CN116175567B - Identification method for mechanical arm joint friction model parameters and kinetic parameters - Google Patents
Identification method for mechanical arm joint friction model parameters and kinetic parameters Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The application belongs to the technical field of robot dynamics, and particularly relates to a mechanical arm joint friction model parameter and a dynamic parameterThe identification method comprises the following steps: based on a pre-constructed mechanical arm friction model, acquiring experimental moment tau of each joint of the mechanical arm after movement in a first excitation mode n For experimental moment tau n Fitting and identifying to obtain friction models of all joints; constructing a mechanical arm dynamics equation without friction factors, then carrying out a second excitation mode to obtain joint moment, constructing an identification model with friction factors based on the obtained friction model and joint moment, carrying out matrix decomposition and fitting identification on the identification model to obtain mechanical arm dynamics parameters; the friction identification model in the application can be changed according to different types and different requirements of the mechanical arm, and can be used for independently identifying the identification flow without influencing the integral dynamic inertia parameters.
Description
Technical Field
The application belongs to the technical field of robot dynamics, and particularly relates to a method for identifying mechanical arm joint friction model parameters and kinetic parameters.
Background
The mechanical arm has the advantages that the performance requirement of people on the mechanical arm is improved to a new step in the application of the mechanical arm in the fields of life health and medical operation. Whether the mechanical structure of the mechanical arm or the algorithm of motion control, the advantages and disadvantages of the mechanical arm influence the final result of the man-machine interaction mode. The mechanical arm motion control effect is based on the establishment of a basic dynamic model, and the quality of the dynamic model depends on whether the dynamic inertia parameters are accurate or not. Meanwhile, friction generated by interaction among mechanical components in the mechanical arm joint has a considerable influence on a finally established dynamic model. In order to build a model capable of better expressing motion characteristics, inertial parameters of the mechanical arm and sometimes friction model parameters are required to be identified.
The existing dynamic parameter identification method considering the friction model takes a preset friction model into consideration when a motion equation is constructed, then the dynamic inertia parameter and the preset friction parameter are fitted through linearization treatment and finally through experiments. Under the method, whether the friction model is built or not influences the accuracy of the friction parameters, and the accuracy of the inertia parameters is influenced. The dynamic parameter identification process is relatively long, and the friction model cannot be improved and compensated in the whole identification process. Therefore, it is needed to provide a method for identifying the friction model of the mechanical arm joint with the advantages of "integral identification", "friction guiding", "friction model replacement", etc., so as to improve the accuracy of identifying the integral kinetic parameters.
Disclosure of Invention
The application aims to provide a method for identifying friction model parameters and kinetic parameters of a mechanical arm joint, which aims to solve the problem that the accuracy of friction parameter identification can be affected by the existing kinetic parameter identification method in the background technology.
The application realizes the above purpose through the following technical scheme:
a method for identifying mechanical arm joint friction model parameters and kinetic parameters comprises the following steps:
s1, acquiring experimental moment tau of each joint after movement of a mechanical arm in a first excitation mode based on a pre-constructed mechanical arm friction model n According to the experimental moment tau of each joint n The experimental moment tau of each joint is obtained by different partial data in the forward rotation and reverse rotation processes n Fitting and identifying to obtain a forward friction model and a reverse friction model of each joint;
s2, constructing a mechanical arm dynamics equation without friction factors, and sampling experimental data after the mechanical arm is excited in a second excitation mode to obtain forward moment tau of each joint p And a reverse torque tau m ；
S3, based on the forward friction model, the reverse friction model and the forward torque tau p And a reverse torque tau m And constructing an identification model with friction factors, carrying out matrix decomposition on the identification model, and identifying the dynamic parameters of the mechanical arm after fitting.
As a further optimization scheme of the application, the first excitation mode specifically comprises: establishing a first excitation function based on the gestures of each joint, and extracting the gestures of the joint to be recognized at different moments according to the first excitation function to form a gesture set of each joint after forward rotation and reverse rotation, wherein the first excitation function is expressed as a functional relation of each joint gesture established according to preset circulation times in a unidirectional speed excitation sampling time period.
