CN114888803A - Mechanical arm dynamic parameter identification method based on iterative optimization - Google Patents
Mechanical arm dynamic parameter identification method based on iterative optimization Download PDFInfo
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Abstract
The invention discloses a mechanical arm dynamics parameter identification method based on iterative optimization, and relates to the technical field of intelligent numerical control; the method comprises the steps of establishing a friction force model of a mechanical arm joint according to friction torque in a dynamic model of the multi-degree-of-freedom serial mechanical arm, iteratively identifying basic dynamic parameters by using a weighted least square method aiming at a linearized dynamic model in the multi-degree-of-freedom serial mechanical arm dynamic model, iteratively obtaining Stribeck nonlinear friction force parameters and nonlinear viscous friction force parameters in the friction force model respectively by using an ellipsoid method according to friction force obtained from the basic dynamic parameters, and constraining the Stribeck nonlinear friction force parameters and the nonlinear viscous friction force parameters until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction force parameters and the nonlinear viscous friction force parameters are used as identified mechanical arm dynamic parameters.
Description
Technical Field
The invention discloses a method, relates to the technical field of intelligent numerical control, and particularly relates to a mechanical arm dynamics parameter identification method based on iterative optimization.
Background
The traditional industrial robot lacks flexibility and is huge in size and is not popular with small and medium-sized enterprises, and on the other hand, the cooperative robot can work in a place close to human beings, is usually smaller in size and is more popular at present. Compared with the traditional industrial robot, the cooperative robot not only shows superiority in direct teaching and interaction with the environment, but also better guarantees the safety of cooperation with human beings. Whereas cooperative robots perform tasks highly dependent on accurate dynamic models (including friction models).
However, by only roughly estimating nominal parameters from Computer Aided Design (CAD) software, the collaborative robot dynamic model is usually unknown or only partially known, and due to production tolerances, dynamic model accuracy is even less guaranteed. Therefore, the mechanical arm of the cooperative robot needs to be subjected to dynamic model parameter identification so as to accurately control the cooperative robot, but at present, only least square estimation and an inverse dynamic model in a linear relation with dynamic parameters are studied, and the power problem generated by a friction effect is not considered, so that the current identification parameters are not accurate enough and need to be improved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a mechanical arm dynamics parameter identification method based on iterative optimization, which can quickly and effectively identify the mechanical arm dynamics parameters and provide parameter information basis for accurately controlling the mechanical arm.
The specific scheme provided by the invention is as follows:
the invention provides a mechanical arm dynamic parameter identification method based on iterative optimization, which establishes a frictional force model of a mechanical arm joint according to frictional torque in a dynamic model of a multi-degree-of-freedom serial mechanical arm,
aiming at a linear dynamic model in a multi-degree-of-freedom series-connection type mechanical arm dynamic model, basic dynamic parameters are iteratively identified by using a weighted least square method,
respectively iterating and obtaining a Stribeck nonlinear friction force parameter and a nonlinear viscous friction force parameter in a friction force model by utilizing an ellipsoid method according to the friction force obtained from the basic kinetic parameters, and constraining the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter until the parameters are stable, wherein the basic kinetic parameters corresponding to the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter are used as the identified mechanical arm kinetic parameters.
Further, the dynamics model of the multi-degree-of-freedom tandem type robot arm in the method for identifying the dynamic parameters of the robot arm based on the iterative optimization of claim 1 is as follows:
wherein M (q) e R n×n And n is the inertia matrix and the joint number;
and G (q) ε R n×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q is the sum of the values of q,
is a vector of n x 1 joint angular displacement, angular velocity and angular acceleration; tau epsilon to R n×1 And τ f ∈R n×1 Respectively the driving torque and the friction torque of the joint in the traditional dynamic model.
Further, in the method for identifying kinetic parameters of a mechanical arm based on iterative optimization according to claim 1, a friction model of a mechanical arm joint is established according to friction torque, as follows:
wherein, delta si As a non-linear parameter, α si Exponential parameter, alpha, being the Stribeck nonlinearity vi Is an exponential parameter of the non-linearity of the viscous friction forming, K ci ,K si ,K vi Indicating the friction of the joint.
