CN113189865A - Rehabilitation robot control method, system, equipment and medium based on kinetic parameter identification - Google Patents
Rehabilitation robot control method, system, equipment and medium based on kinetic 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 kinetic 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 kinetic parameter identification instruction is received, identifying kinetic parameters of the synchronous motion system to obtain a kinetic parameter identification result; acquiring the movement intention of the patient according to the measured interaction torque between the synchronous movement system and the rehabilitation robot body and the kinetic parameter identification result; acquiring driving torque required to be output by the rehabilitation robot body according to the movement intention of the patient; and generating a corresponding control command according to the driving moment, and sending the control command to the rehabilitation robot body. The invention improves the flexibility of the cooperative interaction force control of the rehabilitation robot and the training side limb of the patient.
Description
Technical Field
The invention relates to the field of robot control, in particular to a rehabilitation robot control method, a rehabilitation robot control system, rehabilitation robot control equipment and a rehabilitation robot control medium based on kinetic parameter identification.
Background
With the increasing aging degree of population, the incidence of diseases of the aging population is increasing, taking the most serious stroke as an example, according to the data of the national statistical bureau, the number of deaths caused by stroke in China is up to 140.3/10 ten thousand in 2019, even if the patients are timely cured, about 75 percent of the patients still leave a plurality of sequelae with different degrees after the stroke occurs, and the sequelae can greatly reduce the self-care ability of the patients and seriously affect the life quality of the patients and the family members. In such a plurality of sequelae, the probability of hemiplegia of a patient is highest, and clinical application shows that scientific exercise rehabilitation training is matched with operation treatment and drug treatment, so that the probability of the function recovery of the limbs of a patient with hemiplegia due to stroke can be obviously improved, the damaged nervous system of the patient in the stroke disease process can be repaired by timely repeated rehabilitation exercise training, the motor systems such as musculoskeletal system and the like are strengthened, and the rehabilitation of the exercise machine for training the lateral limbs of the patient is facilitated.
With the development of chip technology, cooperative robots have also gained a great deal of development in miniaturization and intellectualization, and rehabilitation robots are gradually replacing traditional rehabilitation training which is dominated by rehabilitation therapists due to the characteristics of flexible and complete rehabilitation modes, high interactivity and interestingness and the like. The existing rehabilitation robots in the market are mainly used for rehabilitating a single affected limb, but the upper limb and the lower limb of the body of most stroke patients need to be rehabilitated and trained simultaneously. In order to realize rehabilitation training of different diseased parts, training systems such as isokinetic muscle strength rehabilitation robots and the like are available. The constant-speed muscle strength training system can realize multi-joint multi-modal rehabilitation training, and can perform constant-speed, equal-length, equal-tension, centrifugal, centripetal, continuous passive, proprioceptive, elastic resistance and other related items of rehabilitation training for six joints such as shoulders, elbows, wrists, hips, knees and ankles. Aiming at multi-joint and multi-mode training modes of different patients, the difficulty that the kinetic parameters of the output end of the rehabilitation robot are changed all the time is inevitably brought, and the control precision of the robot is reduced or even unstable under the force-assisting and other torque modes.
At present, some researches aiming at the control of the isokinetic muscle strength 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 name of "isokinetic muscle force testing system and core control algorithm thereof", the interaction force between a patient and the output end of a rehabilitation robot is acquired through a torque sensor, the angle of the output end of the rehabilitation robot is acquired through an angular displacement sensor, and then the robot is controlled to move at the same speed according to the interaction force and the angle; the patent application with the application number of 201810660563.1 and the title of 'a constant velocity muscle strength training system and a control method thereof' obtains interaction force through a resistance strain gauge positioned on an output rotating shaft of a motor so as to complete the compensation of the muscle strength of a patient and the moment in the motion process of the muscle strength. Although the control precision of the rehabilitation robot can be improved to a certain extent by the method for controlling according to the detected interaction force, when a patient is replaced or the training side limb of the patient is replaced, the kinetic parameters are changed, if only the interaction force is relied on and the system kinetic parameters are not considered, the ideal following of the rehabilitation robot on the motion of the training side limb of the patient is difficult to realize, and the stability of the motion control of the rehabilitation robot cannot be ensured.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, an object of the present invention is to provide a method, a system, a device and a medium for controlling a rehabilitation robot based on parameter identification, so as to improve the compliance of the rehabilitation robot and the control of the cooperative interaction force of the lateral limb of the patient training by introducing the kinetic parameter identification, and increase the comfort of the patient during the rehabilitation training process, thereby improving the satisfaction 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 including:
when a kinetic parameter identification instruction is received, identifying kinetic parameters of the synchronous motion system to obtain a kinetic parameter identification result;
acquiring the movement intention of the patient according to the measured interaction torque between the synchronous movement system and the rehabilitation robot body and the kinetic parameter identification result;
acquiring driving torque required to be output by the rehabilitation robot body according to the movement intention of the patient;
and generating a corresponding control command according to the driving moment, and sending the control command to the rehabilitation robot body.
