CN104492066A - Task-oriented active training control method and corresponding rehabilitation robot - Google Patents

Abstract

The invention discloses a task-oriented active training control method applied to a rehabilitation robot. The rehabilitation robot is provided with a sensor, a control system and a driving mechanism, the control system is used for receiving a signal generation control command collected by the sensor and sending the control command to the driving mechanism, and the driving mechanism can be worn on the body part of a patient and is used for receiving the control command and applying and regulating acting force on the body part of the patient according to the control command so as to control rehabilitation training of the patient. The control method includes the steps: S1, detecting an active movement signal of the body part of the patient; S2, regulating the acting force applied by the driving mechanism of the rehabilitation robot on the body part of the patient according to the active movement signal of the body part of the patient.

Description

Task orientation formula active training control method and corresponding healing robot

Technical field

The present invention relates to a kind of task orientation formula active training control method and corresponding healing robot, belong to rehabilitation appliances technical field.

Background technology

Apoplexy and spinal cord injury cause two of lower extremity motor function obstacle large main causes.Apoplexy, also known as cerebral apoplexy, is a kind of acute cranial vascular disease, and it is fallen ill suddenly and is difficult to prediction; permanent cerebral nerve can be caused to damage; disability rate remains high always, and the patient of survival usually can suffer the torment of sequelae, hemiplegia to be exactly wherein common one.Spinal cord injury is normally caused by serious spinal trauma, and various contingency all likely causes the generation of spinal cord injury.The same with apoplexy, spinal cord injury has very high disability rate, may cause the illness such as paraplegia, tetraplegia, the serious daily life campaign hindering patient.Due to high incidence and the frequent various contingencies occurred of cranial vascular disease, the paralysed patient quantity of China grows with each passing day.For paralysed patient, after the Clinical Processing through acute stages such as such as operations, rehabilitation becomes main a kind of auxiliary treatment means.According to neural plasticity principle, the extremity motor function that it can help patient to recover impaired to a certain extent, relearns ADL, thus as much as possible helps patient to return normal life.

Although training has facilitation to neural plasticity, the extremity motor function that paralysed patient recovers impaired to a certain extent can be helped, but rehabilitation is all a quite long-term time-continuing process usually, sometimes even may run through the time of patient lifetime.

In traditional rehabilitation means, the training of patient mainly relies on Physical Therapist one to one sometimes or even many-to-one manually auxiliary, and generally compare loss time and medical resource, cost is also therefore relatively high; In addition, because active training is difficult to manual realization, so the motion of patient body part is all passive substantially, Training strategy is more single, deceives and turns round and look at concrete condition and adopt machine-made passive exercise pattern to be unfavorable for the rehabilitation of patient.Because motion process is primarily of Physical Therapist's Non-follow control, therefore rehabilitation training is very easily subject to the experience of Physical Therapist, the impact of the subjective factor such as mood and muscle power, the movement locus of patient body part, mutual being often difficult to be applied between dynamics on patient body part and patient-Physical Therapist keeps good uniformity, and this subjectivity and inconsistency often cause the not reproducible of rehabilitation efficacy.

Healing robot is that the patient body part impaired with motor function interacts, and patient is the object possessing autokinetic movement consciousness, current healing robot is in passive exercise mode, be difficult to the interactive controlling realized between robot and patient, if patient body part is because of spasm, the abnormal muscle activity such as to tremble and creating antagonism with robot, patient body part may suffer secondary damage, increases the weight of the condition of the injury; And passive exercise is difficult to the active movement intention obtaining patient, causes patient to participate in enthusiasm and greatly reduces, affect rehabilitation training effect.

Summary of the invention

(1) technical problem that will solve

The object of the invention is the defect adopting passive exercise control method for existing rehabilitation equipment, propose a kind of task orientation formula active training control method, to provide safe, comfortable, natural and to possess the training environment of active compliance, improve the training enthusiasm of patient, improve the efficiency of rehabilitation.

(2) technical scheme

The present invention proposes a kind of task orientation formula active training control method, be applied in healing robot, described healing robot has sensing device, control system and driving mechanism, the signal generation control instruction that control system gathers for receiving sensing device, and control instruction is sent to driving mechanism, driving mechanism can wear the body part in patient, receive described control instruction, apply according to control instruction and regulate the active force to patient body part, to control the rehabilitation training of patient, described control method comprises the steps:

The active movement signal of S1, detection patient body part;

S2, active movement signal according to patient body part, regulate the driving mechanism of healing robot to the active force of patient body part.