Further, the iterative identification method of basic kinetic parameters by using a weighted least square method in the mechanical arm kinetic parameter identification method based on iterative optimization as claimed in claim 1, includes:
collecting M data points, identifying a basic kinetic parameter Πs by a standard linear square method,
further identifying and obtaining a basic kinetic parameter pi through a weighted least square method wls ，
The basic kinetic parameter Π is identified by an iterative weighted least squares method (IRLS).
Further, the method for identifying the kinetic parameters of the mechanical arm based on the iterative optimization as claimed in claim 1, wherein the friction force F is obtained from the basic kinetic parameters est The Stribeck nonlinear friction parameter comprises a nonlinear parameter delta si And the exponential parameter alpha of the Stribeck nonlinearity si Iteratively obtaining a nonlinear parameter delta by an ellipsoid method si The formula is as follows:
further, the method for identifying the kinetic parameters of the mechanical arm based on the iterative optimization as claimed in claim 1, wherein the friction force F is obtained from the basic kinetic parameters est Obtaining the index parameter alpha of the nonlinear viscous friction by iteration of an ellipsoid method vi The formula is as follows:
the invention also provides a mechanical arm dynamics parameter identification system based on iterative optimization, which comprises a parameter identification analysis module,
the parameter identification and analysis module establishes a friction force model of the mechanical arm joint according to the friction torque in the dynamic model of the multi-degree-of-freedom serial mechanical arm,
aiming at a linear dynamic model in a multi-degree-of-freedom series-connection type mechanical arm dynamic model, basic dynamic parameters are iteratively identified by using a weighted least square method,
respectively iterating and obtaining a Stribeck nonlinear friction force parameter and a nonlinear viscous friction force parameter in a friction force model by utilizing an ellipsoid method according to the friction force obtained from the basic kinetic parameters, and constraining the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter until the parameters are stable, wherein the basic kinetic parameters corresponding to the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter are used as the identified mechanical arm kinetic parameters.
The invention also provides a mechanical arm dynamic parameter identification device based on iterative optimization, which comprises: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute the mechanical arm dynamics parameter identification method based on iterative optimization.
The invention also provides a computer readable medium, which stores computer instructions, and when the computer instructions are executed by a processor, the processor executes the method for identifying the mechanical arm dynamics parameters based on the iterative optimization.
The invention has the advantages that:
the invention provides a mechanical arm dynamic parameter identification method based on iterative optimization, which can relatively quickly obtain an accurate dynamic model, can firstly identify the linear component of the dynamic parameter by utilizing a three-ring iterative format in the inner ring execution process, then respectively considers the nonlinear friction parameter and the nonlinear viscous friction parameter of the Stribeck effect of the friction model in the middle ring and the outer ring, and further determines the dynamic parameter corresponding to the nonlinear friction parameter and the nonlinear viscous friction parameter, thereby greatly improving the parameter identification efficiency.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a schematic diagram of the process of the present invention.
Fig. 3-8 are fitting comparison diagrams of friction force velocity models of six-degree-of-freedom series-type mechanical arm joints 1-6, respectively.
Fig. 9-14 are graphs comparing actual moment and fitting moment of six-degree-of-freedom series-connected type mechanical arm joints 1-6, respectively.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
The invention provides a mechanical arm dynamic parameter identification method based on iterative optimization, which establishes a frictional force model of a mechanical arm joint according to frictional torque in a dynamic model of a multi-degree-of-freedom serial mechanical arm,
aiming at a linear dynamic model in a multi-degree-of-freedom series-connection type mechanical arm dynamic model, basic dynamic parameters are iteratively identified by using a weighted least square method,
respectively iterating and obtaining a Stribeck nonlinear friction force parameter and a nonlinear viscous friction force parameter in a friction force model by utilizing an ellipsoid method according to the friction force obtained from the basic kinetic parameters, and constraining the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter until the parameters are stable, wherein the basic kinetic parameters corresponding to the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter are used as the identified mechanical arm kinetic parameters.