In a preferred embodiment of the invention, said kinetic parameters comprise: 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 kinetic parameters of the synchronous motion system to obtain a kinetic parameter identification result includes:
performing online identification on the sum of the gravity vector and the static friction vector;
and according to the online identification result of the sum of the gravity vector and the static friction vector, performing online identification on the viscous friction vector and the rotational inertia.
In a preferred embodiment of the present invention, the online identifying 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 that the interaction torque measured at each specific angle is equal to the sum of the gravity vector and the static friction vector;
and fitting a relational expression between the sum of the gravity vector and the static friction vector and the angle according to the angle values of the specific angles, thereby obtaining 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 recognizing the viscous friction vector and the moment of inertia according to the online recognition result of the sum of the gravity vector and the static friction vector includes:
establishing a system state equation shown as the formula (1):
wherein, ω iskRepresenting the angle of rotation, τ, of the synchronous moving system at the time kkRepresenting the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, k representing the kth sampling moment, fkRepresenting the viscous friction vector, T representing the sampling period, MkRepresents the moment of inertia at time k, (D)k+Gk) Representing the identification result of the sum of the gravity vector and the static friction vector;
measuring the angular velocity omega of the rotation of the synchronous motion system at the kth momentk；
Measuring the interaction torque tau between the synchronous motion system and the rehabilitation robot body at the kth momentk；
For the measured angular velocity ωkAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
Obtaining the identification result of the moment of inertia at the k-th moment according to the following formulas (2) to (4)
Δωk＝ωk-ωk-1 (3)
Wherein, Δ τk＝(τk-τk-1) Beta is preset;
unbiased estimation quantity of the obtained interaction torque
Unbiased estimate of angular velocity
Identification result of moment of inertia
And the result (D) of the recognition of the sum of the gravity vector and the static friction vectork+Gk) Substituting formula (1) to obtain the identification result of the viscous friction vector at the k-th moment
Judging the identification result of the moment of inertia at the k moment
The result of identification of the viscous friction vector
If the predetermined condition is met, ending the process when the predetermined condition is met, and if the predetermined condition is not met, enabling k to be k +1 and returning to execute the measurement of the angular speed omega of the rotation of the synchronous motion system at the k momentk。
In a preferred embodiment of the invention, said pair of measured angular velocities ω iskAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
The method comprises the following steps:
using an extended Kalman filter to measure the angular velocity omegakAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
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 including:
the parameter identification module is used for identifying the kinetic parameters of the synchronous motion system when receiving a kinetic parameter identification instruction to obtain a kinetic parameter identification result;
the exercise intention acquisition module is used for acquiring the exercise intention of the patient according to the measured interaction torque between the synchronous motion system and the rehabilitation robot body and the kinetic parameter identification result;
the driving moment acquisition module is used for acquiring the 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 command according to the driving moment and sending the control command to the rehabilitation robot body.