As a further optimization scheme of the application, the process of extracting the gestures of the joint to be identified at different moments according to the first excitation function to form the gesture set of each joint after forward rotation and reverse rotation is specifically as follows: extracting the joint i to be identifiedAttitude θ at time i (t) forming a set of vectors as i joint forward rotation reference poses:
θ ip as i joint forward rotation reference pose;
extracting the joint i to be identifiedAttitude θ at time i (t) forming a set of vectors as i joint counter-rotation reference poses:
θ im as i joint counter-rotation reference pose;
in the above formula, i is a joint number, T is time, n is the circulation times of an excitation function, and T is a unidirectional speed excitation sampling time period;
obtaining two sets of gestures according to the reference gestures of each joint:
forward direction: θ p ＝(θ 1p ，θ 2p ...θ 7p ) T
Reversing: θ m ＝(θ 1m ，θ 2m ...θ 7m ) T 。
As a further optimization of the application, according to the experimental moment tau of each joint n The experimental moment tau of each joint is obtained by different partial data in the forward rotation and reverse rotation processes n The step of fitting and identifying to obtain the forward friction model and the reverse friction model of each joint comprises the steps of obtaining experimental moment tau of each joint through the first excitation mode n Removing a mass term, a gravity term and a centrifugal force term according to two sets of gestures generated in forward rotation and reverse rotation states of each joint to obtain a joint friction model F, and reading joint moment to identify parameters to be identified of the mechanical arm joint friction model, and obtaining the forward rotation friction model of each joint after identificationAnd counter-rotating friction model->
As a further optimization scheme of the application, in the step S2, the mechanical arm dynamics equation without friction factor is constructed as follows:
m ', C ', G ' in the above formula are respectively a mass term, a centrifugal force coriolis force term and a gravity term in the kinetic equation;representing the joint displacement of the mechanical arm->Represents the angular velocity of the mechanical arm joint->Indicating the angular acceleration of the joint of the mechanical arm; τ' g Representing joint moment without friction factor.
As a further optimization of the present application, in the step S3, the forward torque τ and the reverse friction model are based on the forward friction model and the reverse friction model p And the reverse torque tau m The process for constructing the identification model with friction factors comprises the following steps:
during forward rotation, the identification model is as follows:
at inversion, the recognition model is:
τ 'in the above' p Represents the joint forward moment with friction factor, tau' m Indicating the joint counter moment with friction factors.
As a further optimization scheme of the application, the pre-constructed mechanical arm friction model comprises the steps of establishing a robot dynamics equation without a first-order Tustin friction model based on a lagrangian method:
τ in i For the moment of the joint of the mechanical arm i,g (θ) is the mass term, centrifugal force Coriolis force term and gravity term in the kinetic equation, respectively, ++> Respectively, a coulomb friction term, a viscous friction term and a stribeck friction term, wherein theta represents the angle variable of each joint of the mechanical arm, and f c ，f v ，f s ，v s The parameters to be identified are the friction model.
As a further optimization scheme of the application, the mechanical arm is excited in a second excitation mode to obtain the forward moment tau of each joint p And a reverse torque tau m The method comprises the steps of obtaining the forward moment tau of each joint with friction factors in a second excitation mode based on a gesture function, a speed function and an acceleration function p And a reverse torque tau m 。
The application has the beneficial effects that:
1) The application provides a method for identifying parameters and dynamic parameters of a joint friction model of a mechanical arm, which greatly improves the accuracy of the identified dynamic inertia parameters and the friction model with the friction model.
2) The friction identification model in the application can be changed according to different types and different requirements of the mechanical arm, and can be used for independently identifying the identification flow without influencing the integral dynamic inertia parameters.
3) Compared with the prior art, the overall identification step of the application increases the steps of identifying friction first and then further determining the friction model, so that the parameter vector required to be identified is split, and the total calculated amount is greatly reduced.
4) Compared with the mode that when the joint friction of the mechanical arm is identified in the prior art, a single joint is free from load and idles, and the mechanical arm is detached, the integral identification method of the friction force does not need to detach the assembled mechanical arm, and compared with the prior art, the method has the advantages of coexistence of experimental significance and engineering application value.
Drawings
FIG. 1 is a schematic overall flow chart of a method for identifying kinetic parameters and friction parameters of a mechanical arm;
FIG. 2 is a schematic overall flow chart of a prior art method for identifying kinetic parameters including friction models;
FIG. 3 is a 2-joint fit image of a 7-degree-of-freedom robotic arm in an embodiment of the application;
fig. 4 is a 2-joint Tustin simulation image of a 7-degree-of-freedom mechanical arm in an embodiment of the application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings, wherein it is to be understood that the following detailed description is for the purpose of further illustrating the application only and is not to be construed as limiting the scope of the application, as various insubstantial modifications and adaptations of the application to those skilled in the art can be made in light of the foregoing disclosure.