In particular applications, in some embodiments of the method of the invention, the steps are as follows:
s1, establishing a friction force model of the mechanical arm joint according to the friction torque in the dynamic model of the multi-degree-of-freedom serial mechanical arm, wherein the dynamic model of the multi-degree-of-freedom serial mechanical arm is as follows:
wherein，M(q)∈R n×n And n is the inertia matrix and the joint number;
and G (q) ε R n×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q is the sum of the values of q,
is a vector of n x 1 joint angular displacement, angular velocity and angular acceleration; tau epsilon to R n×1 And τ f ∈R n×1 Respectively the driving torque and the friction torque of the joint in the traditional dynamic model,
for the ith (i ═ 1,2, …, n) joint, a friction force model of the robot arm joint is built from the friction torque as follows:
wherein, delta si As a non-linear parameter, α si Exponential parameter, alpha, being the Stribeck nonlinearity vi Is an exponential parameter of the non-linearity of the viscous friction forming, K ci ,K si ,K vi Indicating the friction of the joint.
Aiming at a linear dynamic model in a multi-degree-of-freedom series-connection type mechanical arm dynamic model, the method comprises the following steps:
wherein Y ∈ R n×(b+3n) Is a regression matrix, n ∈ R b+3n Is a kinetic parameter, Y b Is a regression matrix of the basic kinetic part, Y f Regression matrix being part of friction force,. pi b Is a basic kinetic parameter,. pi f Is a parameter of the friction force which is,
s2 iteratively identifying basic kinetic parameters using a weighted least squares method, including:
collecting M data points, and identifying by standard linear square methodBasic kinetic parameter Π ls ，
Further identifying and obtaining a basic kinetic parameter pi through a weighted least square method wls ，
The basic kinetic parameter Π is identified by an iterative weighted least squares method (IRLS).
Further, when m data points are sampled, the identification process of the basic kinetic parameters is as follows:
T＝Y m ·Π (4)
wherein,
II is the basic dynamic parameter, T is the measuring moment, Y m Regression matrix of dynamics.
Further, in step S2, the inner loop of the linear least squares estimation is as follows:
identification of basic kinetic parameters by the standard Linear Square (LS) method, which can be expressed as
The recognition performance is improved by a Weighted Least Squares (WLS) technique, formulated as
R＝T-Y m ·Π Ls (6)
Wherein R ∈ R mn Is the residual, E ∈ R n×m Is a deformation matrix form of R, then
Wherein
Variance matrix representing residual error, E j Is line j of E. To meet the dimension requirement, we get the block diagonal matrix sigma m ∈R mn×mn Which has m identical diagonal blocks sigma,
in the WLS method, it is assumed that the noise in the measured torque of the different joints is independent. In the present invention it is assumed that the noise is correlated, so the off-diagonal covariance matrix Ω ∈ R can be calculated n×n The following were used:
similarly, Ω m ∈R nm×nm Also a block diagonal matrix. Furthermore, to improve the accuracy of the identification, the basic kinetic parameters are iteratively identified using an iterative weighted least squares method (IRLS). For each step in the internal cycle, reference is made to table 1 below,
TABLE 1
S3 respectively obtaining a Stribeck nonlinear friction force parameter and a nonlinear viscous friction force parameter in the friction force model by iteration through an ellipsoid method according to the friction force obtained from the basic kinetic parameters, and constraining the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter until the parameters are stable, wherein the basic kinetic parameters corresponding to the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter are used as the identified mechanical arm kinetic parameters.
Further, the friction force F obtained in S3 from the basic kinetic parameters est The estimated friction approximates the difference between the measured torque and the identified torque, such as:
the Stribeck nonlinear friction force parameter comprises a nonlinear parameter delta si And the exponential parameter alpha of the Stribeck nonlinearity si Iteratively obtaining a nonlinear parameter delta by an ellipsoid method si The formula is as follows:
the parameters are constrained to be greater than zero, and the inner loop needs to be restarted in each iteration of the intermediate loop,
further, the frictional force F obtained from the basic kinetic parameters in S3 est The estimated friction is approximated as the difference between the measured torque and the identified torque, as shown in equation (11),
in a similar way, the exponential parameter alpha of the nonlinear viscous friction is obtained by an ellipsoid method iteration vi The formula is as follows:
each iteration a vi The internal loop and the middle loop are both re-run.
Until the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter in the friction force model tend to be stable, calculating to obtain an accurate dynamic parameter pi.