According to a kind of detailed description of the invention of the present invention, described active movement signal is the active joint moment of patient, and described step S2 comprises:

S21, set up the directed walk corresponding with the motor task of patient, its closest approach of distance on the joint position of body part and this directed walk according to patient, sets up a virtual channel as reference position around specified path;

S22, convert active joint moment to corresponding actual motion, according to the position calculation position of virtual tunnel, speed and acceleration adjustment amount, obtain the control instruction of position, speed and acceleration, and it can be used as control signal to control driving mechanism;

S23, the active joint moment of patient body part to be decomposed along just tangential and positive normal, according to two components and the position offset of active moment, adopt the method for fuzzy logic to regulate impedance parameter.

According to a kind of detailed description of the invention of the present invention, described step S21 comprises:

Step S211: specify directed walk by a two-dimensional sequence Q in the joint space shown in following formula _srepresent:

Q _s：{q _i＝[q _i，1q _i，2] ^T|i＝1，...，L}

Wherein, q _irepresent Q _si-th element, q _{i, j}represent q _ia jth element, L is Q _sthe number of middle element;

Step S212: by intermediate variable equally spaced uniform discrete in its domain of definition, obtain terminal position sequence by parametrization equation, utilize inverse kinematics equation that end sequence is transformed into joint space;

Step S213: find the point that distance current location in position sequence is nearest

I ^*=argmin _{i=1 ..., L-1}|| q _i-q||, wherein i ^*represent the sequence number of closest approach;

Step S214: use closest approach with the next point in sequence determine straight line, this straight line is used as the tangent line at closest approach place on specified path approx:

$q_p=\left\{\begin{array}{cc}{\left[q_{i^{*},1]}{q}_2\right]}^T,& q_{i^{*}+\mathrm{1,1}}=q_{i^{*},1}\\ {\left[\frac{q_1+k(q_2-b)}{k^2+1}\frac{b+k(q_1+{\mathrm{kq}}_2)}{k^2+1}\right]}^T,& \mathrm{otherwise}\end{array}\right.$

Wherein $k=(q_{i^{*}+\mathrm{1,2}}-q_{i^{*},2})/(q_{i^{*}+\mathrm{1,1}}-q_{i^{*},1})$ Represent straight line slope, represent the intercept of this straight line; q _pbe used to replace be used as the closest approach of distance current joint position on specified path;

Step S215: according to its closest approach of distance on real-time joint position and specified path, determine the reference position q in training process _r:

$q_r=\left\{\begin{array}{cc}{q}_p+\frac{w_t}{2}{\left|\right|q_e\left|\right|}^{-1}& q_e,\left|\right|q_e\left|\right|>\frac{w_t}{2}\\ q,& \mathrm{otherwise}\end{array}\right.,$

Thus around specified path, establish a virtual tunnel to limit the active movement of patient.

According to a kind of detailed description of the invention of the present invention, described step S22 comprises:

Step S221: by reverse impedance equation, according to the joint moment τ that patient self produces _hproduce position, speed and acceleration adjustment amount, revise reference locus, to regulate the Interaction Force of patient body part and driving mechanism;

Step S222: after utilizing position, speed, acceleration adjustment amount to revise reference movement locus, obtain position, speed and acceleration instruction, and it can be used as the reference signal of position servo control.

According to a kind of detailed description of the invention of the present invention, described step S23 comprises:

Step S231: according to the directed walk of specifying and real-time joint position, the active moment of patient body part is decomposed into two components:

τ _h＝τ _td _t+τ _ed _e，

Wherein, d _trepresent and specify subpoint q on directed walk _pthat locates is just tangential, d _erepresent subpoint place positive normal, q is departed from it and position _edirection consistent, point to current actual positions by subpoint, τ _tand τ _erepresent τ respectively _halong the component of above-mentioned both direction;

Step S232: the tangent line of the subpoint on specified path can by straight line be similar to, the just tangential column vector in order to lower 2 × 1 represents:

$d_t={\left|\right|q_{i^{*}+1}-q_{i^{*}}\left|\right|}^{-1}(q_{i^{*}+1}-q_{i^{*}});$

Step S233: the initiatively projection of joint moment on positive normal, under nonsingular state, namely || q _e|| ≠ 0, time, it can according to q _etry to achieve, otherwise, be set to d _tone of them normal direction:

$d_e=\left\{\begin{array}{cc}{\left|\right|q_e\left|\right|}^{-1}{q}_e,& \left|\right|q_e\left|\right|\≠0\\ {[d_{t2}-d_{t1}]}^T,& \mathrm{otherwise}\end{array}\right.,$

Wherein, d _tirepresent d _ti-th element, q _ebe orthogonal to straight line d can be obtained _t⊥ d _e, || d _t||=1 and || d _e||=1, wherein ⊥ is orthogonal symbols, thus initiatively joint moment can respectively by formula in the projection just tangentially and on positive normal with try to achieve;

Step S234: by decomposing initiatively moment, obtain patient in the motion intention tangentially and in normal direction, when patient is intended to antagonism specified path, increases the impedance of active movement, otherwise reduces impedance.

