CN113189865B - Rehabilitation robot control system based on dynamic parameter identification - Google Patents
Rehabilitation robot control system based on dynamic parameter identification Download PDFInfo
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Abstract
The invention provides a rehabilitation robot control method, a system, equipment and a medium based on dynamic parameter identification, wherein the rehabilitation robot comprises a rehabilitation robot body and a rehabilitation robot accessory, a training side limb and the rehabilitation robot accessory are fixed together to form a synchronous motion system, and the method comprises the following steps: when a dynamic parameter identification instruction is received, identifying the dynamic parameters of the synchronous motion system to obtain a dynamic parameter identification result; acquiring the movement intention of the patient according to the measured interaction moment between the synchronous movement system and the rehabilitation robot body and the dynamic parameter identification result; according to the movement intention of the patient, obtaining a driving moment required to be output by the rehabilitation robot body; and generating a corresponding control instruction according to the driving moment, and sending the control instruction to the rehabilitation robot body. The invention improves the flexibility of the cooperative interaction force control of the rehabilitation robot and the patient training side limb.
Description
Technical Field
The invention relates to the field of robot control, in particular to a rehabilitation robot control method, system, equipment and medium based on dynamic parameter identification.
Background
With the aggravation of the aging population, the incidence rate of diseases of the aging population is increased, and taking the most serious cerebral apoplexy as an example, the sequelae can greatly reduce the life self-care ability of patients and seriously affect the life quality of the patients and families thereof. Among the sequelae, the hemiplegia occurrence probability of the patient is highest, and clinic shows that scientific exercise rehabilitation training is matched with operation treatment and drug treatment, so that the probability of limb function recovery of the hemiplegia patient with cerebral apoplexy can be remarkably improved, the damaged nervous system of the patient in the cerebral apoplexy attack process can be repaired by timely repeated rehabilitation exercise training, and the exercise systems such as musculoskeletal and the like are strengthened, thereby being beneficial to the rehabilitation of the exercise function of the patient training side limbs.
Along with the development of chip technology, the cooperative robot has also achieved the development of great footage in miniaturization and intellectualization, and the rehabilitation robot is gradually replacing the traditional rehabilitation training which is led by a rehabilitation therapist due to the characteristics of flexibility, perfected rehabilitation mode, high interactivity, high interestingness and the like. Most rehabilitation robots existing in the market at present are used for rehabilitation of a single affected limb, however, most of cerebral apoplexy patients need rehabilitation training on upper and lower limbs on one side of the body. In order to realize rehabilitation training of different diseased parts, training systems such as a constant-speed muscle strength rehabilitation robot and the like are developed. The constant-speed muscle strength training system can realize multi-mode rehabilitation training of multiple joints, and can perform rehabilitation training of relevant items such as constant speed, equal length, equal tension, centrifugation, centripetal, continuous passive, proprioception, elastic resistance and the like aiming at six joints such as shoulder, elbow, wrist, hip, knee, ankle and the like. Aiming at the multi-joint and multi-mode training modes of different patients, the difficulty that the dynamic parameters of the output end of the rehabilitation robot are always changed is necessarily brought, and the control accuracy of the robot is reduced or even unstably under the moment modes such as power assistance and the like can be caused.
At present, a plurality of researches on the control of the isokinetic muscle rehabilitation robot are carried out at home and abroad. For example, in the patent application with the application number of 201010301048.8 and the invention of a constant-speed muscle force test system and a core control algorithm thereof, the interaction force between a patient and the output end of the rehabilitation robot is obtained through a moment sensor, the angle of the output end of the rehabilitation robot is obtained through an angular displacement sensor, and then the constant-speed movement of the robot is controlled according to the interaction force and the angle; the patent application with the application number of 201810660563.1 and the invention name of 'constant-speed muscle strength training system and control method thereof' acquires interaction force through a resistance strain gauge positioned on an output rotating shaft of a motor so as to complete the force compensation of a patient and the moment compensation in the motion process of the patient. Although the control accuracy of the rehabilitation robot can be improved to a certain extent by the method for controlling the rehabilitation robot according to the detected interaction force, dynamic parameters can be changed when a patient is replaced or the patient is replaced and the training side limbs are changed, if the method only depends on the interaction force and does not consider the dynamic parameters of the system, the ideal following of the rehabilitation robot to the movement of the patient training side limbs is difficult to realize, and the stability of the movement control of the rehabilitation robot cannot be ensured.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a rehabilitation robot control method, a system, equipment and a medium based on parameter identification, so as to improve the flexibility of cooperative interaction force control of a rehabilitation robot and a patient training side limb and the comfort level of the patient in the rehabilitation training process by introducing dynamic parameter identification, thereby improving the satisfaction degree of the patient on the rehabilitation training and improving the rehabilitation effect.