In a preferred embodiment of the invention, said kinetic parameters comprise: 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 comprises:
the first online identification unit is used for online identifying the sum of the gravity vector and the static friction vector;
and the second online identification unit is used for online identifying 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 that the interaction torque measured at each specific angle is equal to the sum of the gravity vector and the static friction vector;
and fitting a relational expression between the sum of the gravity vector and the static friction vector and the angle according to the angle values of the specific angles, thereby obtaining 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 shown as the formula (1):
wherein, ω iskRepresenting the angle of rotation, τ, of the synchronous moving system at the time kkRepresenting the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, k representing the kth sampling moment, fkRepresenting the viscous friction vector, T representing the sampling period, MkRepresents the moment of inertia at time k, (D)k+Gk) Representing the identification result of the sum of the gravity vector and the static friction vector;
measuring rotation of the synchronous moving system at time kAngular velocity omegak；
Measuring the interaction torque tau between the synchronous motion system and the rehabilitation robot body at the kth momentk；
For the measured angular velocity ωkAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
Obtaining the identification result of the moment of inertia at the k-th moment according to the following formulas (2) to (4)
Δωk＝ωk-ωk-1 (3)
Wherein, Δ τk＝(τk-τk-1) Beta is preset;
unbiased estimation quantity of the obtained interaction torque
Unbiased estimate of angular velocity
Identification result of moment of inertia
And the direction of gravityRecognition of the sum of the quantity and the static friction vector (D)k+Gk) Substituting formula (1) to obtain the identification result of the viscous friction vector at the k-th moment
Judging the identification result of the moment of inertia at the k moment
The result of identification of the viscous friction vector
If the predetermined condition is met, ending the process when the predetermined condition is met, and if the predetermined condition is not met, enabling k to be k +1 and returning to execute the measurement of the angular speed omega of the rotation of the synchronous motion system at the k momentk。
In a preferred embodiment of the present invention, the second online identification unit uses an extended kalman filter to measure the angular velocity ω of the pairkAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
In order to achieve the above object, the present invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the 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:
the invention introduces the kinetic parameter identification, can identify the kinetic parameters of the synchronous motion system formed by the rehabilitation robot accessory and the patient training side limb when the rehabilitation robot carries out patient replacement or the training side limb of the patient, and can accurately acquire the motion intention of the patient by combining the interactive torque between the synchronous motion system and the rehabilitation robot body; and then, according to the movement intention of the patient, obtaining a driving moment of the rehabilitation robot body, generating a corresponding control command according to the driving moment, and sending the control command 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 move integrally by the driving moment. Because the dynamic parameter identification is introduced, the flexibility of the cooperative interaction force control of the rehabilitation robot and the training side limbs of the patient can be 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.
Drawings
Fig. 1 is a flowchart of a rehabilitation robot control method based on kinetic parameter identification in embodiment 1 of the present invention;
fig. 2 is a flowchart of a rehabilitation robot control method based on kinetic parameter identification according to embodiment 2 of the present invention;
FIG. 3 is a graph of drive torque versus theoretical drive torque obtained using the method of example 2 of the present invention;
FIG. 4 is a graph of driving torque versus theoretical driving torque identified using a prior art kinetic parameter identification method;
fig. 5 is a block diagram of a rehabilitation robot control system based on kinetic parameter identification according to embodiment 3 of the present invention;
fig. 6 is a block diagram of a rehabilitation robot control system based on kinetic parameter identification according to 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
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present 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 and all possible combinations of one or more of the associated listed items.
Example 1
The embodiment provides a rehabilitation robot control method based on kinetic parameter identification. In this embodiment, the rehabilitation robot includes the rehabilitation robot body and the rehabilitation robot accessory, and this rehabilitation robot accessory includes ties up structure and rehabilitation 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 training side limbs of the patient and the rehabilitation mechanical arm through a binding structure, so that the training side limbs and the rehabilitation mechanical arm form a synchronous motion system (the relative motion between the training side limbs and the rehabilitation mechanical arm can be ignored), and the rehabilitation mechanical arm drives the training side limbs of the patient to rotate around an output shaft of the rehabilitation robot body; then, the rehabilitation robot is adjusted to a corresponding training mode, a man-machine cooperative interaction self-adaptive control periodic cycle is started, meanwhile, prompts, games and the like related to rehabilitation can appear in a man-machine interaction interface configured on the rehabilitation robot body, and a patient can select from the interface according to own requirements.
In the present embodiment, the rehabilitation robot is preferably an isokinetic muscle strength 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:
and S1, when a kinetic parameter identification instruction is received, identifying the kinetic parameters of the synchronous motion system formed by the patient training side limb and the rehabilitation robot to obtain a kinetic parameter identification result.
S2, acquiring the movement intention of the patient according to the interactive moment and the kinetic parameter identification result;
s3, acquiring the driving moment of the rehabilitation robot body according to the movement intention of the patient;
and S4, generating a corresponding control command according to the driving moment, and sending the control command 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.
The embodiment identifies by introducing the kinetic parameters, improves the flexibility of the cooperative interaction force control of the rehabilitation robot and the training side limbs of the patient, increases the comfort level of the patient in the rehabilitation training process, improves the satisfaction degree of the patient on the rehabilitation training, and improves the rehabilitation effect.