According to a kind of detailed description of the invention of the present invention, in step S234, fuzzy logic is adopted to carry out impedance adjustment, initiatively two component τ of joint moment _tand τ _eand positional offset amount || q _e|| as the input variable of fuzzy logic, wherein || || represent Euclid 2-norm operator.

(3) beneficial effect

Task orientation formula active training control method of the present invention obtains patient's active movement intention, can guarantee the movement locus of rehabilitation training, is applied to dynamics on patient body part and possesses good uniformity alternately between patient and robot.

The present invention can for patient provide one safe, comfortable, natural and possess the training environment of active compliance, avoid patient body part due to spasm, the abnormal secondary damage caused of muscle activity such as tremble.

The present invention focuses on the participating actively consciousness exciting patient in rehabilitation exercise process, encourages patient to control the contraction of muscle of patient body part energetically, effectively improves the rehabilitation efficacy of training.

Accompanying drawing explanation

Fig. 1 is the flow chart being applied to the task orientation formula active training control method of healing robot of the present invention;

Fig. 2 is the closest approach searching distance current location on specified path;

Fig. 3 is the virtual channel around specified path;

Fig. 4 is location-based impedance Control double circle structure;

Fig. 5 is for initiatively moment decomposition--current location is positioned at specified path inside;

Fig. 6 is that initiatively moment is divided--current location is positioned at specified path outside;

Fig. 7 is the membership function of input variable obfuscation;

Fig. 8 A ~ 8D is task orientation formula active training result under fuzzy logic.

Detailed description of the invention

Healing robot has sensing device, control system and driving mechanism, the signal generation control instruction that control system gathers for receiving sensing device, and control instruction is sent to driving mechanism, driving mechanism can wear the body part in patient, receive described control instruction, apply and regulate the active force to patient body part according to control instruction, to control the rehabilitation training of patient.Because the mechanical realization of healing robot and the control method of routine thereof are prior aries, therefore do not illustrate in the description of the invention.

What the present invention proposed is a kind of task orientation formula active training control method being applied to healing robot.Fig. 1 is the flow chart being applied to the task orientation formula active training control method of healing robot of the present invention, and as shown in Figure 1, the method mainly comprises the steps:

The active movement signal of S1, detection patient body part.

The active movement signal of the body part of patient can be the active joint moment of patient, i.e. the moment in the joint relative to this part that produces when active movement of the body part of patient.

S2, active movement signal according to patient body part, regulate the driving mechanism of healing robot to the active force of patient body part.

Active movement signal due to patient body part embodies patient's active movement intention, the movement locus of rehabilitation training can be regulated according to this intention pointedly, make to be applied to dynamics on patient body part and possess good uniformity alternately between patient and robot.

When active movement signal is the active joint moment of patient, step S2 can comprise as follows step by step:

S21, set up the directed walk corresponding with the motor task of patient, its closest approach of distance on the joint position of body part and this directed walk according to patient, sets up a virtual channel as reference position around specified path.

In step S21, described motor task is predefined as a directed walk, only comprises positional information and does not have speed and acceleration information; Described motor task the most directly represents the path of body part (such as lower limb) end in normally cartesian space, typically such as { x _s(v) | v ∈ Ω _s, wherein, v is intermediate variable, Ω _sit is the domain of definition; For obtaining joint space position sequence, first by intermediate variable equally spaced uniform discrete in its domain of definition, then obtaining terminal position sequence by parametrization equation, finally utilizing reverse movement equation that end sequence is transformed into joint space; When task training is a shuttling movement, for ensureing sequential element uniqueness, Ω _sto be set in the period of motion, when training mission is a back and forth movement, Ω _sto be set in half period of motion;

S22, convert active joint moment to corresponding actual motion, according to the position calculation position of virtual tunnel, speed and acceleration adjustment amount, obtain the control instruction of position, speed and acceleration, and it can be used as control signal to control driving mechanism.

