CN113995629B - Mirror image force field-based upper limb double-arm rehabilitation robot admittance control method and system - Google Patents
Mirror image force field-based upper limb double-arm rehabilitation robot admittance control method and system Download PDFInfo
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Abstract
The invention provides an admittance control method and system of an upper limb double-arm rehabilitation robot based on a mirror image force field, wherein the method comprises the following steps: step 1, modeling a human-computer tight coupling healthy side force field based on multi-sensor signal fusion to obtain the exercise intention of the healthy side of a subject; step 2: performing physiological signal and force field mapping of the healthy side based on the state space according to the motion intention of the healthy side of the subject to obtain the motion trail and intention of the healthy side of the subject; step 3: and synchronous coupling control of the healthy side based on the force field mirror image is performed according to the motion track and intention of the affected side of the subject, so that the motion of the exoskeleton is controlled. Aiming at the important clinical requirement of reconstructing the upper limb movement function after the clinical nerve shift operation, the invention combines the force field control strategy of human-computer tight coupling with the mirror image rehabilitation strategy, explores a new mirror image force field rehabilitation strategy for guiding the action of the affected side based on the information of the force field of the affected side of the patient, is more natural, and improves the participation feeling and the active rehabilitation capability of the patient.
Description
Technical Field
The invention relates to the technical field of admittance control of upper limb double-arm rehabilitation robots, in particular to an admittance control method and system of an upper limb double-arm rehabilitation robot based on a mirror image force field.
Background
The current peripheral nerve injury is a clinical multiple disease, tens of millions of patients with new trauma are annually, wherein the most serious peripheral nerve injury, such as brachial plexus injury, can cause complete paralysis of one side upper limb, seriously affect the life quality of the patients, and the treatment is a worldwide difficult problem. The currently accepted optimal treatment for brachial plexus avulsion is nerve shift. Rehabilitation is the key to the functional recovery of the upper limb after operation, and the cerebral cortex can be widely remodeled in the recovery process, and the result of the remodelling is critical to clinical prognosis. Studies have shown that following nerve shift surgery, the original functional areas of the affected limbs in the brain are reactivated by remodeling and effective control of the affected limbs is achieved. However, in clinical rehabilitation, many patients have the problems of poor limb movement control, wrong movement pattern and the like, and the root cause is that peripheral nerve innervation and access of the affected limb are greatly changed after nerve shift operation.
Research proves that compared with single unilateral rehabilitation training, the rehabilitation training is completed by guiding the affected side through the healthy side, so that the rehabilitation training is more in accordance with the natural movement mode of the upper limb of the human body, and the rehabilitation training is favorable for the neural plasticity of the semi-brain of the affected side and is more favorable for improving the rehabilitation effect of the movement function of the affected limb of the patient. Mirror therapy is the most mainly used therapy means for guiding the patient side movement by using the health side information in the traditional clinic. Mirror therapy is also called mirror vision feedback therapy, which uses the principle of plane mirror imaging to copy the motion picture of the healthy side to the affected side, so that the patient can imagine the motion of the affected side, and a rehabilitation training therapy means combining optical illusion, vision feedback and virtual reality is used. In the mirror image treatment, after the patient sees the mirror image of the exercise on the healthy side, mirror image neurons of the corresponding cerebral cortex are activated, which helps to restore the exercise function on the affected side. However, the mirror is not immersed in the mirror as a mirror carrier, and the stability and improvement of the clinical research result of the mirror therapy are directly affected. In addition, since the rehabilitation training of the traditional mirror image therapy can only realize the control of the movement track of the upper limb, and neglect the stress state of the muscle group of the affected limb, the improvement of the clinical research effect of the mirror image therapy is directly affected.
The Chinese patent document with publication number of CN109091818A discloses a training method and a training system of a rope traction upper limb rehabilitation robot based on admittance control, wherein when a user performs upper limb joint rehabilitation training exercise, interactive force signals applied to the rope traction upper limb rehabilitation robot by the user and kinematic signals of the upper limb are collected in real time; converting the interaction force signal into a motion parameter of an expected motion track through an admittance model, and determining the motion parameter of a target motion track according to the motion parameter of the expected motion track and the motion signal of the upper limb; and the determined motion parameters are used as control quantity and are converted into motor control quantity of the rope traction rehabilitation robot, and the corresponding motor output is controlled, so that the user can autonomously control the rehabilitation training action, and the active participation of the user is improved.
Aiming at the related technology, the inventor considers that the method is to use a mirror to perform visual feedback for rehabilitation of the affected side after the nerve repair operation, and the patient has poor participation feeling and general rehabilitation effect.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an upper limb double-arm rehabilitation robot method and system based on a mirror image force field.
The invention provides an admittance control method of an upper limb double-arm rehabilitation robot based on a mirror image force field, which comprises the following steps:
step 1: human-computer tight coupling healthy side force field modeling based on multi-sensor signal fusion is adopted to obtain the exercise intention of the healthy side of the subject;
step 2: performing physiological signal and force field mapping of the healthy side based on the state space according to the motion intention of the healthy side of the subject to obtain the motion trail and intention of the healthy side of the subject;
step 3: and synchronous coupling control of the healthy side based on the force field mirror image is performed according to the motion track and intention of the affected side of the subject, so that the motion of the exoskeleton is controlled.