Example 2
This example is a further improvement over example 1, and specifically provides a preferred implementation for the foregoing step S1.
In the prior art, in order to realize the kinetic parameter identification, a fixed kinetic parameter identification model is generally established in advance, so as to perform parameter identification through the model. However, an accurate model is difficult to establish for a time-varying system with complex man-machine interaction, such as rehabilitation training, and the scheme has complex data processing and higher requirement on hardware; moreover, when a patient is replaced or a training side limb is replaced, because the weight of different people or different limbs and the difference of the rotational inertia around the output shaft are extremely large, the traditional fixed dynamic parameter identification model is difficult to realize ideal following of the rehabilitation robot to the motion of the training side limb of the patient and ensure the stability of the motion control of the rehabilitation robot under a multi-person or multi-joint constant-speed rehabilitation training model.
In this regard, the present embodiment provides an online identification method for identifying the kinetic parameters of the synchronous motion system formed by the patient training side limb and the rehabilitation robot, so as to obtain the kinetic parameter identification result.
In general, the kinetic system can be described using the euler-lagrange kinetic model as follows:
wherein M (q) represents a rotational inertia matrix,
representing a matrix of coriolis forces and centrifugal forces, g (q) representing a gravity vector, f representing a viscous friction vector, D representing a static friction vector,
indicating angular velocity and tau the interaction moment.
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 may not be considered, so the model may be simplified as follows:
in order to realize the modeling of the dynamic characteristics of the training side limbs of the patient and the accessories of the rehabilitation robot, the interactive moment tau between the synchronous motion system and the rehabilitation robot body can be measured by a moment sensor in the parameter identification process and used as input in the parameter identification process, and the angular speed of the motion of the system
As system input, order
According to the standardThe simplified model can be linearized by the semantic input and output and rewritten into a state space equation
In the form of (a), it is possible to obtain:
at the same time, the output equation is also written in terms of the canonical form of the state space equation, y ═ cx + v
Where w and v represent the system noise and the measurement noise vector, respectively, and the covariance matrix of the two can be represented by the formula Q ═ cov (w) ═ E { wwTAnd R ═ cov (v) ═ E { vv }TGet, respectively, where E { } represents the desired calculation.
It should be understood that the goal of parameter identification is to determine the identification results of the three items M, f, (D + G).
As shown in fig. 2, the present embodiment realizes parameter identification by the following steps:
s11, when receiving the dynamic parameter identification instruction, carrying out online identification on the sum of the gravity vector and the static friction vector in the dynamic parameter, specifically realizing the following process:
firstly, controlling the rehabilitation robot body to drive the synchronous motion system to rotate to a plurality of specific angles thetakSuch that the interaction torque measured at each of said specific angles is entirely generated by said gravity vector and said static friction vector, i.e. (D)k+Gk)＝τk。
Then, the sum (D) of the gravity vector and the static friction vector is fitted according to the angle values of the specific anglesk+Gk) And angle thetakThe online identification result of the sum of the gravity vector and the static friction vector can be obtained through the relational expression between the gravity vector and the static friction vector.
S12, performing 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, wherein the online identification is specifically realized by the following steps:
s121, configuring initial values M of the sum of the gravity vector and the static friction vector, the viscous friction vector and the moment of inertia at the 0 th moment0、f0、D0+G0。
S122, writing the system model of the state space equation mode
Discretizing by taking the sampling period T as a time scale, a system state equation shown as a formula (1) can be obtained:
wherein, ω iskRepresenting the angle of rotation, τ, of the synchronous moving system at the time kkRepresenting the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, k representing the kth sampling moment, fkRepresenting the viscous friction vector, T representing the sampling period, MkRepresents the moment of inertia at time k, (D)k+Gk) Representing the previously obtained identification result of the sum of the gravity vector and the static friction vector.