In step S22, utilize impedance adjustment, convert the active joint moment embodying patient motion intention to corresponding actual motion: in joint space, adopt location-based impedance adjustment, realized by double-closed-loop control structure, impedance Control outer shroud produces the adjustment amount of position, speed and acceleration by reverse impedance equation, revise reference locus, regulate the Interaction Force of the driving mechanism of patient body part and rehabilitation equipment; Set up the man-machine interface possessing active compliance; make the moment that the motion Initial adaption patient body part of driving mechanism produces; guarantee that patient comfort completes active training naturally; even if in emergency; abnormal movement-the spasm of such as body muscle, to tremble, also can guarantee patient's safety in the training process

S23, the active joint moment of patient body part to be decomposed along just tangential and positive normal, according to two components and the position offset of active moment, adopt the method for fuzzy logic to regulate impedance parameter.

Step S23 sets up self adaptation active compliance environment, with in the training process for patient provides tactile feedback, encourages patient to move according to appointment directed walk; First by the active joint moment of patient body part, along both direction-just, tangential and positive normal decomposes, and then according to two components and the position offset of active moment, adopts the method adjustment impedance parameter of fuzzy logic.

For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in further detail.

This embodiment in conjunction with treadmill movement as training mission, can for patient provide one safe, comfortable, natural and possess the training environment of active compliance, avoid patient body part due to spasm, the abnormal secondary damage caused of muscle activity such as tremble; Focus on the participating actively consciousness exciting patient in rehabilitation exercise process, encourage patient to control the contraction of muscle of patient body part energetically, the rehabilitation efficacy of training can be improved, possess rehabilitation training, prevent the functions such as secondary damage.

Step S21: described motor task is predefined as a directed walk, only comprises positional information and does not have speed and acceleration information; Described motor task the most directly represents the path of lower limb end in normally cartesian space, typically such as { x _s(v) | v ∈ Ω _s, wherein, v is intermediate variable, Ω _sit is the domain of definition.The terminal end path x of treadmill movement system _sv () is by following parametrization the Representation Equation:

$x_s\left(v\right)=\left[\begin{array}{c}0.1\cos \left(v\right)+0.62\\ -0.1\sin \left(v\right)\end{array}\right],v\∈\left[\mathrm{0,2}\mathrm{\π}\right)$

This path only comprises movement position and directional information, is a clockwise circumference, and the center of circle is [0.62,0], and radius is 0.1m.For realizing task orientation formula active training, need terminal end path to be transformed into joint space position sequence.For this reason, first by the domain of definition [0 of terminal end path, 2 π) discretely equably turn to 5000 points, obtain the sequence of a strictly monotone increasing, then these 5000 points are substituted into above formula and try to achieve the terminal position sequence that comprises 5000 elements, convert terminal position sequence to joint position sequence finally by inverse kinematics.Obviously the joint position sequence Q obtained thus _scome from smooth terminal end path curve, 5000 elements are also enough intensive.

Step S211: specify directed walk by a two-dimensional sequence Q in the joint space shown in following formula _srepresent:

Q _s：{q _i＝[q _i，1q _i，2] ^T|i＝1，...，L}

Wherein, q _irepresent Q _si-th element, q _{i, j}represent q _ia jth element, L is Q _sthe number of middle element, in order to represent that the joint position sequence of specified path is that the smooth curve discretization can led by single order obtains, and all elements in sequence is all unique; Training starts front joint position and is initialized as Q _sstarting point q ₀, joint velocity and acceleration are all initialized as 0;

Step S212: by intermediate variable equally spaced uniform discrete in its domain of definition, obtain terminal position sequence by parametrization equation, utilize inverse kinematics equation that end sequence is transformed into joint space;

Step S213: in training process, the real time position of task path and the system of specifying is depended in the reference position in each moment; Find the point that distance current location in position sequence is nearest

i ^*＝argmin _{i＝1，...，L-1}||q _i-q||

Wherein i ^*represent the sequence number of closest approach;

Step S214: closest approach with the next point in sequence 2 determine unique straight line, and because position sequence is that the smooth curve discretization can led by single order obtains, thus when the element in sequence is enough intensive, this straight line can be used as the tangent line at closest approach place on specified path approx; As shown in Figure 2, dot represents a part of position sequence on specified path, and large circle point represents the joint position of current reality, and orbicular spot represents that a q is at straight line on projection, q _prepresent that current location q is at straight line on projection:

$q_p=\left\{\begin{array}{cc}{\left[q_{i^{*},1]}{q}_2\right]}^T,& q_{i^{*}+\mathrm{1,1}}=q_{i^{*},1}\\ {\left[\frac{q_1+k(q_2-b)}{k^2+1}\frac{b+k(q_1+{\mathrm{kq}}_2)}{k^2+1}\right]}^T,& \mathrm{otherwise}\end{array}\right.$