Preferably, the step 1 includes predicting the movement intention of the subject in real time by the healthy side myoelectric sensor, modeling the acting force in the interaction process as an impedance model, and predicting the joint state of the subject by the impedance model, wherein the impedance model is shown in formula (1):
wherein u is h Acting force in the interaction process of the upper limb double-arm rehabilitation robot and the subject; x is the actual position of the tail end of the upper limb double-arm rehabilitation robot; x is x r The method is characterized in that the method is a desired position of the tail end of an upper limb double-arm rehabilitation robot; superscript notation represents the derivative of the corresponding state quantity with respect to time; l (L) h,1 Gain for position error; l (L) h,2 Is the speed gain;
estimating the movement intention of the subject by the formula (1), as shown in the formula (2):
wherein,,
an estimated value representing the exercise intention of the healthy side of the subject, and the superscript symbol is an estimated value of the corresponding quantity;
an initial value representing the error gain at any virtual target position; />
An initial value representing the velocity gain at any virtual target; the superscript v indicates that the value is based on any initial value given by the virtual target.
Preferably, the step 2 includes the steps of:
step 2.1: according to the steps1 modeling the subject's healthy side force field and physiological electromyographic signals to obtain the exercise intention of the subject's healthy side
Step 2.2: the double arms of the healthy side of the subject perform rehabilitation actions along the same track, and the movement track and intention of the healthy side of the subject are obtained through the mirror image principle.
Preferably, the step 3 includes combining the interaction force generated by the affected side during the interaction with the established affected side motion trajectory and intention, and controlling the motion of the exoskeleton through admittance control, wherein the affected side intention is expressed as:
wherein,,
subject intent predicted for robust model; τ r Original exercise intention for the affected side model; lambda is a super parameter that adjusts the weight ratio of the two.
Preferably, the admittance control in step 3 includes:
the dynamic equation of the interaction process of the upper limb double-arm rehabilitation robot and the subject is shown in a formula (4):
wherein M and G respectively represent an inertial matrix and a gravity matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system; c represents a Coriolis force and centrifugal force matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system; f (f) dis Is a disturbance in the interactive system; u is the control input of the system; the superscript · represents the second derivative of the actual position of the rehabilitation robot tip with respect to time, i.e. the acceleration;
supposing upper limb double arm rehabilitation machineThe derivative of the actual position x of the human end and the actual position of the upper arm rehabilitation robot end with respect to time
Is obtained by measurement; let x be 1 ＝[q 1 ,q 2 ,…,q n ] T ,/>
Wherein q i And->
Respectively representing the rotation angle and the angular velocity of the ith joint, i is more than or equal to 1 and less than or equal to n; x is x 1 A position matrix formed by the rotation angles of all joints of the robot is shown; x is x 2 A velocity matrix including angular velocities of the joints; the superscript T denotes a transpose; the dynamics of the interaction task is expressed as follows:
defining position error z 1 ＝x 1 -x r Error of speed z 2 ＝x 2 -α 1 ，α 1 To z 1 Virtual control of (c) to obtain:
using lyapunov function
V 1 Representing the constructed function in the form of a lyapunov function; symbol represents matrix multiplication; and (3) deriving time:
order the
Wherein K is 1 For the gain matrix, reset equation (7) to get:
the expression (8) is used for obtaining:
definition of Lyapunov function
V 2 Representing the constructed function in the form of a lyapunov function; and (3) deriving time:
when the parameters of the dynamics are known, the control is expressed in the form:
wherein K is 2 Representing a gain matrix;
g, C and M terms of the robot dynamics are approximated using a radial basis neural network; the external disturbance is compensated by a disturbance observer; the self-adaptive control law is as follows:
wherein,,
is a radial basis function neural network, W is a weight coefficient,y (Z) is a dynamic regression matrix, namely the distance between a sample point and each radial base center, and Z represents the input of a radial function network; let->
The form of the high-order disturbance observer is as follows:
wherein K is d Representing a gain matrix in the disturbance observation process;
representing an estimation error; y is Y d (Z d ) Representing a dynamic regression matrix, Y d (Z d ) Representing a dynamic regression matrix; z is Z d Representing the actual sampling point; w (W) d Representing the weight coefficient; />
The update of the weight matrix is as follows:
Y d (Z d )W d ＝M -1 (u+u h -C(x 1 ,x 2 )x 2 -G(x 1 ))-∈ d
wherein Y is i (Z) represents an updated value of the dynamic regression quantity matrix; z 2i An update indicative of a speed error; w (W) i Representing an update of the estimated value; superscript symbol
Representing the expected value of the weight derivative; w (W) di An updated value representing the physical parameter; epsilon represents the estimation error; e-shaped article d Representing the expected estimation error; y (Z) W represents the output of the radial basis function; Γ -shaped structure i And Γ di For update rate, θ i And theta di Is the weight.
The invention provides an admittance control system of an upper limb double-arm rehabilitation robot based on a mirror image force field, which comprises the following modules:
module M1: human-computer tight coupling healthy side force field modeling based on multi-sensor signal fusion is adopted to obtain the exercise intention of the healthy side of the subject;
module M2: performing physiological signal and force field mapping of the healthy side based on the state space according to the motion intention of the healthy side of the subject to obtain the motion trail and intention of the healthy side of the subject;
module M3: and synchronous coupling control of the healthy side based on the force field mirror image is performed according to the motion track and intention of the affected side of the subject, so that the motion of the exoskeleton is controlled.