Example 1
As shown in fig. 2, a process flow of a method for identifying kinetic parameters by a friction model in the prior art, as shown in fig. 1, the application provides a method for identifying kinetic parameters and parameters of a joint friction model of a mechanical arm, which is different from the method for identifying kinetic parameters by a friction model in the prior art, and is mainly characterized in that:
in the prior art, the problem of low accuracy exists in the prior art that the accuracy of the friction model is firstly established, and meanwhile, whether the friction model is established or not influences the accuracy of friction parameters, and the accuracy of inertia parameters is influenced, so that the dynamic parameter identification process is relatively long, and the friction model cannot be improved and compensated in the whole identification process. The application adopts integral identification for the identification of the friction force of the mechanical arm, and can replace the friction model at any time according to the identification precision.
The identification method comprises the following steps:
s1, acquiring experimental moment tau of each joint after movement of a mechanical arm in a first excitation mode based on a pre-constructed mechanical arm friction model n According to the experimental moment tau of each joint n The experimental moment tau of each joint is obtained by different partial data in the forward rotation and reverse rotation processes n Fitting and identifying to obtain a forward friction model and a reverse friction model of each joint;
s2, constructing a mechanical arm dynamics equation without friction factors, and performing a second stepThe experimental data after the mechanical arm is excited in the excitation mode are sampled to obtain the forward moment tau of each joint p And a reverse torque tau m ；
S3, based on the forward friction model, the reverse friction model and the forward torque tau p And a reverse torque tau m And constructing an identification model with friction factors, carrying out matrix decomposition on the identification model, and identifying the dynamic parameters of the mechanical arm after fitting.
The classical friction model comprises coulomb friction factors and viscous friction factors, and the friction and the rotation speed are generally regarded as linear relations when describing the friction characteristics of each joint of the mechanical arm by using the classical friction model, but the friction influence is much larger than that of a state (low speed) before each joint of the mechanical arm starts to move to a fixed numerical speed, so that a more complex friction model is adopted in the embodiment to express the friction influence of the low speed stage. Of course, models other than the embodiment can be selected according to actual conditions or time, and flexible selection of the friction model is also one of the advantages of the method.
The structure of the Stribeck friction form is more complex than that of a classical model form, but the relation between friction and joint rotation speed in a low-speed state is clearly shown, the Stribeck friction form is selected in the implementation process of the method, the first-order Tustin improved model is an expression form of Stribeck friction, the application range is wider, the accuracy is determined according to different application ranges, and experiments show that the model can be fit to real friction with the accuracy of almost 90%.
The mechanical arm friction model pre-constructed in the step S1 comprises the steps of establishing a robot dynamics equation without a first-order Tustin friction model based on a Lagrangian method:
τ in i For the moment of the joint of the mechanical arm i,g (θ) is the mass term, centrifugal force Coriolis force term and gravity term in the kinetic equation, respectively, ++> Respectively, a coulomb friction term, a viscous friction term and a stribeck friction term, wherein theta represents the angle variable of each joint of the mechanical arm, and f c ，f v ，f s ，v s The parameters to be identified are the friction model.
After the friction model to be identified is determined, an excitation experiment is needed to identify each parameter in the friction model; because the friction is related to the rotation speed of the mechanical arm with multiple degrees of freedom, the mechanical arm must be excited to move at different speeds, and because the embodiment adopts integral identification and adopts the mechanical arm with 7 degrees of freedom to carry out excitation experiments, once the mechanical arm moves, a moment other than the friction moment is necessarily generated, which is also a problem to be solved by the embodiment.
For excitation experiments, the first excitation pattern in step S1 of this embodiment is specifically: establishing a first excitation function based on the gestures of each joint, and extracting the gestures of the joint to be recognized at different moments according to the first excitation function to form a gesture set of each joint after forward rotation and reverse rotation, wherein the first excitation function is expressed as a functional relation of each joint gesture established according to a preset cycle number in a unidirectional speed excitation sampling time period, and the first excitation function is expressed as:
in the above, θ i (t) each joint posture, i is the joint number, t is the time, n is the number of excitation function cycles,is the rate of change of speed, is a constant, and T is the unidirectional speed excitation sampling time period.