Wherein $k=(q_{i^{*}+\mathrm{1,2}}-q_{i^{*},2})/(q_{i^{*}+\mathrm{1,1}}-q_{i^{*},1})$ Represent straight line slope, represent the intercept of this straight line; q _pbe used to replace be used as the closest approach of distance current joint position on specified path, the necessity doing so to replace is, when q distance specified path is sufficiently near, when the element especially in sequence is relatively sparse, to distance will arrive close to being even less than it distance, cause near-tangential with approximate normal no longer meet approximately perpendicular relation, thus considerable influence is produced to active moment afterwards.

Step S215: according to its closest approach of distance on real-time joint position and specified path, determine the reference position q in training process _r:

$q_r=\left\{\begin{array}{cc}{q}_p+\frac{w_t}{2}{\left|\right|q_e\left|\right|}^{-1}& q_e,\left|\right|q_e\left|\right|>\frac{w_t}{2}\\ q,& \mathrm{otherwise}\end{array}\right.,$

Thus around specified path, establish a virtual tunnel to limit the active movement of patient, as shown in Figure 3, middle line represents desirable motion path, and external boundary represents the outer wall in tunnel, and inner boundary represents inwall.Wherein, w _trepresent the width in tunnel, it is a constant, is set to 0.04m in this tunnel width.Q _e=q-q _prepresent that position is departed from, it is the column vector of 2 × 1; Because of q _pbe the subpoint of q on tangent line, thus position is departed from tangent line mutually orthogonal; If current location is positioned at tunnel internal, so reference position is current location, otherwise reference position is just arranged in tunnel wall.

Step S22: utilize impedance adjustment, converts the active joint moment embodying patient motion intention to corresponding actual motion: in joint space, adopt location-based impedance adjustment, realized, as shown in Figure 4 by double-closed-loop control structure.Impedance Control outer shroud produces the adjustment amount of position, speed and acceleration by reverse impedance equation, revises reference locus, regulates the Interaction Force of lower limb and pedipulator; Set up the man-machine interface possessing active compliance; make the moment that the motion Initial adaption lower limb of pedipulator produce; guarantee that patient comfort completes active training naturally; even if in emergency; abnormal movement-the spasm of such as lower limb muscles, to tremble, also can guarantee patient's safety in the training process.

Step S221: impedance Control, by reverse impedance equation, shrinks the joint moment τ produced according to lower limb muscles _hproduce position, speed and acceleration adjustment amount, revise reference locus, reach the object of the Interaction Force regulating lower limb and pedipulator;

Step S222: after utilizing position, speed, acceleration adjustment amount to revise reference movement locus, obtain position, speed and acceleration instruction, and it can be used as the reference signal of position servo control, position control is by the PD algorithm realization with BP neural networks compensate; When lower limb do not produce any active moment, i.e. τ _h=0, system will keep inactive state at tunnel internal, and when system motion is to tunnel outer, elasticity wall can be attempted to be withdrawn into tunnel internal.

Step S23: set up self adaptation active compliance environment, with in the training process for patient provides tactile feedback, encourages patient to move according to appointment directed walk; First by the active joint moment of patient's lower limb, along both direction-just, tangential and positive normal decomposes, and then according to two components and the position offset of active moment, adopts the method adjustment impedance parameter of fuzzy logic.

Step S231: be the motion intention of clear and definite patient, according to the directed walk of specifying and real-time joint position, the active moment of lower limb will be broken down into two components:

τ _h＝τ _td _t+τ _ed _e

Wherein, d _trepresent and specify subpoint q on directed walk _pthat locates is just tangential, and it is actually the designated movement direction at subpoint place on task path, d _erepresent subpoint place positive normal, q is departed from it and position _edirection consistent, point to current actual positions by subpoint, τ _tand τ _erepresent τ respectively _halong the component of above-mentioned both direction; If τ _tbe one on the occasion of, show the positive direction of the motion intention of patient on tangential along specified path, negative value then shows along negative direction; If τ _ebe greater than zero, show that the motion intention of patient in normal direction is away from specified path, be less than zero and represent that intention is close; Null value on any one direction represents that the party does not upwards have active movement to be intended to, as shown in Figure 5, current physical location is positioned at the inside of specified path, initiatively moment is negative at tangent component upwards, represent that patient is intended to resist the direction of specifying and moves, initiatively the component of moment on positive normal is just, represents that patient is intended to principle specified path.As shown in Figure 6, current physical location is positioned at outside specified path, and initiatively moment is just at tangent component upwards, represents that patient's intention is moved along the direction of specifying, initiatively the component of moment on positive normal is negative, represents that patient is intended near specified path.