Preferably, the module M1 includes predicting the movement intention of the subject in real time by the healthy side myoelectric sensor, modeling the acting force in the interaction process as an impedance model, and predicting the joint state of the subject by the impedance model, wherein the impedance model is shown in formula (1):
wherein u is h Acting force in the interaction process of the upper limb double-arm rehabilitation robot and the subject; x is the actual position of the tail end of the upper limb double-arm rehabilitation robot; x is x r The method is characterized in that the method is a desired position of the tail end of an upper limb double-arm rehabilitation robot; superscript notation represents the derivative of the corresponding state quantity with respect to time; l (L) h,1 Is bitSetting an error gain; l (L) h,2 Is the speed gain;
estimating the movement intention of the subject by the formula (1), as shown in the formula (2):
wherein,,
an estimated value representing the exercise intention of the healthy side of the subject, and the superscript symbol is an estimated value of the corresponding quantity;
an initial value representing the error gain at any virtual target position; />
An initial value representing the velocity gain at any virtual target; the superscript v indicates that the value is based on any initial value given by the virtual target.
Preferably, the module M2 includes the following modules:
module M2.1: modeling a subject's exercise stress field and physiological myoelectric signals according to the module M1 to obtain the exercise intention of the subject's exercise
Module M2.2: the double arms of the healthy side of the subject perform rehabilitation actions along the same track, and the movement track and intention of the healthy side of the subject are obtained through the mirror image principle.
Preferably, the module M3 includes combining the interaction force generated by the patient side during the interaction with the established patient side motion trajectory and intent to control the movement of the exoskeleton through admittance control, wherein the patient side intent is expressed as:
wherein,,
subject intent predicted for robust model; τ r Original exercise intention for the affected side model; lambda is a super parameter that adjusts the weight ratio of the two.
Preferably, the admittance control in the module M3 includes:
the dynamic equation of the interaction process of the upper limb double-arm rehabilitation robot and the subject is shown in a formula (4):
wherein M and G respectively represent an inertial matrix and a gravity matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system; c represents a Coriolis force and centrifugal force matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system; f (f) dis Is a disturbance in the interactive system; u is the control input of the system; the superscript · represents the second derivative of the actual position of the rehabilitation robot tip with respect to time, i.e. the acceleration;
assume that the actual position x of the upper arm rehabilitation robot tip and the derivative of the actual position of the upper arm rehabilitation robot tip with respect to time
Is obtained by measurement; let x be 1 ＝[q 1 ,q 2 ,…,q n ] T ,/>
Wherein q i And->
Respectively representing the rotation angle and the angular velocity of the ith joint, i is more than or equal to 1 and less than or equal to n; x is x 1 A position matrix formed by the rotation angles of all joints of the robot is shown; x is x 2 A velocity matrix including angular velocities of the joints; upper partThe label T denotes transposition; the dynamics of the interaction task is expressed as follows:
defining position error z 1 ＝x 1 -x r Error of speed z 2 ＝x 2 -α 1 ，α 1 To z 1 Virtual control of (c) to obtain:
using lyapunov function
V 1 Representing the constructed function in the form of a lyapunov function; symbol represents matrix multiplication; and (3) deriving time:
order the
Wherein K is 1 For the gain matrix, reset equation (7) to get:
the expression (8) is used for obtaining:
definition of Lyapunov function
V 2 Representation structureA function in the form of a built lyapunov function; and (3) deriving time:
when the parameters of the dynamics are known, the control is expressed in the form:
wherein K is 2 Representing a gain matrix;
g, C and M terms of the robot dynamics are approximated using a radial basis neural network; the external disturbance is compensated by a disturbance observer; the self-adaptive control law is as follows:
wherein,,
the method is characterized in that the method is a radial basis function network, W is a weight coefficient, Y (Z) is a dynamic regression matrix, namely the distance between a sample point and each radial basis center, and Z represents the input of the radial function network; let->
The form of the high-order disturbance observer is as follows:
wherein K is d Representing a gain matrix in the disturbance observation process;
representing an estimation error; y is Y d (Z d ) Representing a dynamic regression matrix, Y d (Z d ) Representing a dynamic regression matrix; z is Z d Representing the actual sampling point; w (W) d Representing the weight coefficient; />
The update of the weight matrix is as follows:
Y d (Z d )W d ＝M -1 (u+u h -C(x 1 ,x 2 )x 2 -G(x 1 ))-∈ d
wherein Y is i (Z) represents an updated value of the dynamic regression quantity matrix; z 2i An update indicative of a speed error; w (W) i Representing an update of the estimated value; superscript symbol
Representing the expected value of the weight derivative; w (W) di An updated value representing the physical parameter; epsilon represents the estimation error; e-shaped article d Representing the expected estimation error; y (Z) W represents the output of the radial basis function; Γ -shaped structure i And Γ di For update rate, θ i And theta di Is the weight.