In order to achieve the above object, the present invention provides a rehabilitation robot control method based on kinetic parameter identification, the rehabilitation robot including a rehabilitation robot body, and a rehabilitation robot accessory for driving a training side limb of a patient to rotate around an output shaft of the rehabilitation robot body, the training side limb and the rehabilitation robot accessory being fixed together to form a synchronous motion system, the control method comprising:
when a dynamic parameter identification instruction is received, identifying the dynamic parameters of the synchronous motion system to obtain a dynamic parameter identification result;
acquiring the movement intention of the patient according to the measured interaction moment between the synchronous movement system and the rehabilitation robot body and the dynamic parameter identification result;
according to the movement intention of the patient, obtaining a driving moment required to be output by the rehabilitation robot body;
and generating a corresponding control instruction according to the driving moment, and sending the control instruction to the rehabilitation robot body.
In a preferred embodiment of the invention, the kinetic parameters include: the sum of the gravity vector and the static friction vector, the viscous friction vector, and the moment of inertia.
In a preferred embodiment of the present invention, the identifying the dynamic parameters of the synchronous motion system to obtain the dynamic parameter identification result includes:
carrying out online identification on the sum of the gravity vector and the static friction vector;
and carrying out online identification on the viscous friction vector and the rotational inertia according to an online identification result of the sum of the gravity vector and the static friction vector.
In a preferred embodiment of the present invention, the on-line identification of the sum of the gravity vector and the static friction vector includes:
controlling the rehabilitation robot body to drive the synchronous motion system to rotate to a plurality of specific angles so as to enable the interaction moment measured at each specific angle to be equal to the sum of the gravity vector and the static friction vector;
fitting a relation between the sum of the gravity vector and the static friction vector and the angle according to the angle values of the specific angles, so as to obtain an online identification result of the sum of the gravity vector and the static friction vector.
In a preferred embodiment of the present invention, the online identification of the viscous friction vector and the moment of inertia according to the online identification result of the sum of the gravity vector and the static friction vector includes:
establishing a system state equation as shown in the formula (1):
wherein omega k Represents the rotation angle of the synchronous motion system at the kth time, and tau k Representing the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, wherein k represents the kth sampling moment, f k Represents the viscous friction vector, T represents the sampling period, M k Representing moment of inertia at k, (D) k +G k ) Representing an identification result of the sum of the gravity vector and the static friction vector;
measuring angular velocity omega of rotation of synchronous motion system at kth time k ；
Measuring interaction moment tau between the synchronous motion system and the rehabilitation robot body at the kth moment k ；
For the measured angular velocity omega k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>
Obtaining the identification result of the moment of inertia at the kth time according to the following formulas (2) to (4)
Δω k ＝ω k -ω k-1 (3)
Wherein Deltaτ k ＝(τ k -τ k-1 ) Beta is preset;
unbiased estimation of the obtained interaction momentAngular velocity unbiased estimate +.>Identification result of moment of inertiaAnd the result of recognition (D k +G k ) Substituting (1) to obtain the identification result of the viscous friction vector at the kth time +.>
Judging the identification result of the moment of inertia at the kth timeIdentification result of said viscous friction vector +.>If the predetermined condition is satisfied, ending the flow when the predetermined condition is satisfied, and when the predetermined condition is not satisfied, letting k=k+1, and returning to perform the measurement of the angular velocity ω of rotation of the synchronous motion system at the kth time k 。
In a preferred embodiment of the invention, said pair of measured angular velocities ω k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>Comprising the following steps:
the angular velocity omega measured for the pair using an extended kalman filter k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>
In order to achieve the above object, the present invention further provides a rehabilitation robot control system based on kinetic parameter identification, the rehabilitation robot including a rehabilitation robot body, and a rehabilitation robot accessory for driving a training side limb of a patient to rotate around an output shaft of the rehabilitation robot body, the training side limb and the rehabilitation robot accessory being fixed together to form a synchronous motion system, the control system comprising:
the parameter identification module is used for identifying the dynamic parameters of the synchronous motion system when a dynamic parameter identification instruction is received, so as to obtain a dynamic parameter identification result;
the exercise intention acquisition module is used for acquiring the exercise intention of the patient according to the measured interaction moment between the synchronous exercise system and the rehabilitation robot body and the dynamic parameter identification result;
the driving moment acquisition module is used for acquiring driving moment required to be output by the rehabilitation robot body according to the movement intention of the patient;
and the control module is used for generating a corresponding control instruction according to the driving moment and sending the control instruction to the rehabilitation robot body.