Step S232: the tangent line of the subpoint on specified path can by straight line be similar to, therefore the just tangential column vector in order to lower 2 × 1 represents:

$d_t={\left|\right|q_{i^{*}+1}-q_{i^{*}}\left|\right|}^{-1}(q_{i^{*}+1}-q_{i^{*}})$

Because each element in position sequence is unique, so always set up, above formula there will not be unusual state;

Step S233: the initiatively projection of joint moment on positive normal, under nonsingular state, namely || q _e|| ≠ 0, time, it can according to a _etry to achieve, otherwise, be set to d _tone of them normal direction:

$d_e=\left\{\begin{array}{cc}{\left|\right|q_e\left|\right|}^{-1}{q}_e,& \left|\right|q_e\left|\right|\≠0\\ {[d_{t2}-d_{t1}]}^T,& \mathrm{otherwise}\end{array}\right.$

Wherein, d _tirepresent d _ti-th element.Q _ebe orthogonal to straight line d can be obtained _t⊥ d _e, || d _t||=1 and || d _e||=1, wherein ⊥ is orthogonal symbols, thus initiatively joint moment can respectively by formula in the projection just tangentially and on positive normal with try to achieve.

Step S234: by decomposing initiatively moment, obtain patient in the motion intention tangentially and in normal direction, when patient is intended to antagonism specified path, increases the impedance of active movement, otherwise reduces impedance, thus set up an adaptive haptic interface; Fuzzy logic is adopted to carry out impedance adjustment, initiatively two component τ of joint moment _tand τ _eand positional offset amount || q _e|| as the input variable of fuzzy logic, wherein || || represent Euclid 2-norm operator; Three input variables define three fuzzy set: N (Negative), Z (Zero) and P (Positive) separately.As shown in Figure 7, Triangleshape grade of membership function is used to carry out obfuscation to input variable, in figure, τ _tmin, τ _minwith || q _e|| _minrepresent the lower limit of input variable, and τ _tmax, τ _emaxwith || q _e|| _maxthen represent its upper limit, its value is as shown in table 1.

Table 1 pair fuzzy logic input variable carries out the upper lower limit value of obfuscation

τ tmax	τ emax	||q e|| max	τ tmin	τ emin	||q e|| min
						8(Nm)	40(Nm)	0.03(rad)	-8(Nm)	-40(Nm)	-0.03(rad)

Step S235: adopt zeroth order Sugeno model as fuzzy reasoning method, the output item of fuzzy rule is a constant, as shown in table 2.Wherein, z represents the output variable of fuzzy rule, and U represents fuzzy complete or collected works, with represent the supplementary set of N and Z respectively.

Table 2 fuzzy inference rule

Because the output of every bar fuzzy rule is the real number determined, adopt weighted mean method to carry out defuzzification to fuzzy logic output variable at this, obtain well-determined output quantity z; The output of every rule is using the degree of membership of this rule self as weight:

$z=\frac{{\mathrm{\Σ}}_{j=1}^6\mathrm{\μ}\left(z_j\right)z_j}{{\mathrm{\Σ}}_{j=1}^6\mathrm{\μ}\left(z_j\right)}$

Wherein, z _jrepresent the output of jth rule, use Zadeh operator to derive the degree of membership of this rule:

μ(z _j)＝min(μ _j(τ _t)，μ _j(τ _e)，μ _j(||q _e||)

Wherein μ _j(τ _t), μ _j(τ _e) and μ _j(|| q _e|| be illustrated respectively in input variable τ in regular j _t, τ _ewith || q _e||.Impedance parameter is calculated as follows:

$\left\{\begin{array}{c}M={\mathrm{zM}}_{\max }+(1-z)M_{\min }\\ B={\mathrm{zB}}_{\max }+(1-z)B_{\min }\\ K={\mathrm{zK}}_{\max }(1-z)K_{\min }\end{array}\right.$

Wherein, coefficient z is an arithmetic number being less than or equal to 1, the positive definite diagonal matrix M of 3 × 3 _max, B _maxand K _maxrepresent the upper limit of impedance parameter, M _min, B _minand K _minrepresent its lower limit, be all the positive definite diagonal matrix of 3 × 3, its upper lower limit value is as shown in table 3.