Compared with the prior art, the invention has the following beneficial effects:
1. aiming at the important clinical requirement of reconstructing the upper limb movement function after the clinical nerve shift operation, the invention combines the force field control strategy of human-computer tight coupling with the mirror image rehabilitation strategy, explores a new mirror image force field rehabilitation strategy for guiding the action of the affected side based on the information of the force field of the affected side of the patient, and the method is more natural, and improves the participation feeling and the active rehabilitation capability of the patient;
2. aiming at the clinical important clinical requirements of the rehabilitation of the motor function after the nerve shift operation, the invention explores the force field mirror image rehabilitation strategy for effectively guiding the action of the affected side based on the information of the force field of the affected side of the patient based on the new technology in the engineering and medical fields, and the research result is a great breakthrough in the research field of the peripheral nerve rehabilitation, which not only drives the clinical treatment method in the research field of the peripheral nerve rehabilitation to be greatly innovated, but also provides a new technical means for exploring the rehabilitation of the function after the nerve shift operation and the recovery mechanism of the brain function, and has great academic value and clinical significance; the achievements can be used for hospitals, rehabilitation centers and communities to benefit patients in a shared manner;
3. the invention realizes the mirror image coupling of the healthy side force field and the sick side force field of the upper limb rehabilitation robot by an admittance control method, and obtains the sick side rehabilitation training effect which accords with the real exercise habit of a patient.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of an admittance control system and method for an upper limb double-arm rehabilitation robot based on a mirror image force field;
FIG. 2 is a schematic diagram of the super-parameter lambda adjustment at different rehabilitation training stages according to the present invention;
fig. 3 is a block diagram of an admittance control method of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
The embodiment of the invention discloses an admittance control method of an upper limb double-arm rehabilitation robot based on a mirror image force field, which is shown in fig. 1 and 2 and comprises man-machine tight coupling healthy side force field modeling based on multi-sensor signal fusion, healthy side physiological signals and force field mapping based on a state space and healthy side synchronous coupling control based on force field mirror image. The healthy side includes a healthy side and a diseased side. The method comprises the following steps: step 1: and (3) human-computer tight coupling healthy side force field modeling based on multi-sensor signal fusion to obtain the exercise intention of the healthy side of the subject. The exercise intention of the subject is predicted in real time through the side-building myoelectric sensor, acting force in the interaction process is modeled as an impedance model, and the joint state of the subject is predicted through the impedance model. Human-computer tight coupling healthy side force field modeling based on multi-sensor signal fusion comprises the steps of estimating and predicting the motion intention of a person through healthy side myoelectric sensors, modeling acting force in the interaction process into an impedance model, and predicting the joint state of the person by using the model, wherein the impedance model is shown in a formula (1):
wherein u is h Acting force in the interaction process of the upper limb double-arm rehabilitation robot and the subject, namely interaction force between the upper limb double-arm rehabilitation robot and the subject; x is the actual position of the tail end of the upper limb double-arm rehabilitation robot, and x is r For the expected position of the end of the upper limb double-arm rehabilitation robot, the superscript symbol represents the derivative of the corresponding state quantity with respect to time, L h,1 For position error gain, L h,2 Is the speed gain.
Estimating the movement intention of the subject by the formula (1), as shown in the formula (2):
wherein,,
an estimated value representing the exercise intention of the healthy side of the subject, and the superscript symbol is an estimated value of the corresponding quantity;
an initial value representing the error gain at any virtual target position; />
An initial value representing the velocity gain at any virtual target; the superscript v indicates that the value is based on any initial value given by the virtual target.
Step 2: and mapping physiological signals and force fields of the healthy side based on the state space according to the motion intention of the healthy side of the subject to obtain the motion trail and intention of the healthy side of the subject. Step 2 comprises the following steps: step 2.1: modeling the subject's exercise side force field and physiological electromyographic signals according to the step 1 to obtain the exercise intention of the subject's exercise side
Step 2.2: the double arms of the healthy side of the subject perform rehabilitation actions along the same track, and the movement track and intention of the healthy side of the subject are obtained through the mirror image principle.
The specific process of physiological signal and force field mapping of healthy side based on the state space comprises the following steps: modeling the healthy side force field and the physiological electromyographic signals of the subject according to the method in the step 1, namely obtaining the exercise intention of the healthy side of the subject. In the upper arm rehabilitation training process, rehabilitation actions are carried out on the healthy side of the subject along the same track, and the movement track and intention of the healthy side of the subject are obtained through the mirror image principle.
Step 3: as shown in fig. 1 and 3, synchronous coupling control of the healthy side based on force field mirror image is performed according to the motion track and intention of the healthy side of the subject, so as to control the motion of the exoskeleton.
The interaction force generated by the affected side in the interaction process is combined with the established affected side movement track and intention, and the movement of the exoskeleton is controlled through admittance control. Synchronous coupling control of healthy and wounded sides based on force field mirror images combines interaction force generated by wounded sides in the interaction process with established movement tracks and intentions of the wounded sides, and controls the movement of the exoskeleton through an admittance control method, wherein the wounded side intentions are expressed as follows:
wherein,,
subject intent predicted for robust model; τ r For the original motor intent of the affected side model, λ is the hyper-parameter that adjusts the weight ratio of the two. The influence of interaction force between the affected side and the rehabilitation robot is small at the initial stage of rehabilitation training by adjusting lambda at different stages of rehabilitation training of a hemiplegic patient, at this time, the mirror image of the exercise intention of the healthy side is taken as guide, namely lambda=1, the exercise intention of the affected side in the control process of the rehabilitation robot can be increased by reducing lambda along with the increase of rehabilitation training, and the super-parameter lambda adjusting schematic diagram at different rehabilitation training stages is shown in fig. 2.