Likewise, the system output equation may also be discretized into yk＝ωkAnd k represents the specific moment of the kth sampling, and the interaction moment tau is introduced into a system state equation, which can also be expressed as:
s123, measuring the angular speed omega of the rotation of the synchronous motion system at the kth momentk。
Specifically, the present embodiment measures the angular velocity ω by an encoderk。
S124, measuring the kth moment, wherein the synchronous motion system and the rehabilitation machineInteraction moment tau between human bodiesk。
Specifically, the present embodiment measures the interaction torque τ through a torque sensork。
S125, measuring the obtained angular velocity omegakAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
It should be understood that the measured values of the encoder and the torque sensor may have errors, and in order to eliminate the errors, the embodiment preferably uses the extended kalman filter to measure the angular velocity ωkAnd interaction moment taukAnd filtering, wherein the specific working process of the Kalman filter is as follows:
parameter initialization is performed by the following formula:
parameter prediction is performed by the following formula:
the parameter update is performed by the following formula:
wherein, as mentioned above, Q ═ cov (w) ═ E { wwTW represents a system noise vector, and the value thereof is preset, R ═ cov (v) ═ E { vv }TV denotes 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 quadratic difference of the angular velocity, and simultaneously, the angular acceleration change value and the interaction torque tau are in physical significancekRate of change Δ τ ofk＝(τk-τk-1) And the set adaptive law
In a linear relationship, i.e. can be expressed as ωk＝2ωk-1-ωk-2+bk-1Δτk-1Referring to a dynamic system rotational inertia identification method of Landau discrete time method, the self-adaptive law of the dynamic system inertia is deduced to be:
wherein beta is the variation coefficient of the adaptive law, the value of which is configured in advance, and the angular speed variation rate can be defined as delta omegak＝ωk-ωk-1. Will be provided with
Δωk＝ωk-ωk-1Substitution equation
The identification result of the moment of inertia at the kth moment can be solved
S127, mixing the obtained
Substituting formula (1) to obtain the identification result of the viscous friction vector at the k-th moment
Thus, the identification of the dynamic parameters of the system in a sampling period T is completed, and the system inertia is obtained by estimation at the moment k
And viscous friction force
Will continue to be used as a variable in the parameter estimation at the next moment, i.e. at the moment k +1, at each moment the sum (D) of the gravity vector and the static friction vectork+Gk) The online identification result is not changed.
S128, judging the identification result of the moment of inertia at the k moment
The result of identification of the viscous friction vector
And if the predetermined condition is satisfied, ending the identification process, saving the identification result, and if the predetermined condition is not satisfied, setting k to k +1, and returning to execute the step S223.
In this embodiment, the predetermined condition is configured to: identification result of the moment of inertia at the k-th moment
The result of identification of the viscous friction vector
Are all converged, i.e
And
wherein epsilonMAnd εfAre each MkAnd fkAllowed recognition error.
According to the embodiment, kinetic parameters are identified step by step according to characteristics of the kinetic parameters, an extended Kalman filter is introduced when the rotational inertia and viscous friction vectors are identified, the rotational inertia obtained through a self-adaptive estimation algorithm is used as a parameter of an extended Kalman filter load torque identification algorithm to participate in system parameter evaluation, so that decoupling of driving torque to the rotational inertia is achieved, the problem that the online identification precision of system parameters is reduced after the rotational inertia of a kinetic system is changed in a traditional method can be solved, and the requirements of multi-joint rehabilitation training of a constant velocity muscle force rehabilitation platform are completely met.
The graph of the driving torque and the theoretical driving torque obtained by the method of the embodiment is shown in fig. 3, and the graph of the driving torque and the theoretical driving torque obtained by the identification by the conventional kinetic parameter identification method is shown in fig. 4.
Example 3
The present embodiment provides a rehabilitation robot control system based on kinetic parameter identification, as shown in fig. 5, the control system specifically includes: a parameter identification module 11, an exercise intention acquisition module 12, a driving torque acquisition module 13, and a control module 14, wherein:
the parameter identification module 11 is used for identifying the kinetic parameters of the synchronous motion system formed by the patient training side limb and the rehabilitation robot to obtain the kinetic parameter identification result.
The exercise intention acquisition module 12 is configured to acquire an exercise intention of the patient according to the interaction moment and the kinetic 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.
The embodiment identifies by introducing the kinetic parameters, improves the flexibility of the cooperative interaction force control of the rehabilitation robot and the training side limbs of the patient, increases the comfort level of the patient in the rehabilitation training process, improves the satisfaction degree of the patient on the rehabilitation training, and improves the rehabilitation effect.
Example 4
This embodiment is a further improvement of embodiment 3, and specifically, this embodiment provides a preferred implementation for the aforementioned parameter identification module 11.