In a preferred embodiment of the invention, the kinetic parameters include: the sum of the gravity vector and the static friction vector, the viscous friction vector, and the moment of inertia.
In a preferred embodiment of the present invention, the parameter identification module includes:
the first online identification unit is used for carrying out online identification on the sum of the gravity vector and the static friction vector;
and the second online identification unit is used for carrying out online identification on the viscous friction vector and the rotational inertia according to the online identification result of the sum of the gravity vector and the static friction vector.
In a preferred embodiment of the present invention, the first online identification unit is specifically configured to:
controlling the rehabilitation robot body to drive the synchronous motion system to rotate to a plurality of specific angles so as to enable the interaction moment measured at each specific angle to be equal to the sum of the gravity vector and the static friction vector;
fitting a relation between the sum of the gravity vector and the static friction vector and the angle according to the angle values of the specific angles, so as to obtain an online identification result of the sum of the gravity vector and the static friction vector.
In a preferred embodiment of the present invention, the second online identification unit is specifically configured to:
establishing a system state equation as shown in the formula (1):
wherein omega k Represents the rotation angle of the synchronous motion system at the kth time, and tau k Representing the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, wherein k represents the kth sampling moment, f k Represents the viscous friction vector, T represents the sampling period, M k Representing moment of inertia at k, (D) k +G k ) Representing an identification result of the sum of the gravity vector and the static friction vector;
measuring angular velocity omega of rotation of synchronous motion system at kth time k ；
Measuring the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth momentτ k ；
For the measured angular velocity omega k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>
Obtaining the identification result of the moment of inertia at the kth time according to the following formulas (2) to (4)
Δω k ＝ω k -ω k-1 (3)
Wherein Deltaτ k ＝(τ k -τ k - 1 ) Beta is preset;
unbiased estimation of the obtained interaction momentAngular velocity unbiased estimate +.>Identification result of moment of inertiaAnd the result of recognition (D k +G k ) Substituting (1) to obtain the identification result of the viscous friction vector at the kth time +.>
Judging the identification result of the moment of inertia at the kth timeIdentification result of said viscous friction vector +.>If the predetermined condition is satisfied, ending the flow when the predetermined condition is satisfied, and when the predetermined condition is not satisfied, letting k=k+1, and returning to perform the measurement of the angular velocity ω of rotation of the synchronous motion system at the kth time k 。
In a preferred embodiment of the present invention, the second online identification unit measures the angular velocity ω measured by the pair using an extended kalman filter k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>
To achieve the above object, the present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the steps of the aforementioned method when executing the computer program.
In order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned method.
By adopting the technical scheme, the invention has the following beneficial effects:
according to the invention, the dynamic parameter identification is introduced, so that when a rehabilitation robot changes a patient or a training side limb of the patient is changed, the dynamic parameter of a synchronous motion system formed by a rehabilitation robot accessory and the training side limb of the patient is identified, and then the interaction moment between the synchronous motion system and the rehabilitation robot body is combined, so that the movement intention of the patient can be accurately acquired; and then, according to the movement intention of the patient, obtaining the driving moment of the rehabilitation robot body, generating a corresponding control instruction according to the driving moment, and sending the control instruction to the rehabilitation robot body so as to control the rehabilitation robot body to drive the rehabilitation robot accessory and the patient training side limb to integrally move by the driving moment. Due to the fact that dynamic parameter identification is introduced, the flexibility of cooperative interaction force control of the rehabilitation robot and the patient training side limbs can be improved, comfort level of the patient in the rehabilitation training process is improved, satisfaction degree of the patient on rehabilitation training is improved, and rehabilitation effect is improved.