The synchronous coupling control of the healthy side based on the force field mirror image comprises the following specific processes of admittance control: the dynamic equation of the interaction process of the upper limb double-arm rehabilitation robot and the subject is shown in a formula (4):
wherein M and G respectively represent an inertial matrix and a gravity matrix of the lower limb exoskeleton robot and the human interaction system of the Cartesian space coordinate system, and C represents a Coriolis force and centrifugal force matrix of the lower limb exoskeleton robot and the human interaction system of the Cartesian space coordinate system; f (f) dis U is the control input of the system, which is the disturbance in the interactive system; u (u) h Acting force in the interaction process of the upper limb double-arm rehabilitation robot and the subject; the superscript "here specifically means that the first derivative of the actual position of the rehabilitation robot tip with respect to time, i.e. the robot tip speed, is onThe second derivative of the actual position of the end of the rehabilitation robot with respect to time, i.e. the acceleration, is shown by the label.
Assume that the actual position x of the upper arm rehabilitation robot tip and the derivative of the actual position of the upper arm rehabilitation robot tip with respect to time
Is obtained by measurement. Let x be 1 ＝[q 1 ,q 2 ,…,q n ] T ，/>
Wherein q is i And->
Respectively representing the rotation angle and the angular velocity of the ith joint, i is more than or equal to 1 and less than or equal to n; x is x 1 A position matrix formed by the rotation angles of all joints of the robot is shown; x is x 2 A velocity matrix including angular velocities of the joints; the superscript T denotes a transpose; the dynamics of the interaction task is expressed as follows:
defining position error z 1 ＝x 1 -x r Error of speed z 2 ＝x 2 -α 1 Wherein x is r For the expected position of the tail end of the upper limb double-arm rehabilitation robot, namely the expected reference track alpha 1 To z 1 Virtual control of (c) to obtain:
consider the use of the Lyapunov function
V 1 Representing the function V of the constructed Lyapunov function form 1 The method comprises the steps of carrying out a first treatment on the surface of the Symbol represents matrix multiplicationA method; and (3) deriving time:
order the
Wherein K is 1 Equation (7) is reset for the gain matrix. Namely, the formula (7) is rewritten to obtain:
the expression (8) is used for obtaining:
definition of Lyapunov function
V 2 Representing the function V of the constructed Lyapunov function form 2 The method comprises the steps of carrying out a first treatment on the surface of the And (3) deriving time:
when the parameters of the dynamics are known, the control method is expressed in the following form:
wherein K is 2 Representing the gain matrix.
Due to interference f dis Is difficult to obtain and items such as G, C, M of robot dynamics are not readily available. The G, C and M terms of robot dynamics are approximated using radial basis neural networks (RBFNN). In addition, external disturbances are compensated by a disturbance observerCompensating; the self-adaptive control law is as follows:
wherein,,
the method is characterized in that the radial basis function neural network RBFNN is adopted, W is a weight coefficient, Y (Z) is a dynamic regression matrix, namely the distance between a sample point and each radial basis center, and Z represents the input of the radial function network; let->
The form of the high-order disturbance observer is as follows:
wherein K is d Representing a gain matrix in the disturbance observation process;
representing an estimation error; y is Y d (Z d ) Representing a dynamic regression matrix, Y d Representing a dynamic regression matrix; z is Z d Representing the actual sample dataset; w (W) d Representing the weight coefficient; />
The update of the weight matrix is as follows:
Y d (Z d )W d ＝M -1 (u+u h -C(x 1 ，x 2 )x 2 -G(x 1 ))-∈ d
wherein Y is i (Z) represents an updated value of the dynamic regression quantity matrix; z 2i An update indicative of a speed error; w (W) i Representing an update of the estimated value; superscript symbol
Representing the expected value of the weight derivative; w (W) di An updated value representing the physical parameter; epsilon represents the estimation error; e-shaped article d Representing the expected estimation error; y (Z) W represents the output of the radial basis function; Γ -shaped structure i And Γ di For update rate, θ i And theta di Is the weight. The characteristics of the regressor are also exploited in the disturbance observer.
The admittance control method is shown in fig. 3, and the reference track of the rehabilitation robot is reconstructed based on the physiological signals of the healthy side and the force field mapping in the state space, so that the control method can be suitable for people with different skill levels and different forces under the condition of not performing offline model adjustment, and the robustness of the controller is ensured. The control scheme consists of an inner ring and an outer ring. The former is able to handle unknown mass and moment of inertia in the robot dynamics, the latter is to adjust the interaction model taking into account the human subject's intent.
The embodiment of the invention discloses an admittance control system of an upper limb double-arm rehabilitation robot based on a mirror image force field, which comprises the following modules: module M1: and (3) human-computer tight coupling healthy side force field modeling based on multi-sensor signal fusion to obtain the exercise intention of the healthy side of the subject. Predicting the movement intention of a subject in real time through a healthy side myoelectric sensor, modeling acting force in the interaction process as an impedance model, and predicting the joint state of the subject through the impedance model, wherein the impedance model is shown in a formula (1):
wherein u is h Acting force in the interaction process of the upper limb double-arm rehabilitation robot and the subject; x is the actual position of the tail end of the upper limb double-arm rehabilitation robot; x is x r The method is characterized in that the method is a desired position of the tail end of an upper limb double-arm rehabilitation robot; superscript notation represents the derivative of the corresponding state quantity with respect to time; l (L) h,1 Gain for position error; l (L) h,2 Is the speed gain;
estimating the movement intention of the subject by the formula (1), as shown in the formula (2):
wherein,,
an estimated value representing the exercise intention of the healthy side of the subject, and the superscript symbol is an estimated value of the corresponding quantity;
an initial value representing the error gain at any virtual target position; />
An initial value representing the velocity gain at any virtual target; the superscript v indicates that the value is based on any initial value given by the virtual target.