In the prior art, in order to realize the kinetic parameter identification, a fixed kinetic parameter identification model is generally established in advance, so as to perform parameter identification through the model. However, an accurate model is difficult to establish for a time-varying system with complex man-machine interaction, such as rehabilitation training, and the scheme has complex data processing and higher requirement on hardware; moreover, when a patient is replaced or a training side limb is replaced, because the weight of different people or different limbs and the difference of the rotational inertia around the output shaft are extremely large, the traditional fixed dynamic parameter identification model is difficult to realize ideal following of the rehabilitation robot to the motion of the training side limb of the patient and ensure the stability of the motion control of the rehabilitation robot under a multi-person or multi-joint constant-speed rehabilitation training model.
In this regard, the present embodiment provides an online identification method for identifying the kinetic parameters of the synchronous motion system formed by the patient training side limb and the rehabilitation robot, so as to obtain the kinetic parameter identification result.
In general, the kinetic system can be described using the euler-lagrange kinetic model as follows:
wherein M (q) represents a rotational inertia matrix,
representing a matrix of coriolis forces and centrifugal forces, g (q) representing a gravity vector, f representing a viscous friction vector, D representing a static friction vector,
indicating angular velocity and tau the interaction moment.
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 may not be considered, so the model may be simplified as follows:
in order to realize the modeling of the dynamic characteristics of the training side limbs of the patient and the accessories of the rehabilitation robot, the interactive moment tau between the synchronous motion system and the rehabilitation robot body can be measured by a moment sensor in the parameter identification process and used as input in the parameter identification process, and the angular speed of the motion of the system
As system input, order
The simplified model can be linearized according to defined input and output and rewritten into a state space equation
In the form of (a), it is possible to obtain:
at the same time, the output equation is also written in terms of the canonical form of the state space equation, y ═ cx + v
Where w and v represent the system noise and the measurement noise vector, respectively, and the covariance matrix of the two can be represented by the formula Q ═ cov (w) ═ E { wwTAnd R ═ cov (v) ═ E { vv }TGet, respectively, where E { } represents the desired calculation.
It should be understood that the goal of parameter identification is to determine the identification results of the three items 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 identification unit 111 is configured to perform online identification on a sum of a gravity vector and a static friction vector in the dynamic parameter, and the 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 thetakSuch that the interaction torque measured at each of said specific angles is entirely generated by said gravity vector and said static friction vector, i.e. (D)k+Gk)＝τk。
Then, the sum (D) of the gravity vector and the static friction vector is fitted according to the angle values of the specific anglesk+Gk) And angle thetakThe online identification result of the sum of the gravity vector and the static friction vector can be obtained through the relational expression between the gravity vector and the static friction vector.
The second online identification unit 112 is configured to perform online identification on the viscous friction vector and the inertia moment according to an online identification result of a sum of the gravity vector and the static friction vector, and the foregoing steps S121 to S128 are referred to in the implementation process.
According to the embodiment, kinetic parameters are identified step by step according to characteristics of the kinetic parameters, an extended Kalman filter is introduced when the rotational inertia and viscous friction vectors are identified, the rotational inertia obtained through a self-adaptive estimation algorithm is used as a parameter of an extended Kalman filter load torque identification algorithm to participate in system parameter evaluation, so that decoupling of driving torque to the rotational inertia is achieved, the problem that the online identification precision of system parameters is reduced after the rotational inertia of a kinetic system is changed in a traditional method can be solved, and the requirements of multi-joint rehabilitation training of a constant velocity muscle force rehabilitation platform are completely met.
Example 5
The present embodiment provides an electronic device, which may be represented in the form of a computing device (for example, may be a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, may implement the rehabilitation robot control method based on kinetic parameter identification provided in embodiment 1 or 2.
Fig. 7 shows a schematic diagram of a 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 various 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 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., a keyboard, a pointing device, etc.). Such communication may be through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 96. The network adapter 96 communicates with the other modules of the electronic device 9 via the bus 93. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction 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, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module, according to embodiments of the application. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the steps of the rehabilitation robot control method based on kinetic parameter identification provided in embodiment 1 or 2.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a 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 implementation manner, the present invention can also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps of implementing the rehabilitation robot control method based on kinetic parameter identification described in embodiment 1 or 2, when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a 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 that 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 spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (9)
1. A rehabilitation robot control method based on kinetic parameter identification, the rehabilitation robot comprises a rehabilitation robot body and a rehabilitation robot accessory used 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 are fixed together to form a synchronous motion system, and the control method is characterized by comprising the following steps:
when a kinetic parameter identification instruction is received, identifying kinetic parameters of the synchronous motion system to obtain a kinetic parameter identification result;
acquiring the movement intention of the patient according to the measured interaction torque between the synchronous movement system and the rehabilitation robot body and the kinetic parameter identification result;
acquiring driving torque required to be output by the rehabilitation robot body according to the movement intention of the patient;
and generating a corresponding control command according to the driving moment, and sending the control command to the rehabilitation robot body.