Drawings
FIG. 1 is a flowchart of a rehabilitation robot control method based on dynamic parameter identification in embodiment 1 of the present invention;
FIG. 2 is a flowchart of a rehabilitation robot control method based on dynamic parameter identification in embodiment 2 of the present invention;
FIG. 3 is a graph of the driving torque versus the theoretical driving torque obtained using the method of example 2 of the present invention;
FIG. 4 is a graph of drive torque versus theoretical drive torque identified using a prior art kinetic parameter identification method;
FIG. 5 is a block diagram of a rehabilitation robot control system based on dynamic parameter identification in embodiment 3 of the present invention;
FIG. 6 is a block diagram of a rehabilitation robot control system based on dynamic parameter identification in embodiment 4 of the present invention;
fig. 7 is a hardware architecture diagram of an electronic device in embodiment 5 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
Example 1
The embodiment provides a rehabilitation robot control method based on dynamic parameter identification. In this embodiment, the rehabilitation robot includes a rehabilitation robot body and a rehabilitation robot accessory including a binding structure and a rehabilitation robot arm. When the rehabilitation robot is used, a physical therapist firstly adjusts the rehabilitation robot to a position matched with a patient, and fixes the patient training side limb and the rehabilitation mechanical arm through the binding structure, so that a synchronous motion system is formed between the patient training side limb and the rehabilitation mechanical arm (the relative motion between the patient training side limb and the rehabilitation mechanical arm is negligible), and the rehabilitation mechanical arm drives the patient training side limb to rotate around an output shaft of the rehabilitation robot body; and then, the rehabilitation robot is regulated to a corresponding training mode, the human-computer cooperative interaction self-adaptive control cycle is started, meanwhile, prompts, games and the like related to rehabilitation can appear in a human-computer interaction interface configured by the rehabilitation robot body, and a patient can select in the interface according to own requirements.
In this embodiment, the rehabilitation robot is preferably a isokinetic muscle rehabilitation robot.
Based on the rehabilitation robot described above, the control method of the present embodiment is shown in fig. 1, and specifically includes the following steps:
s1, when a dynamic parameter identification instruction is received, identifying dynamic parameters of a synchronous motion system formed by the patient training side limbs and the rehabilitation robot, and obtaining a dynamic parameter identification result.
S2, acquiring the movement intention of the patient according to the interaction moment and the dynamic parameter identification result;
s3, acquiring the driving moment of the rehabilitation robot body according to the movement intention of the patient;
s4, generating a corresponding control instruction according to the driving moment, and sending the control instruction to the rehabilitation robot body so as to control the rehabilitation robot body to drive the rehabilitation robot accessory and the patient training side limb to integrally move by the driving moment.
According to the embodiment, the dynamic parameter identification is introduced, so that the flexibility of the cooperative interaction force control of the rehabilitation robot and the patient training side limbs is improved, the comfort level of the patient in the rehabilitation training process is increased, the satisfaction degree of the patient on the rehabilitation training is improved, and the rehabilitation effect is improved.
Example 2
This example is directed to a further improvement of example 1, and in particular, this example provides a preferred embodiment to the aforementioned step S1.
In the prior art, in order to implement dynamic parameter identification, a scheme of pre-establishing a fixed dynamic parameter identification model to perform parameter identification through the model is generally adopted. However, for a time-varying system with complex man-machine interaction such as rehabilitation training, an accurate model is difficult to build, and the scheme has complex data processing and higher requirements on hardware; moreover, in the case of changing a patient or changing a training side limb, because the weights of different persons or different limbs and the moment of inertia about the output shaft are extremely different, the conventional fixed dynamic parameter identification model is difficult to realize the ideal following of the rehabilitation robot to the patient training side limb motion and ensure the stability of the rehabilitation robot motion control under the constant-speed rehabilitation training model of multiple persons or multiple joints.