Module M2: and mapping physiological signals and force fields of the healthy side based on the state space according to the motion intention of the healthy side of the subject to obtain the motion trail and intention of the healthy side of the subject. The module M2 includes the following modules: module M2.1: modeling a subject's exercise stress field and physiological myoelectric signals according to the module M1 to obtain the exercise intention of the subject's exercise
Module M2.2: the double arms of the healthy side of the subject perform rehabilitation actions along the same track, and the movement track and intention of the healthy side of the subject are obtained through the mirror image principle.
Module M3: and synchronous coupling control of the healthy side based on the force field mirror image is performed according to the motion track and intention of the affected side of the subject, so that the motion of the exoskeleton is controlled. Combining the interaction force generated by the affected side in the interaction process with the established affected side movement track and intention, and controlling the movement of the exoskeleton through admittance control, wherein the affected side intention is expressed as:
wherein,,
subject intent predicted for robust model; τ r Original exercise intention for the affected side model; lambda is a super parameter that adjusts the weight ratio of the two.
Admittance control includes: the dynamic equation of the interaction process of the upper limb double-arm rehabilitation robot and the subject is shown in a formula (4):
wherein M and G respectively represent an inertial matrix and a gravity matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system; c represents a Coriolis force and centrifugal force matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system; f (f) dis Is a disturbance in the interactive system; u is the control input of the system; the superscript · indicates the second derivative of the actual position of the rehabilitation robot tip with respect to time, i.e. the acceleration.
Assume that the actual position x of the upper arm rehabilitation robot tip and the derivative of the actual position of the upper arm rehabilitation robot tip with respect to time
Is obtained by measurement; let x be 1 ＝[q 1 ,q 2 ,…,q n ] T ,/>
Wherein q i And->
Respectively representing the rotation angle and the angular velocity of the ith joint, i is more than or equal to 1 and less than or equal to n; x is x 1 A position matrix formed by the rotation angles of all joints of the robot is shown; x is x 2 A velocity matrix including angular velocities of the joints; the superscript T denotes a transpose; the dynamics of the interaction task is expressed as follows:
defining position error z 1 ＝x 1 -x r Error of speed z 2 ＝x 2 -α 1 ，α 1 To z 1 Virtual control of (c) to obtain:
using lyapunov function
V 1 Representing the constructed function in the form of a lyapunov function; symbol represents matrix multiplication; and (3) deriving time:
order the
Wherein K is 1 For the gain matrix, reset equation (7) to get:
the expression (8) is used for obtaining:
definition of Lyapunov function
V 2 Representing the constructed function in the form of a lyapunov function; and (3) deriving time:
when the parameters of the dynamics are known, the control is expressed in the form:
wherein K is 2 Representing a gain matrix;
g, C and M terms of the robot dynamics are approximated using a radial basis neural network; the external disturbance is compensated by a disturbance observer; the self-adaptive control law is as follows:
wherein,,
the method is characterized in that the method is a radial basis function network, W is a weight coefficient, Y (Z) is a dynamic regression matrix, namely the distance between a sample point and each radial basis center, and Z represents the input of the radial function network; let->
The form of the high-order disturbance observer is as follows:
wherein K is d Representing a gain matrix in the disturbance observation process;
representing an estimation error; y is Y d (Z d ) Representing a dynamic regression matrix, Y d (Z d ) Representing a dynamic regression matrix; z is Z d Representing the actual sampling point; w (W) d Representing the weight coefficient; />
The update of the weight matrix is as follows:
Y d (Z d )W d ＝M -1 (u+u h -C(x 1 ,x 2 )x 2 -G(x 1 ))-∈ d
wherein Y is i (Z) represents an updated value of the dynamic regression quantity matrix; z 2i An update indicative of a speed error; w (W) i Representing an update of the estimated value; superscript symbol
Representing the expected value of the weight derivative; w (W) di An updated value representing the physical parameter; epsilon represents the estimation error; e-shaped article d Representing the expected estimation error; y (Z) W represents the output of the radial basis function; Γ -shaped structure i And Γ di For update rate, θ i And theta di Is the weight.
The invention explores a novel mirror image force field rehabilitation strategy for guiding the action of the affected side based on the information of the force field of the affected side of a patient, which comprises a human-computer tight coupling force field modeling based on multi-sensor signal fusion, physiological signals and force field mapping of the affected side based on a state space and synchronous coupling control of the affected side based on force field mirror image. Aiming at the important clinical requirement of reconstruction of the upper limb movement function after the clinical nerve shift operation, the invention combines the force field control strategy of human-machine tight coupling with the mirror image rehabilitation strategy.
The invention provides a new research on a medical rehabilitation method, and a research result is a great breakthrough in the field of peripheral nerve rehabilitation research, which not only drives a clinical treatment method in the field of peripheral nerve rehabilitation research to be greatly innovated, but also provides a new technical means for exploring a nerve shift postoperative function reconstruction and brain function recovery mechanism, and has great academic value and clinical significance.