According to the implementation process, taking a 6-DOF tandem type mechanical arm dynamic model as an example, the dynamic model is as follows:
wherein,M(q)∈R 6×6 and n is the inertia matrix and the number of joints respectively;
and G (q) ε R 6×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q is the sum of the values of q,
is a vector of 6 x 1 joint angular displacement, angular velocity and angular acceleration; tau epsilon to R 6×1 And τ f ∈R 6×1 Respectively the driving torque and the friction torque of the joint in the traditional dynamic model.
The friction model used for the i (i ═ 1,2, …,6) th joint is as follows:
wherein, delta si Is the Stribeck velocity, alpha si Is an exponential parameter of the Stribeck nonlinearity. Alpha is alpha vi Is an exponential parameter that creates the nonlinearity of viscous friction.
The linearized dynamics model is as follows, the linearization process reduces the original 60 parameters of the basic dynamics to 36:
wherein Y ∈ R 6×(36+3*6) Is a regression matrix, n ∈ R 36+3*6 Is a kinetic parameter, Y b Is a regression matrix of the basic kinetic part, Y f Regression matrix being part of friction force,. pi b Is the basic kinetic parameter (36), Π f Is the friction parameter (18).
S2, when m data points are sampled, acquiring data of 1500 data points at an acquisition frequency fs of 1kHz, that is, m is 1500, and identifying parameters as follows:
T＝Y m ·Π (4)
wherein,
pi is a kinetic parameter of the water-soluble film,
the inner loop of the linear least squares estimate is as follows:
a simple way to identify the basic parameters is by the standard Linear Square (LS) method, which can be expressed as
The recognition performance is improved by a Weighted Least Squares (WLS) technique, formulated as
R＝T-Y m ·Π LS (6)
Wherein R ∈ R mn Is the residual, E ∈ R n×m Is a deformation matrix form of R, then
Wherein
Variance matrix representing residual error, E j Is line j of E. To meet the dimension requirement, we get the block diagonal matrix sigma m ∈R mn×mn It has m identical diagonal blocks sigma.
In the WLS method, an off-diagonal covariance matrix omega epsilon R is calculated n×n The following were used:
similarly, Ω m ∈R nm×nm Also a block diagonal matrix. In addition, in order to improve the accuracy of the identification model, an iterative weighted least squares (IRLS) method is introduced to iteratively identify the base parameters. Each step in the inner loop is referred to table 1,
s3, middle loop for Stribeck effect estimation:
based on the linear part of the basic parameters that have been determined, the friction model is considered. The estimated friction is approximated as the difference between the measured torque and the identified torque, e.g.
Use of Stribeck nonlinearity parameter determination
For the non-linear parameter δ in the friction model of equation (2) si Here, the ellipsoid method is used for estimation:
n is 6, all these parameters are constrained to be greater than zero during the optimization, and the inner loop needs to be restarted in each iteration of the intermediate loop,
outer ring for non-linear viscous friction estimation:
estimation of the exponential parameter α of the non-linear viscous friction in the outer ring using a similar approach vi As shown in (12), and the remaining constraints are also similar to (11), the formula is as follows:
it is obvious that each timeIteration alpha vi Updated in the outer loop, the other two loops in the inner run again,
until the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter in the friction force model tend to be stable, calculating to obtain an accurate dynamic parameter pi.
Nonlinear parameters in the friction force model and 54 parameters in the linearized dynamic model are determined through experiments, and in order to verify the effectiveness of the friction force model, a fitting graph of the friction force-speed model is provided. As shown in fig. 3-8, since the torque is a multiple of the current, the torque is replaced by the current, and the unit of the side is mA, and the unit of the lower side is m/s. The curve in the figure represents the current of the actual friction and the nearly middle broken line curve represents the friction current fitted using the friction model of the present invention. Parameter alpha in joint-identifying friction model s1 ＝1.1271，α v1 0.5009; parameter alpha in joint two-identification friction model s2 ＝1.0023，α v2 0.4076; parameter alpha in joint three-identification friction model s3 ＝0.9879，α v3 0.6077; parameter alpha in four-identification friction model of joint s4 ＝1.2032，α v4 0.5980; parameter alpha in five-joint identification friction model s5 ＝1.3142，α v5 0.5899; parameter alpha in six-identification friction model of joint s6 ＝1.2587，α v6 ＝0.4399。
In order to verify the validity of the identification method, the invention provides a fitting graph comparing the measured current with the identified current predicted value. Fig. 9-14 show the results of the verification of the joints 1-6. The dashed line represents the measured current; the solid line represents the current predicted by model identification; the middle solid line indicates the error. The unit of the side surface is mA, and the graph shows that the current identified and predicted by the method highly conforms to the trend of the actually measured current, which shows that the method greatly improves the identification efficiency and precision of the parameters.