In this regard, the present embodiment provides an online identification method for identifying the dynamic parameters of the synchronous motion system formed by the patient training side limb and the rehabilitation robot, so as to obtain the dynamic parameter identification result.
In general, the kinetic system can be described using the following euler-lagrangian kinetic model:
wherein M (q) represents a rotational inertia matrix,represents the matrix of coriolis and centrifugal forces, G (q) represents the gravity vector, f represents the viscous friction vector, D represents the static friction vector,/v>Representing angular velocity, τ represents interaction torque.
Since the application scenario of the embodiment is a single-degree-of-freedom low-speed rehabilitation robot, the coriolis force and the centrifugal force of the rehabilitation robot can be omitted, so that the model can be simplified as follows:
in order to realize modeling of the dynamic characteristics of the patient training side limbs and the rehabilitation robot accessories, the interaction torque tau of the synchronous motion system and the rehabilitation robot body can be measured by a torque sensor in the parameter identification process, and the interaction torque tau is used as input in the parameter identification process, and the angular velocity of the motion of the systemAs system input, let->The simplified model can be linearized according to the defined input and output and rewritten into the state space equation +.>In the form of (a) can be obtained:
meanwhile, the output equation is written according to the canonical form y=cx+v of the state space equationWherein w and v represent the system noise and the measurement noise vector, respectively, and the covariance matrix of both can be represented by the formula q=cov (w) =e { ww T Sum r=cov (v) =e { vv) T Obtained separately, where E { } represents the desired calculation.
It should be understood that the object of parameter identification is to determine the identification results of three terms, M, f, (D+G). As shown in fig. 2, the present embodiment implements parameter identification by:
s11, when a dynamic parameter identification instruction is received, carrying out online identification on the sum of a gravity vector and a static friction vector in dynamic parameters, wherein the specific implementation process is as follows:
firstly, controlling the rehabilitation robot body to drive the synchronous motion system to rotate to a plurality of specific angles theta k So that the interaction moment measured at each specific angle is completely generated by the gravity vector and the static friction vector, namely (D k +G k )＝τ k 。
Then, according to the angle values of the specific angles, fitting the sum (D k +G k ) And angle theta k And obtaining the online identification result of the sum of the gravity vector and the static friction vector by the relational expression.
S12, carrying out online identification on the viscous friction vector and the rotational inertia according to an online identification result of the sum of the gravity vector and the static friction vector, wherein the implementation process is as follows:
s121, configuring the gravity vector and the static friction vectorInitial value M of three items of viscous friction vector and rotational inertia at 0 th moment 0 、f 0 、D 0 +G 0 。
S122, writing the system model of the state space equation modeDiscretizing by taking the sampling period T as a time scale to obtain a system state equation shown as a formula (1):
wherein omega k Represents the rotation angle of the synchronous motion system at the kth time, and tau k Representing the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, wherein k represents the kth sampling moment, f k Represents the viscous friction vector, T represents the sampling period, M k Representing moment of inertia at k, (D) k +G k ) And the identification result of the sum of the gravity vector and the static friction vector obtained in the previous step is shown.
Likewise, the system output equation may also be discretized to y k ＝ω k Where k represents the specific moment of the kth sample, introducing the interaction torque τ into the system state equation, which can also be expressed as:
s123, measuring the angular velocity omega of the rotation of the synchronous motion system at the kth moment k 。
Specifically, the present embodiment measures the angular velocity ω by an encoder k 。
S124, measuring the interaction torque tau between the synchronous motion system and the rehabilitation robot body at the kth moment k 。
Specifically, the present embodiment measures the interactive force through a torque sensorMoment τ k 。
S125, for the measured angular velocity omega k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>
It should be appreciated that the encoder and torque sensor measurements may be subject to error, and that the present embodiment preferably uses an extended Kalman filter for measuring the angular velocity ω k And interaction moment tau k Filtering, wherein the specific working process of the Kalman filter is as follows:
parameter initialization is performed by the following formula:
parameter predictions were made by the following formula:
parameter updating is performed by the following formula:
wherein, as previously described, q=cov (w) =e { ww T W represents a systematic noise vector whose value is preconfigured, r=cov (v) =e { vv) T V represents a measurement noise vector, the value of which is preconfigured.