The invention relates to the technical fields of man-machine interaction, artificial intelligence and interaction control, and the man-machine tight coupling healthy side force field modeling based on multi-sensor signal fusion, healthy side physiological signals and force field mapping based on a state space and healthy side synchronous coupling control based on force field mirror image are included. The traditional method is to use a mirror to perform visual feedback for the rehabilitation of the affected side after the nerve repair operation, and the method has weak participation of patients and general rehabilitation effect. Different from the existing rehabilitation means. Aiming at the important clinical requirement of reconstructing the upper limb movement function after the clinical nerve shift operation, the invention combines the force field control strategy of human-computer tight coupling with the mirror image rehabilitation strategy, explores a novel mirror image force field rehabilitation strategy for guiding the action of the affected side based on the information of the force field of the affected side of the patient, and the method is more natural and improves the participation feeling and the active rehabilitation capability of the patient.
Those skilled in the art will appreciate that the invention provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (2)
1. The utility model provides an upper limbs both arms rehabilitation robot admittance control system based on mirror image force field which characterized in that includes following module:
module M1: human-computer tight coupling healthy side force field modeling based on multi-sensor signal fusion is adopted to obtain the exercise intention of the healthy side of the subject;
module M2: performing physiological signal and force field mapping of the healthy side based on the state space according to the motion intention of the healthy side of the subject to obtain the motion trail and intention of the healthy side of the subject;
module M3: synchronous coupling control of the healthy side based on force field mirror image is carried out according to the motion track and intention of the affected side of the subject, so that the motion of the exoskeleton is controlled;
the module M1 includes predicting the movement intention of the subject in real time through the healthy side myoelectric sensor, modeling the acting force in the interaction process as an impedance model, and predicting the joint state of the subject through the impedance model, wherein the impedance model is shown as formula (1):
wherein,,
acting force in the interaction process of the upper limb double-arm rehabilitation robot and the subject; />
The actual position of the tail end of the upper limb double-arm rehabilitation robot; />
The method is characterized in that the method is a desired position of the tail end of an upper limb double-arm rehabilitation robot; superscript notation represents the derivative of the corresponding state quantity with respect to time; />
Gain for position error; />
Is the speed gain;
estimating the movement intention of the subject by the formula (1), as shown in the formula (2):
wherein,,
an estimated value representing the exercise intention of the healthy side of the subject, and the superscript symbol is an estimated value of the corresponding quantity; />
Expressed in arbitrary virtual target positionSetting an initial value of an error gain; />
An initial value representing the velocity gain at any virtual target; superscriptvThe representation value is based on an arbitrary initial value given by the virtual target;
the module M2 comprises the following modules:
module M2.1: modeling a subject's exercise stress field and physiological myoelectric signals according to the module M1 to obtain the exercise intention of the subject's exercise
；
Module M2.2: the double arms of the healthy side of the subject perform rehabilitation actions along the same track, and the movement track and intention of the healthy side of the subject are obtained through the mirror image principle;
the module M3 includes combining the interaction force generated by the patient side during the interaction with the established patient side motion trajectory and intent to control the exoskeleton's motion through admittance control, wherein the patient side intent is expressed as:
wherein,,
subject intent predicted for robust model; />
Original exercise intention for the affected side model; />
Super parameters for adjusting the weight ratio of the two;
admittance control in the module M3 includes:
the dynamic equation of the interaction process of the upper limb double-arm rehabilitation robot and the subject is shown in a formula (4):
wherein,,MandGrespectively representing an inertia matrix and a gravity matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system;Crepresenting a coriolis force and centrifugal force matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system;
is a disturbance in the interactive system; />
Is a control input of the system; the superscript · represents the second derivative of the actual position of the rehabilitation robot tip with respect to time, i.e. the acceleration;
assume the actual position of the end of an upper limb double arm rehabilitation robot
And the derivative of the actual position of the upper arm rehabilitation robot tip with respect to time +.>
Is obtained by measurement; is provided with->
=/>
,/>
Wherein->
And->
Respectively represent the firstiRotation angle of individual jointsThe degree and the angular velocity are less than or equal to 1 percenti≤n；/>
A position matrix formed by the rotation angles of all joints of the robot is shown; />
A velocity matrix including angular velocities of the joints; superscriptTRepresenting a transpose; the dynamics of the interaction task is expressed as follows:
defining position errors
Speed error->
，/>
For->
Virtual control of (c) to obtain:
using lyapunov function
，/>
Representing the constructed function in the form of a lyapunov function; sign->
Representing a matrix multiplication; and (3) deriving time:
order the
Wherein->
For the gain matrix, reset equation (7) to get:
the expression (8) is used for obtaining:
definition of Lyapunov function
,/>
Representing the constructed function in the form of a lyapunov function; and (3) deriving time:
when the parameters of the dynamics are known, the control is expressed in the form:
wherein,,
representing a gain matrix;
approximating robot dynamics using radial basis neural networksG、CAndMan item; the external disturbance is compensated by a disturbance observer; the self-adaptive control law is as follows:
wherein,,
is radial basis function neural network, < >>
Is a weight coefficient>
For the dynamic regression matrix, i.e. the distance of the sample point from each radial basis center,/for the radial basis center>
Representing a radial function network input; let->
The form of the high-order disturbance observer is as follows:
wherein,,
representing a gain matrix in the disturbance observation process; />
Representing an estimation error; />
Representing a dynamic regression matrix,/->
Representing the actual sampling point; />
Representing the weight coefficient; />
，/>
The update of the weight matrix is as follows:
wherein,,
representing updated values of the dynamic regression quantity matrix; />
An update indicative of a speed error; />
Representing an update of the estimated value; superscript symbol->
Representing the expected value of the weight derivative; />
An updated value representing the physical parameter; />
Representing an estimation error; />
Representing the expected estimation error; />
An output representing a radial basis function; />
For update rate->
And->
Is the weight.