In step S1 of this embodiment, the process of extracting the poses of the joints to be identified at different moments according to the first excitation function to form the pose set of each joint after forward rotation and reverse rotation specifically includes: extracting the joint i to be identifiedAttitude θ at time i (t) forming a set of vectors as i joint forward rotation reference poses:
θ ip as i joint forward rotation reference pose;
extracting the joint i to be identifiedAttitude θ at time i (t) forming a set of vectors as i joint counter-rotation reference poses:
θ im as i joint counter-rotation reference pose;
in the above formula, i is a joint number, T is time, n is the circulation times of an excitation function, and T is a unidirectional speed excitation sampling time period;
obtaining two sets of gestures according to the reference gestures of each joint:
forward direction: θ p ＝(θ 1p ，θ 2p ...θ 7p ) T
Reversing: θ m ＝(θ 1m ，θ 2m ...θ 7m ) T 。
In step S1 of the present embodiment, the experimental moment τ is determined according to the joint n The experimental moment tau of each joint is obtained by different partial data in the forward rotation and reverse rotation processes n The steps of fitting and identifying to obtain the forward friction model and the reverse friction model of each joint are specifically as follows:
acquiring experimental moment tau of each joint through the first excitation mode n Removing a mass term, a gravity term and a centrifugal force term according to two sets of gestures generated in forward rotation and reverse rotation states of each joint to obtain a joint friction model F, and reading joint moment to identify parameters to be identified of the mechanical arm joint friction model, and obtaining the forward rotation friction model of each joint after identificationAnd counter-rotating friction model->
As shown in fig. 3, the experimental moment τ of each joint of the arm is obtained in the step S1 n As shown in fig. 3, a fitted image of the joint 2 in this example, wherein "abscissa speed, unit (rad/s), ordinate moment, unit (N.m)"; as shown in fig. 4, in this embodiment, a Tustin first-order model is used to perform a fitting approximation process on a fitted image of the 2 joints, where "abscissa speed (rad/s), ordinate moment, unit (N.m)"; and identifying the friction model F based on the processed image of the experimental moment.
The fitting process adopts a moment sampling algorithm based on math tools.
Obtaining the forward rotation friction model of each jointAnd counter-rotating friction model->Step S2 is then carried out, and the constructed mechanical arm power without friction factors is constructedThe mathematical equation is as follows:
m ', C ', G ' in the above formula are respectively a mass term, a centrifugal force coriolis force term and a gravity term in the kinetic equation;representing the joint displacement of the mechanical arm->Represents the angular velocity of the mechanical arm joint->Indicating the angular acceleration of the joint of the mechanical arm; τ' g Representing joint moment without friction factor.
The mechanical arm is excited in a second excitation mode to obtain the forward moment tau of each joint p And a reverse torque tau m The method comprises the steps of obtaining the forward moment tau of each joint with friction factors in a second excitation mode based on a gesture function, a speed function and an acceleration function p And a reverse torque tau m 。
In step S3 of the present embodiment, the forward torque τ is calculated based on the obtained forward friction model and the reverse friction model p And the reverse torque tau m The process for constructing the identification model with friction factors comprises the following steps:
during forward rotation, the identification model is as follows:
at inversion, the recognition model is:
τ 'in the above' p Represents the joint forward moment with friction factor, tau' m Indicating the joint counter moment with friction factors.
Finding an observation matrix W of full rank using matrix decomposition g Vector P for generalizing and deducing identification parameters g ：
Joint forward rotation:
joint reversal:
using the positive moment tau obtained in the step S2 after the mechanical arm is excited p And the reverse torque tau m Fitting the kinetic parameters P of the mechanical arm by using a least square method g 。
The above examples illustrate only one embodiment of the application, which is described in more detail and is not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application.