The invention also provides a mechanical arm dynamics parameter identification system based on iterative optimization, which comprises a parameter identification analysis module,
the parameter identification and analysis module establishes a friction force model of the mechanical arm joint according to the friction torque in the dynamic model of the multi-degree-of-freedom serial mechanical arm,
aiming at a linear dynamic model in a multi-degree-of-freedom series-connection type mechanical arm dynamic model, basic dynamic parameters are iteratively identified by using a weighted least square method,
respectively iterating and obtaining a Stribeck nonlinear friction force parameter and a nonlinear viscous friction force parameter in a friction force model by utilizing an ellipsoid method according to the friction force obtained from the basic kinetic parameters, and constraining the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter until the parameters are stable, wherein the basic kinetic parameters corresponding to the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter are used as the identified mechanical arm kinetic parameters.
The information interaction, execution process and other contents between the modules in the system are based on the same concept as the method embodiment of the present invention, and specific contents can be referred to the description in the method embodiment of the present invention, and are not described herein again.
Similarly, the system can relatively quickly obtain an accurate dynamic model, and can utilize a three-ring iteration format to firstly identify the linear component of the dynamic parameter in the execution process of the inner ring, then respectively consider the nonlinear friction parameter and the nonlinear viscous friction parameter of the Stribeck effect of the friction model in the middle ring and the outer ring, and further determine the dynamic parameter corresponding to the nonlinear friction parameter and the nonlinear viscous friction parameter, thereby greatly improving the identification efficiency of the parameter.
It should be noted that not all steps and modules in the above flows and system structures are necessary, and some steps or modules may be omitted according to actual needs. The execution order of the steps is not fixed and can be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by a plurality of physical entities, or some components in a plurality of independent devices may be implemented together.
The invention also provides a mechanical arm dynamic parameter identification device based on iterative optimization, which comprises: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute the mechanical arm dynamics parameter identification method based on iterative optimization.
The information interaction, execution process and other contents of the processor in the device are based on the same concept as the method embodiment of the present invention, and specific contents can be referred to the description in the method embodiment of the present invention, and are not described herein again.
Similarly, the device can relatively quickly obtain an accurate dynamic model, can utilize a three-ring iteration format, firstly identify the linear component of the dynamic parameter in the execution process of the inner ring, then respectively consider the nonlinear friction parameter and the nonlinear viscous friction parameter of the Stribeck effect of the friction model in the middle ring and the outer ring, and further determine the dynamic parameter corresponding to the nonlinear friction parameter and the nonlinear viscous friction parameter, thereby greatly improving the identification efficiency of the parameter.
The invention also provides a computer readable medium, which stores computer instructions, and when the computer instructions are executed by a processor, the processor executes the method for identifying the mechanical arm dynamics parameters based on the iterative optimization. Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion unit connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion unit to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (9)
1. A mechanical arm dynamic parameter identification method based on iterative optimization is characterized in that a friction force model of a mechanical arm joint is established according to friction torque in a dynamic model of a multi-degree-of-freedom serial type mechanical arm,
aiming at a linear dynamic model in a multi-degree-of-freedom series-connection type mechanical arm dynamic model, basic dynamic parameters are iteratively identified by using a weighted least square method,
respectively iterating and obtaining a Stribeck nonlinear friction force parameter and a nonlinear viscous friction force parameter in a friction force model by utilizing an ellipsoid method according to the friction force obtained from the basic kinetic parameters, and constraining the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter until the parameters are stable, wherein the basic kinetic parameters corresponding to the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter are used as the identified mechanical arm kinetic parameters.