Table 3 impedance parameter upper lower limit value

For avoiding the vibration of movement position, impedance parameter must meet inequality wherein m _i, b _ii-th diagonal element of M, B and K is represented respectively with k; Therefore, the bound of impedance parameter meets constraint:

$\left\{\begin{array}{c}{b}_{i\max }=3.1\sqrt{m_{i\max }{k}_{i\max }}\\ b_{i\min }=3.1\sqrt{m_{i\min }{k}_{i\min }}\end{array}\right.$

Wherein, m _imax, b _imaxand k _imaxrepresent M respectively _max, B _maxand K _maxi-th diagonal element, m _imin, b _iminand k _iminrepresent M respectively _min, B _minand K _mini-th diagonal element.

Joint space puts on the active moment τ of pedipulator _hform by two, be respectively tangentially with the moment of normal direction, as follows:

Wherein direction vector d _c=[d _t2-d _t1] ^t, it is by just tangential d _tturn clockwise 90 ° and obtain; According to the reaction time of most people, direction vector d _tand d _cupdate cycle is 0.2s; Sinusoidal silver in normal direction moment item is used for simulating the shake of lower limb, and its frequency is 1Hz.

Fig. 8 A-8D is the result adopting task orientation formula active training, and the movement resistance in the method is determined jointly by the active moment of position deviation and patient.As shown in the 13s < t < 14s period, when patient attempts to move in the other direction, impedance adjustment factor increases, and the active movement of patient becomes difficulty, provides passive tactile feedback.As be labeled as " A " in figure period shown in, position deviation continues to reduce, but there is the change first reducing to increase afterwards in impedance adjustment factor, this is because original position deviation is larger, and normal direction active moment is negative, namely along the direction that deviation reduces, now movement resistance reduces to be supplied to patient's tactile feedback energetically; But along with the reduction of deviation and the increase of negative normal direction active moment, need to increase movement resistance and significantly surmount specified path to avoid hybrid system, cause larger position deviation.As be labeled as " B " in figure period shown in, originally hybrid system crosses specified path, and position deviation continues to increase, and normal direction initiatively moment just be, namely depart from the direction of increase along position, so movement resistance increase is with the tactile feedback being supplied to patient's passiveness; But along with normal direction active moment reduces gradually, and from just becoming negative (normal direction moment becomes towards specified path from deviating from specified path), movement resistance reduces again.At " B " in the period, in Fig. 8 C, the concussion of position deviation is interacted by the wall of hybrid system and virtual channel and produced equally.In 0s < t < 13s, impedance adjustment factor mean value under task orientation formula active training method is 0.3949, position deviation mean-square value is 0.0130, achieves and completes active training task more accurately with less movement resistance.

Therefore guarantee the movement locus of rehabilitation training, be applied to and possess good uniformity alternately between dynamics in suffering limb and patient-robot; For patient create one safe, comfortable, natural and possess the training environment of active compliance, avoid suffering limb due to spasm, the abnormal secondary damage caused of muscle activity such as tremble; Can obtain patient's active movement intention, encourage patient to play an active part in motion, realize so-called active training, thus raising rehabilitation efficacy is the break-through point in rehabilitation medical.

Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (6)

1. a task orientation formula active training control method, be applied in healing robot, described healing robot has sensing device, control system and driving mechanism, the signal generation control instruction that control system gathers for receiving sensing device, and control instruction is sent to driving mechanism, driving mechanism can wear the body part in patient, receive described control instruction, apply according to control instruction and regulate the active force to patient body part, to control the rehabilitation training of patient, it is characterized in that, described control method comprises the steps:

The active movement signal of S1, detection patient body part;

S2, active movement signal according to patient body part, regulate the driving mechanism of healing robot to the active force of patient body part.

2. task orientation formula active training control method as claimed in claim 1, it is characterized in that, described active movement signal is the active joint moment of patient, and described step S2 comprises:

S21, set up the directed walk corresponding with the motor task of patient, its closest approach of distance on the joint position of body part and this directed walk according to patient, sets up a virtual channel as reference position around specified path;

S22, convert active joint moment to corresponding actual motion, according to the position calculation position of virtual tunnel, speed and acceleration adjustment amount, obtain the control instruction of position, speed and acceleration, and it can be used as control signal to control driving mechanism;

S23, the active joint moment of patient body part to be decomposed along just tangential and positive normal, according to two components and the position offset of active moment, adopt the method for fuzzy logic to regulate impedance parameter.