S126, in the motion process, the angular acceleration change value can be defined by the secondary difference of the angular velocity, and meanwhile, the angular acceleration change value is interacted with the interaction moment tau according to the physical meaning k Rate of change Δτ of (2) k ＝(τ k -τ k-1 ) And a set adaptive lawIn a linear relationship, i.e. can be expressed as ω k ＝2ω k-1 -ω k-2 +b k-1 Δτ k-1 The self-adaptive law of inertia of the dynamic system is deduced by referring to the dynamic system moment of inertia identification method of Lang discrete time method as follows:
where β is the coefficient of variation of the adaptive law, whose value is preconfigured, the rate of change of angular velocity can be defined as Δω k ＝ω k -ω k-1 . Will beΔω k ＝ω k -ω k-1 Substitution equation->The identification result of the moment of inertia at the kth moment can be solved>
S127, the above obtained(D k +G k ) Substituting the above-mentioned parameters into the formula (1) to obtain the identification result +.>The identification of the system dynamic parameters in a sampling period T is completed, and the system inertia estimated at the moment k is +.>And viscous friction->Will continue as a variable to be used in the parameter estimation at the next instant, i.e. at time k+1, the sum of the gravity vector and the static friction vector (D k +G k ) The online identification result of the system is unchanged.
S128, judging the identification result of the moment of inertia at the kth timeRecognition result of the viscous friction vectorIf the predetermined condition is satisfied, when the predetermined condition is satisfied, the identification process is ended, the identification result is saved, and when the predetermined condition is not satisfied, k=k+1 is set, and the step S123 is executed again.
In this embodiment, the predetermined condition is configured to: identification result of moment of inertia at k timeIdentification result of said viscous friction vector +.>All converge, i.e.)>And->Wherein ε M And epsilon f Respectively M k And f k Allowed identification errors.
According to the embodiment, dynamic parameters are identified step by step according to the characteristics of the dynamic parameters, an extended Kalman filter is introduced when the rotational inertia and the viscous friction vector are identified, the rotational inertia obtained through a self-adaptive estimation algorithm is used as one parameter of an extended Kalman filter load torque identification algorithm to participate in system parameter evaluation, so that decoupling of driving moment to the rotational inertia is realized, the problem that the online identification precision of the system parameters is reduced after the rotational inertia of a dynamic system is changed in a traditional method is avoided, and the requirement of multi-joint rehabilitation training of a constant-speed muscle force rehabilitation platform is completely met.
The graph of the driving moment and the theoretical driving moment obtained by the method of the embodiment is shown in fig. 3, and the graph of the driving moment and the theoretical driving moment obtained by the identification of the existing kinetic parameter identification method is shown in fig. 4, so that the method of the embodiment has better performance.
Example 3
The embodiment provides a rehabilitation robot control system based on dynamic parameter identification, as shown in fig. 5, the control system specifically includes: a parameter identification module 11, a movement intention acquisition module 12, a driving moment acquisition module 13 and a control module 14, wherein:
the parameter identification module 11 is configured to identify a kinetic parameter of a synchronous motion system formed by the patient training side limb and the rehabilitation robot, so as to obtain a kinetic parameter identification result.
The exercise intention obtaining module 12 is configured to obtain an exercise intention of the patient according to the interaction moment and the dynamic parameter identification result;
the driving moment acquisition module 13 is used for acquiring the driving moment of the rehabilitation robot body according to the movement intention of the patient;
the control module 14 is configured to generate a corresponding control instruction according to the driving torque, and send the control instruction to the rehabilitation robot body, so as to control the rehabilitation robot body to drive the rehabilitation robot accessory and the patient training side limb to integrally move with the driving torque.
According to the embodiment, the dynamic parameter identification is introduced, so that the flexibility of the cooperative interaction force control of the rehabilitation robot and the patient training side limbs is improved, the comfort level of the patient in the rehabilitation training process is increased, the satisfaction degree of the patient on the rehabilitation training is improved, and the rehabilitation effect is improved.