2. The method for identifying the kinetic parameters of the mechanical arm based on the iterative optimization as claimed in claim 1, wherein the kinetic model of the multi-degree-of-freedom tandem type mechanical arm is as follows:
wherein M (q) e R n×n And n is the inertia matrix and the joint number;
and G (q) ε R n×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q is the sum of the values of q,
is a vector of n x 1 joint angular displacement, angular velocity and angular acceleration; tau epsilon to R n×1 And τ f ∈R n×1 Respectively the driving torque and the friction torque of the joint in the traditional dynamic model.
3. The method for identifying the kinetic parameters of the mechanical arm based on the iterative optimization as claimed in claim 2, wherein the friction force model of the mechanical arm joint is established according to the friction torque as follows:
wherein, delta si As a non-linear parameter, α si Exponential parameter, alpha, being the Stribeck nonlinearity vi Is an exponential parameter of the non-linearity of the viscous friction forming, K ci ,K si ,K vi Indicating the friction of the joint.
4. The mechanical arm dynamics parameter identification method based on the iterative optimization as claimed in any one of claims 1-3, wherein the iterative identification of the basic dynamics parameters by using the weighted least square method comprises:
collecting M data points, identifying a basic kinetic parameter II 1s by a standard linear square method,
further identifying and obtaining a basic kinetic parameter pi through a weighted least square method wls ，
The basic kinetic parameter Π is identified by an iterative weighted least squares method (IRLS).
5. The method as claimed in claim 3, wherein the method comprises obtaining the friction F from the basic kinetic parameters est Iteratively obtaining a nonlinear parameter delta by an ellipsoid method si The formula is as follows:
s.t.K si -K ci ≥0
δ si ，K ci ，K si ，K vi ≥0
2≥α si ≥0.5
i＝1，2，3，...，n。
6. the method as claimed in claim 3, wherein the method comprises obtaining the friction F from the basic kinetic parameters est Obtaining the index parameter alpha of the nonlinear viscous friction by iteration of an ellipsoid method vi The formula is as follows:
s.t.K si -K ci ≥0
α vi ，K ci ，K si ，K vi ≥0
i＝1，2，3，...，n。
7. a mechanical arm dynamics parameter identification system based on iterative optimization is characterized by comprising a parameter identification analysis module,
the parameter identification and analysis module establishes a friction force model of the mechanical arm joint according to the friction torque in the dynamic model of the multi-degree-of-freedom serial mechanical arm,
aiming at a linear dynamic model in a multi-degree-of-freedom series-connection type mechanical arm dynamic model, basic dynamic parameters are iteratively identified by using a weighted least square method,
respectively and iteratively obtaining a Stribeck nonlinear friction force parameter and a nonlinear viscous friction force parameter in a friction force model by utilizing an ellipsoid method according to the friction force obtained from the basic kinetic parameters, and constraining the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter until the parameters are stable, wherein the basic kinetic parameters corresponding to the Stribeck nonlinear friction force parameter and the nonlinear viscous friction force parameter are used as identified mechanical arm kinetic parameters.
8. A mechanical arm dynamic parameter identification device based on iterative optimization is characterized by comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program to perform the method of identifying mechanical arm dynamics parameters based on iterative optimization of any one of claims 1 to 6.