2. The rehabilitation robot control method based on kinetic parameter identification as claimed in claim 1, wherein the kinetic parameters include: the sum of the gravity vector and the static friction vector, the viscous friction vector, and the moment of inertia.
3. The rehabilitation robot control method based on kinetic parameter identification as claimed in claim 1, wherein said identifying kinetic parameters of the synchronous motion system comprises:
performing online identification on the sum of the gravity vector and the static friction vector;
and according to the online identification result of the sum of the gravity vector and the static friction vector, performing online identification on the viscous friction vector and the rotational inertia.
4. The rehabilitation robot control method based on kinetic parameter identification as claimed in claim 1, wherein said identifying kinetic parameters of the synchronous motion system comprises:
controlling the rehabilitation robot body to drive the synchronous motion system to rotate to a plurality of specific angles, so that the interaction torque measured at each specific angle is equal to the sum of the gravity vector and the static friction vector;
and fitting a relational expression between the sum of the gravity vector and the static friction vector and the angle according to the angle values of the specific angles, thereby obtaining an online identification result of the sum of the gravity vector and the static friction vector.
5. The rehabilitation robot control method based on kinetic parameter identification as claimed in claim 3, wherein 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 comprises:
establishing a system state equation shown as the formula (1):
wherein, ω iskRepresenting the angle of rotation, τ, of the synchronous moving system at the time kkRepresenting the interaction moment between the synchronous motion system and the rehabilitation robot body at the kth moment, k representing the kth sampling moment, fkRepresenting the viscous friction vector, T representing the sampling period, MkRepresents the moment of inertia at time k, (D)k+Gk) Representing the identification result of the sum of the gravity vector and the static friction vector;
measuring the angular velocity omega of the rotation of the synchronous motion system at the kth momentk；
Measuring the interaction torque tau between the synchronous motion system and the rehabilitation robot body at the kth momentk；
For the measured angular velocity ωkAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
Obtaining the identification result of the moment of inertia at the k-th moment according to the following formulas (2) to (4)
Δωk＝ωk-ωk-1 (3)
Wherein, Δ τk＝(τk-τk-1) Beta is preset;
unbiased estimation quantity of the obtained interaction torque
Unbiased estimate of angular velocity
Identification result of moment of inertia
And the result (D) of the recognition of the sum of the gravity vector and the static friction vectork+Gk) Substituting formula (1) to obtain the identification result of the viscous friction vector at the k-th moment
Judging the identification result of the moment of inertia at the k moment
The result of identification of the viscous friction vector
If the predetermined condition is met, ending the process when the predetermined condition is met, and if the predetermined condition is not met, enabling k to be k +1 and returning to execute the measurement of the angular speed omega of the rotation of the synchronous motion system at the k momentk。
6. The rehabilitation robot control method based on kinetic parameter identification as claimed in claim 5, characterized in that said angular velocities obtained by said pair of measurementsDegree omegakAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
The method comprises the following steps:
using an extended Kalman filter to measure the angular velocity omegakAnd interaction moment taukFiltering to obtain corresponding angular velocity unbiased estimation quantity
Unbiased estimation of sum interaction torque
7. The utility model provides a recovered robot control system based on kinetic parameter discerns, recovered robot includes recovered robot body and is used for driving the training side limb of patient around recovered robot body's output shaft pivoted recovered robot accessory, the training side limb with recovered 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 kinetic parameters of the synchronous motion system when receiving a kinetic parameter identification instruction to obtain a kinetic parameter identification result;
the exercise intention acquisition module is used for acquiring the exercise intention of the patient according to the measured interaction torque between the synchronous motion system and the rehabilitation robot body and the kinetic parameter identification result;
the driving moment acquisition module is used for acquiring the 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 command according to the driving moment and sending the control command to the rehabilitation robot body.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 6 are implemented when the computer program is executed by the processor.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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