Example 4
This example is directed to a further improvement of example 3, and in particular, this example provides a preferred implementation of the parameter identification module 11 described above.
In the prior art, in order to implement dynamic parameter identification, a scheme of pre-establishing a fixed dynamic parameter identification model to perform parameter identification through the model is generally adopted. However, for a time-varying system with complex man-machine interaction such as rehabilitation training, an accurate model is difficult to build, and the scheme has complex data processing and higher requirements on hardware; moreover, in the case of changing a patient or changing a training side limb, because the weights of different persons or different limbs and the moment of inertia about the output shaft are extremely different, the conventional fixed dynamic parameter identification model is difficult to realize the ideal following of the rehabilitation robot to the patient training side limb motion and ensure the stability of the rehabilitation robot motion control under the constant-speed rehabilitation training model of multiple persons or multiple joints.
In this regard, the present embodiment provides an online identification method for identifying the dynamic parameters of the synchronous motion system formed by the patient training side limb and the rehabilitation robot, so as to obtain the dynamic parameter identification result.
In general, the kinetic system can be described using the following euler-lagrangian kinetic model:
wherein M (q) represents a rotational inertia matrix,represents the matrix of coriolis and centrifugal forces, G (q) represents the gravity vector, f represents the viscous friction vector, D represents the static friction vector,/v>Representing angular velocity, τ represents interaction torque.
Since the application scenario of the embodiment is a single-degree-of-freedom low-speed rehabilitation robot, the coriolis force and the centrifugal force of the rehabilitation robot can be omitted, so that the model can be simplified as follows:
in order to realize modeling of the dynamic characteristics of the patient training side limbs and the rehabilitation robot accessories, the interaction torque tau of the synchronous motion system and the rehabilitation robot body can be measured by a torque sensor in the parameter identification process, and the interaction torque tau is used as input in the parameter identification process, and the angular velocity of the motion of the systemAs system input, let->The simplified model can be linearized according to the defined input and output and rewritten into the state space equation +.>In the form of (a) can be obtained:
meanwhile, the output equation is written according to the canonical form y=cx+v of the state space equationWherein w and v represent the system noise and the measurement noise vector, respectively, and the covariance matrix of both can be represented by the formula q=cov (w) =e { ww T Sum r=cov (v) =e { vv) T Obtained separately, where E { } represents the desired calculation.
It should be understood that the object of parameter identification is to determine the identification results of three terms, M, f, (D+G). As shown in fig. 6, the parameter identification module of the present embodiment specifically includes a first online identification unit 111 and a second online identification unit 112.
The first online identifying unit 111 is configured to identify the sum of the gravity vector and the static friction vector in the kinetic parameters online, and specifically comprises the following steps:
firstly, controlling the rehabilitation robot body to drive the synchronous motion system to rotate to a plurality of specific angles theta k So that the interaction moment measured at each specific angle is completely generated by the gravity vector and the static friction vector, namely (D k +G k )＝τ k 。
Then, according to the angle values of the specific angles, fitting the sum (D k +G k ) And angle theta k And obtaining the online identification result of the sum of the gravity vector and the static friction vector by the relational expression.
The second online identifying unit 112 is configured to identify the viscous friction vector and the moment of inertia online according to the online identification result of the sum of the gravity vector and the static friction vector, and the specific implementation process refers to steps S121-S128.
According to the embodiment, dynamic parameters are identified step by step according to the characteristics of the dynamic parameters, an extended Kalman filter is introduced when the rotational inertia and the viscous friction vector are identified, the rotational inertia obtained through a self-adaptive estimation algorithm is used as one parameter of an extended Kalman filter load torque identification algorithm to participate in system parameter evaluation, so that decoupling of driving moment to the rotational inertia is realized, the problem that the online identification precision of the system parameters is reduced after the rotational inertia of a dynamic system is changed in a traditional method is avoided, and the requirement of multi-joint rehabilitation training of a constant-speed muscle force rehabilitation platform is completely met.
Example 5
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (for example, may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the rehabilitation robot control method based on kinetic parameter identification provided in embodiment 1 or 2 when executing the computer program.