9. A computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform a method for identifying a mechanical arm dynamics parameter based on iterative optimization as claimed in any one of claims 1 to 6.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106426174A (en) * | 2016-11-05 | 2017-02-22 | 上海大学 | Robot contact force detecting method based on torque observation and friction identification |
| CN107918276A (en) * | 2017-11-13 | 2018-04-17 | 东南大学 | A kind of secondary Precise modeling of Electromechanical Actuators friction |
| US20180202900A1 (en) * | 2017-01-19 | 2018-07-19 | I-Shou University | Method and apparatus for estimating parameters of a dc machine by least-squares method |
| CN108297093A (en) * | 2017-12-29 | 2018-07-20 | 中国海洋大学 | A kind of step identification method of Manipulator Dynamics parameter |
| CN108381529A (en) * | 2018-05-28 | 2018-08-10 | 上海优尼斯工业服务有限公司 | A kind of man-machine collaboration teaching method of industrial machinery arm |
| CN109483591A (en) * | 2018-10-23 | 2019-03-19 | 华南理工大学 | Joint of robot frictional force discrimination method based on LuGre friction model |
| CN109940609A (en) * | 2019-02-22 | 2019-06-28 | 浙江工业大学 | A kind of Dynamic Modeling in Robotics method based on central symmetry static friction model |
| CN110065073A (en) * | 2019-05-28 | 2019-07-30 | 上海交通大学 | A kind of Dynamic Models of Robot Manipulators discrimination method |
| US20200055184A1 (en) * | 2018-08-16 | 2020-02-20 | Hehua Ju | Axis-Invariant based Multi-axis robot kinematics modeling method |
| 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 |
| CN112883504A (en) * | 2021-01-11 | 2021-06-01 | 常州工程职业技术学院 | Method for estimating tail end load dynamic parameters of industrial mechanical arm |
| CN112975987A (en) * | 2021-03-25 | 2021-06-18 | 江苏集萃复合材料装备研究所有限公司 | Orthopedic surgery robot control method based on dynamic model |
| CN113021331A (en) * | 2019-12-24 | 2021-06-25 | 沈阳智能机器人创新中心有限公司 | Seven-degree-of-freedom cooperative robot dynamics modeling and identification method |
| CN113283063A (en) * | 2021-05-10 | 2021-08-20 | 珠海格力电器股份有限公司 | Friction force identification method, module, equipment and computer readable medium |
| CN114186498A (en) * | 2021-12-20 | 2022-03-15 | 中国科学院长春光学精密机械与物理研究所 | Robot joint friction model parameter identification method based on improved wolf algorithm |
-
2022
- 2022-05-19 CN CN202210544014.4A patent/CN114888803B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106426174A (en) * | 2016-11-05 | 2017-02-22 | 上海大学 | Robot contact force detecting method based on torque observation and friction identification |
| US20180202900A1 (en) * | 2017-01-19 | 2018-07-19 | I-Shou University | Method and apparatus for estimating parameters of a dc machine by least-squares method |
| CN107918276A (en) * | 2017-11-13 | 2018-04-17 | 东南大学 | A kind of secondary Precise modeling of Electromechanical Actuators friction |
| CN108297093A (en) * | 2017-12-29 | 2018-07-20 | 中国海洋大学 | A kind of step identification method of Manipulator Dynamics parameter |
| CN108381529A (en) * | 2018-05-28 | 2018-08-10 | 上海优尼斯工业服务有限公司 | A kind of man-machine collaboration teaching method of industrial machinery arm |
| US20200055184A1 (en) * | 2018-08-16 | 2020-02-20 | Hehua Ju | Axis-Invariant based Multi-axis robot kinematics modeling method |
| CN109483591A (en) * | 2018-10-23 | 2019-03-19 | 华南理工大学 | Joint of robot frictional force discrimination method based on LuGre friction model |
| 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 |
| CN109940609A (en) * | 2019-02-22 | 2019-06-28 | 浙江工业大学 | A kind of Dynamic Modeling in Robotics method based on central symmetry static friction model |
| CN110065073A (en) * | 2019-05-28 | 2019-07-30 | 上海交通大学 | A kind of Dynamic Models of Robot Manipulators discrimination method |
| CN113021331A (en) * | 2019-12-24 | 2021-06-25 | 沈阳智能机器人创新中心有限公司 | Seven-degree-of-freedom cooperative robot dynamics modeling and identification method |
| CN112883504A (en) * | 2021-01-11 | 2021-06-01 | 常州工程职业技术学院 | Method for estimating tail end load dynamic parameters of industrial mechanical arm |
| CN112975987A (en) * | 2021-03-25 | 2021-06-18 | 江苏集萃复合材料装备研究所有限公司 | Orthopedic surgery robot control method based on dynamic model |
| CN113283063A (en) * | 2021-05-10 | 2021-08-20 | 珠海格力电器股份有限公司 | Friction force identification method, module, equipment and computer readable medium |
| CN114186498A (en) * | 2021-12-20 | 2022-03-15 | 中国科学院长春光学精密机械与物理研究所 | Robot joint friction model parameter identification method based on improved wolf algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 傅霞君;袁建军;王帅;王宁;张伟军;: "基于粒子群算法的机器人动力学参数辨识", 机电一体化, no. 02, pages 3 - 8 * |
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