mean-square value RMS is simultaneously normalized, for characterizing muscle Contribution rate ωj；Its muscle activity degree α is calculated using Ipsilateral electromyography signalj(t)；Utilize muscle contribution rate ωjWith muscle activity degree αj(t) upper limb mobility η is calculated, and then adjusts impedance equation coefficient；
If impedance equation is：
In formula, qd, q be respectively healing robot reference locus and actual path；BdFor damped coefficient；KdFor stiffness coefficient；
Upper limb mobility η is introduced into impedance equation parameter, and then realizes the adaptive adjustment of impedance parameter；
The impedance parameter BdAnd KdIt indicates as follows：
Bd=sig (λB·η)·B0
Kd=sig (λK·η)·K0
In formula, λBAnd λKRespectively damping term and stiffness term gain coefficient, B0And K0For initial impedance coefficient, BdAnd KdIt is revised Impedance factor, sig (*) is sigmoid functions, and makes B as clip functionsdAnd KdVariation range is
It realizes and impedance parameter is adaptively adjusted according to upper limb mobility η.
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