2. The mirror-image force field-based upper limb double-arm rehabilitation robot admittance control system according to claim 1, characterized in that the following steps can be realized:
step 1: human-computer tight coupling healthy side force field modeling based on multi-sensor signal fusion is adopted to obtain the exercise intention of the healthy side of the subject;
step 2: performing physiological signal and force field mapping of the healthy side based on the state space according to the motion intention of the healthy side of the subject to obtain the motion trail and intention of the healthy side of the subject;
step 3: synchronous coupling control of the healthy side based on force field mirror image is carried out according to the motion track and intention of the affected side of the subject, so that the motion of the exoskeleton is controlled;
the step 1 includes predicting the movement intention of the subject in real time through the side-strengthening myoelectric sensor, modeling the acting force in the interaction process as an impedance model, and predicting the joint state of the subject through the impedance model, wherein the impedance model is shown as a formula (1):
wherein,,
acting force in the interaction process of the upper limb double-arm rehabilitation robot and the subject; />
The actual position of the tail end of the upper limb double-arm rehabilitation robot; />
The method is characterized in that the method is a desired position of the tail end of an upper limb double-arm rehabilitation robot; superscript notation represents the derivative of the corresponding state quantity with respect to time; />
Gain for position error; />
Is the speed gain;
estimating the movement intention of the subject by the formula (1), as shown in the formula (2):
wherein,,
an estimated value representing the exercise intention of the healthy side of the subject, and the superscript symbol is an estimated value of the corresponding quantity; />
An initial value representing the error gain at any virtual target position; />
An initial value representing the velocity gain at any virtual target; superscriptvThe representation value is based on an arbitrary initial value given by the virtual target;
the step 2 comprises the following steps:
step 2.1: modeling the subject's exercise side force field and physiological electromyographic signals according to the step 1 to obtain the exercise intention of the subject's exercise side
；
Step 2.2: the double arms of the healthy side of the subject perform rehabilitation actions along the same track, and the movement track and intention of the healthy side of the subject are obtained through the mirror image principle;
step 3 includes combining the interaction force generated by the affected side during the interaction with the established affected side motion trail and intention, and controlling the motion of the exoskeleton through admittance control, wherein the affected side intention is expressed as:
wherein,,
subject intent predicted for robust model; />
Original exercise intention for the affected side model; />
Super parameters for adjusting the weight ratio of the two;
the admittance control in step 3 includes:
the dynamic equation of the interaction process of the upper limb double-arm rehabilitation robot and the subject is shown in a formula (4):
wherein,,MandGrespectively representing an inertia matrix and a gravity matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system;Crepresenting a coriolis force and centrifugal force matrix of the Cartesian space coordinate system lower limb exoskeleton robot and the human interaction system;
is a disturbance in the interactive system; />
Is a control input of the system; the superscript · represents the second derivative of the actual position of the rehabilitation robot tip with respect to time, i.e. the acceleration;
assume the actual position of the end of an upper limb double arm rehabilitation robot
And the derivative of the actual position of the upper arm rehabilitation robot tip with respect to time +.>
Is obtained by measurement; is provided with->
=/>
,/>
Wherein->
And->
Respectively represent the firstiThe rotation angle and the angular velocity of each joint are less than or equal to 1 percenti≤n；/>
A position matrix formed by the rotation angles of all joints of the robot is shown; />
A velocity matrix including angular velocities of the joints; superscriptTRepresenting a transpose; the dynamics of the interaction task is expressed as follows:
defining position errors
Speed error->
，/>
For->
Virtual control of (c) to obtain:
using lyapunov function
，/>
Representing the constructed function in the form of a lyapunov function; sign->
Representing a matrix multiplication; and (3) deriving time:
order the
Wherein->
For the gain matrix, reset equation (7) to get:
the expression (8) is used for obtaining:
definition of Lyapunov function
,/>
Representing the constructed function in the form of a lyapunov function; and (3) deriving time:
when the parameters of the dynamics are known, the control is expressed in the form:
wherein,,
representing a gain matrix;
approximating robot dynamics using radial basis neural networksG、CAndMan item; the external disturbance is compensated by a disturbance observer; the self-adaptive control law is as follows:
wherein,,
is radial basis function neural network, < >>
Is a weight coefficient>
For the dynamic regression matrix, i.e. the distance of the sample point from each radial basis center,/for the radial basis center>
Representing a radial function network input; let->
The form of the high-order disturbance observer is as follows:
wherein,,
representing a gain matrix in the disturbance observation process; />
Representing an estimateCounting errors; />
Representing a dynamic regression matrix,/->
Representing the actual sampling point; />
Representing the weight coefficient; />
，/>
The update of the weight matrix is as follows:
wherein,,
representing updated values of the dynamic regression quantity matrix; />
An update indicative of a speed error; />
Representing an update of the estimated value; superscript symbol->
Representing the expected value of the weight derivative; />
An updated value representing the physical parameter; />
Representing an estimation error; />
Representing the expected estimation error; />
An output representing a radial basis function; />
For update rate->
And->
Is the weight.
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