Fig. 7 shows a schematic diagram of the hardware structure of the present embodiment, and as shown in fig. 7, the electronic device 9 specifically includes:
at least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
the bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 also includes a program/utility 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing such as the rehabilitation robot control method based on kinetic parameter identification provided in embodiment 1 or 2 of the present invention by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module according to embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the rehabilitation robot control method based on kinetic parameter identification provided in embodiment 1 or 2.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be realized in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the kinetic parameter identification based rehabilitation robot control method as described in embodiment 1 or 2, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (3)
1. Rehabilitation robot control system based on kinetic parameter discernment, rehabilitation robot includes rehabilitation robot body and is used for driving patient's training side limb around rehabilitation robot body's output shaft pivoted rehabilitation robot accessory, training side limb with rehabilitation robot accessory is fixed together and is formed synchronous motion system, its characterized in that, control system includes:
the parameter identification module is used for identifying the dynamic parameters of the synchronous motion system when a dynamic parameter identification instruction is received, so as to obtain a dynamic parameter identification result;
the exercise intention acquisition module is used for acquiring the exercise intention of the patient according to the measured interaction moment between the synchronous exercise system and the rehabilitation robot body and the dynamic parameter identification result;
the driving moment acquisition module is used for acquiring driving moment required to be output by the rehabilitation robot body according to the movement intention of the patient;
the control module is used for generating corresponding control instructions according to the driving moment and sending the control instructions to the rehabilitation robot body;
the kinetic parameters include: the sum of the gravity vector and the static friction vector, the viscous friction vector, and the moment of inertia;
the parameter identification module comprises:
the first online identification unit is used for carrying out online identification on the sum of the gravity vector and the static friction vector;
the second online identification unit is used for carrying out online identification on the viscous friction vector and the moment of inertia according to the online identification result of the sum of the gravity vector and the static friction vector;
the second online identification unit is specifically configured to:
establishing a system state equation as shown in formula (1):
wherein omega k Represents the rotation angle of the synchronous motion system at the kth time, and tau k Representing the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, wherein k represents the kth sampling moment, f k Represents the viscous friction vector, T represents the sampling period, M k Representing moment of inertia at k, (D) k +G k ) Representing an identification result of the sum of the gravity vector and the static friction vector;
measuring angular velocity omega of rotation of synchronous motion system at kth time k ；
Measuring interaction moment tau between the synchronous motion system and the rehabilitation robot body at the kth moment k ；
For the measured angular velocity omega k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>
Obtaining the identification result of the moment of inertia at the kth time according to the following formulas (2) to (4)
Δω k ＝ω k -ω k-1 (3)
Wherein Deltaτ k ＝(τ k -τ k-1 ) Beta is preset and the self-adaptive law is setIs in a linear relationship;
unbiased estimation of the obtained interaction momentAngular velocity unbiased estimate +.>Identification result of moment of inertia->And the result of recognition (D k +G k ) Substituting (1) to obtain the identification result of the viscous friction vector at the kth time +.>
Judging the identification result of the moment of inertia at the kth timeIdentification result of said viscous friction vector +.>If the predetermined condition is satisfied, ending the flow when the predetermined condition is satisfied, and when the predetermined condition is not satisfied, letting k=k+1, and returning to perform the measurement of the angular velocity ω of rotation of the synchronous motion system at the kth time k 。
2. The rehabilitation robot control system based on kinetic parameter identification according to claim 1, wherein the first online identification unit is specifically configured to:
controlling the rehabilitation robot body to drive the synchronous motion system to rotate to a plurality of specific angles so as to enable the interaction moment measured at each specific angle to be equal to the sum of the gravity vector and the static friction vector;
fitting a relation between the sum of the gravity vector and the static friction vector and the angle according to the angle values of the specific angles, so as to obtain an online identification result of the sum of the gravity vector and the static friction vector.
3. The rehabilitation robot control system based on kinetic parameter identification according to claim 1, wherein the second online identification unit uses an extended kalman filter for the angular velocity ω measured by the pair k And interaction moment tau k Filtering to obtain corresponding unbiased estimated angular velocityAnd an unbiased estimate of the interaction moment +.>
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