3. task orientation formula active training control method as claimed in claim 2, it is characterized in that, described step S21 comprises:

Step S211: specify directed walk by a two-dimensional sequence Q in the joint space shown in following formula _srepresent:

Q _s：{q _i＝[q _i，1q _i，2] ^T|i＝1，...，L}

Wherein, q _irepresent Q _si-th element, q _{i, j}represent q _ia jth element, L is Q _sthe number of middle element;

Step S212: by intermediate variable equally spaced uniform discrete in its domain of definition, obtain terminal position sequence by parametrization equation, utilize inverse kinematics equation that end sequence is transformed into joint space;

Step S213: find the point that distance current location in position sequence is nearest

I ^*=argmin _{i=1 ..., L-1}|| q _i-q||, wherein i ^*represent the sequence number of closest approach;

Step S214: use closest approach with the next point in sequence determine straight line, this straight line is used as the tangent line at closest approach place on specified path approx:

$q_p=\left\{\begin{array}{cc}{\left[q_{i^{*},1]}{q}_2\right]}^T,& q_{i^{*}+\mathrm{1,1}}=q_{i^{*},1}\\ {\left[\frac{q_1+k(q_2-b)}{k^2+1}\frac{b+k(q_1+k{q}_2)}{k^2+1}\right]}^T,& \mathrm{otherwise}\end{array}\right.$

Wherein $k=(q_{i^{*}+\mathrm{1,2}}-q_{i^{*},2})/(q_{i^{*}+\mathrm{1,1}}-q_{i^{*},1})$ Represent straight line slope, represent the intercept of this straight line; q _pbe used to replace be used as the closest approach of distance current joint position on specified path;

Step S215: according to its closest approach of distance on real-time joint position and specified path, determine the reference position q in training process _r:

$q_r=\left\{\begin{array}{cc}{q}_p+\frac{w_t}{2}{\left|\right|q_e\left|\right|}^{-1}{q}_e,& \left|\right|q_e\left|\right|>\frac{w_t}{2}\\ q,& \mathrm{otherwise}\end{array}\right.,$

Thus around specified path, establish a virtual tunnel to limit the active movement of patient.

4. task orientation formula active training control method as claimed in claim 2, it is characterized in that, described step S22 comprises:

Step S221: by reverse impedance equation, according to the joint moment τ that patient self produces _hproduce position, speed and acceleration adjustment amount, revise reference locus, to regulate the Interaction Force of patient body part and driving mechanism;

Step S222: after utilizing position, speed, acceleration adjustment amount to revise reference movement locus, obtain position, speed and acceleration instruction, and it can be used as the reference signal of position servo control.

5. task orientation formula active training control method as claimed in claim 2, it is characterized in that, described step S23 comprises:

Step S231: according to the directed walk of specifying and real-time joint position, the active moment of patient body part is decomposed into two components:

τ _h＝τ _td _t+τ _ed _e，

Wherein, d _trepresent and specify subpoint q on directed walk _pthat locates is just tangential, d _erepresent subpoint place positive normal, q is departed from it and position _edirection consistent, point to current actual positions by subpoint, τ _tand τ _erepresent τ respectively _halong the component of above-mentioned both direction;

Step S232: the tangent line of the subpoint on specified path can by straight line be similar to, the just tangential column vector in order to lower 2 × 1 represents:

$d_t={\left|\right|q_{i^{*}+1}-q_{i^{*}}\left|\right|}^{-1}(q_{i^{*}+1}-q_{i^{*}});$

Step S233: the initiatively projection of joint moment on positive normal, under nonsingular state, namely || q _e|| ≠ 0, time, it can according to q _etry to achieve, otherwise, be set to d _tone of them normal direction:

$d_e=\left\{\begin{array}{cc}{\left|\right|q_e\left|\right|}^{-1}{q}_e,& \left|\right|q_e\left|\right|\&NotEqual;0\\ {[d_{t2}-d_{t1}]}^T,& \mathrm{otherwise}\end{array}\right.,$

Wherein, d _tirepresent d _ti-th element, q _ebe orthogonal to straight line d can be obtained _t⊥ d _e, || d _t||=1 and || d _e||=1, wherein ⊥ is orthogonal symbols, thus initiatively joint moment can respectively by formula in the projection just tangentially and on positive normal with try to achieve;

Step S234: by decomposing initiatively moment, obtain patient in the motion intention tangentially and in normal direction, when patient is intended to antagonism specified path, increases the impedance of active movement, otherwise reduces impedance.

6. task orientation formula active training control method as claimed in claim 5, is characterized in that, in step S234, adopts fuzzy logic to carry out impedance adjustment, initiatively two component τ of joint moment _tand τ _eand positional offset amount || q _e|| as the input variable of fuzzy logic, wherein || || represent Euclid 2-norm operator.