Claims (3)
1. The method for identifying the friction model parameters and the kinetic parameters of the mechanical arm joint is characterized by comprising the following steps:
s1, acquiring experimental moment tau of each joint after movement of a mechanical arm in a first excitation mode based on a pre-constructed mechanical arm friction model n According to the experimental moment tau of each joint n The experimental moment tau of each joint is obtained by different partial data in the forward rotation and reverse rotation processes n Fitting and identifying to obtain a forward friction model and a reverse friction model of each joint;
s2, constructing a mechanical arm dynamics equation without friction factors, and exciting the mechanical arm in a second excitation modeSampling the experimental data after excitation to obtain the forward moment tau of each joint p And a reverse torque tau m ；
S3, based on the forward friction model, the reverse friction model and the forward torque tau p And a reverse torque tau m Building an identification model with friction factors, carrying out matrix decomposition on the identification model, and identifying the dynamic parameters of the mechanical arm after fitting;
the first excitation mode specifically comprises the following steps: establishing a first excitation function based on the gestures of each joint, and extracting the gestures of the joint to be identified at different moments according to the first excitation function to form a gesture set of each joint after forward rotation and reverse rotation, wherein the first excitation function is expressed as a function relation of each joint gesture established according to preset cycle times in a unidirectional speed excitation sampling time period;
wherein the first excitation function is expressed as:
in the above, θ i (t) each joint posture, i is the joint number, t is the time, n is the number of excitation function cycles,the speed change rate is constant, and T is the unidirectional speed excitation sampling time period;
the process of extracting the gestures of the joints to be identified at different moments according to the first excitation function to form gesture sets of the joints after forward rotation and reverse rotation specifically comprises the following steps: extracting the joint i to be identifiedAttitude θ at time i (t) forming a set of vectors as i joint forward rotation reference poses:
θ ip as i joint forward rotation reference pose;
extracting the joint i to be identifiedAttitude θ at time i (t) forming a set of vectors as i joint counter-rotation reference poses:
θ im as i joint counter-rotation reference pose;
in the above formula, i is a joint number, T is time, n is the circulation times of an excitation function, and T is a unidirectional speed excitation sampling time period;
obtaining two sets of gestures according to the reference gestures of each joint:
forward direction: θ p ＝(θ 1p ，θ 2p ...θ 7p ) T
Reversing: θ m ＝(θ 1m ，θ 2m ...θ 7m ) T ；
According to the two gesture sets and the experimental moment tau of each corresponding joint n The experimental moment tau of each joint is obtained by different partial data in the forward rotation and reverse rotation processes n The step of fitting and identifying to obtain the forward friction model and the reverse friction model of each joint comprises the steps of obtaining experimental moment tau of each joint through the first excitation mode n Removing a mass term, a gravity term and a centrifugal force term according to two sets of gestures generated in forward rotation and reverse rotation states of each joint to obtain a joint friction model F, and reading joint moment to identify parameters to be identified of the mechanical arm joint friction model, and obtaining the forward rotation friction model of each joint after identificationAnd counter-rotating friction model->
2. The method for identifying the friction model parameters and the kinetic parameters of the mechanical arm joint according to claim 1, wherein in the step S2, the mechanical arm kinetic equation without friction factors is constructed as follows:
m ', C ', G ' in the above formula are respectively a mass term, a centrifugal force coriolis force term and a gravity term in the kinetic equation;representing the joint displacement of the mechanical arm->Represents the angular velocity of the mechanical arm joint->Indicating the angular acceleration of the joint of the mechanical arm; τ' g Representing joint moment without friction factor.
3. The method for identifying the mechanical arm joint friction model parameters and the kinetic parameters according to claim 2, wherein in the step S3, the forward torque τ is based on the forward friction model, the reverse friction model and the forward torque τ p And the reverse torque tau m The process for constructing the identification model with friction factors comprises the following steps:
during forward rotation, the identification model is as follows:
at inversion, the recognition model is:
τ 'in the above' p Represents the joint forward moment with friction factor, tau' m Indicating the joint counter moment with friction factors.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4698572A (en) * | 1986-04-04 | 1987-10-06 | Westinghouse Electric Corp. | Kinematic parameter identification for robotic manipulators |
| CN103344243A (en) * | 2013-07-02 | 2013-10-09 | 北京航空航天大学 | Friction parameter identification method for aerial remote-sensing inertial stabilization platform |
| WO2020133270A1 (en) * | 2018-12-28 | 2020-07-02 | 深圳配天智能技术研究院有限公司 | Dynamic parameter identification method for robot, robot and storage device |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4698572A (en) * | 1986-04-04 | 1987-10-06 | Westinghouse Electric Corp. | Kinematic parameter identification for robotic manipulators |
| CN103344243A (en) * | 2013-07-02 | 2013-10-09 | 北京航空航天大学 | Friction parameter identification method for aerial remote-sensing inertial stabilization platform |
| WO2020133270A1 (en) * | 2018-12-28 | 2020-07-02 | 深圳配天智能技术研究院有限公司 | Dynamic parameter identification method for robot, robot and storage device |
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