CN114888803B - Mechanical arm dynamic parameter identification method based on iterative optimization - Google Patents
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Abstract
The invention discloses a mechanical arm dynamic parameter identification method based on iterative optimization, and relates to the technical field of intelligent numerical control; establishing a friction model of a mechanical arm joint according to friction moment in a dynamics model of the multi-degree-of-freedom serial mechanical arm, iteratively identifying basic dynamics parameters by using a weighted least square method aiming at a linearization dynamics model in the multi-degree-of-freedom serial mechanical arm dynamics model, respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in the friction model by using an ellipsoid method according to friction force obtained in the basic dynamics parameters, and restricting the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamics parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamics parameters.
Description
Technical Field
The invention discloses a method, relates to the technical field of intelligent numerical control, and in particular relates to a mechanical arm dynamic parameter identification method based on iterative optimization.
Background
The lack of flexibility and bulkiness of conventional industrial robots is not welcome by small and medium-sized enterprises, and on the other hand, collaborative robots can work close to humans, are generally smaller in size, and are therefore currently more popular. Compared with the traditional industrial robot, the cooperative robot not only has superiority in direct teaching and environment interaction, but also ensures the safety of cooperation with human beings better. While collaborative robots perform tasks highly dependent on accurate kinetic models (including friction models).
However, by having nominal parameters that can only be roughly estimated from Computer Aided Design (CAD) software, collaborative robot dynamic models are generally unknown or only partially known, and due to the existence of production tolerances, dynamic model accuracy is more not 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 only least square estimation and an inverse dynamic model which is in linear relation with dynamic parameters are studied at present, and the dynamic problem caused by friction effect is not considered, so that the current identification parameters are inaccurate and need to be improved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides the mechanical arm dynamic parameter identification method based on iterative optimization, which can be used for quickly and effectively identifying mechanical arm dynamic parameters and providing 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 friction model of a mechanical arm joint according to friction moment in a dynamic model of a multi-degree-of-freedom serial mechanical arm,
aiming at a linearization kinetic model in a multi-degree-of-freedom serial mechanical arm kinetic model, a weighted least square method is utilized to iteratively identify basic kinetic parameters,
and respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in a friction model by utilizing an ellipsoid method according to the friction force obtained in the basic dynamic parameters, and constraining the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamic parameters.
Further, in the mechanical arm kinetic parameter identification method based on iterative optimization, the kinetic model of the multi-degree-of-freedom serial mechanical arm is as follows:
wherein M (q) ∈R n×n And n is the inertial matrix and the number of joints;and G (q) ∈R n×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q, & gt>Is a vector of angular displacement, angular velocity and angular acceleration of the joint n x 1; τ ε R n×1 And τ f ∈R n×1 Respectively the driving moment and the friction moment of the joint in the traditional dynamics model.
Further, in the mechanical arm dynamic parameter identification method based on iterative optimization, a friction model of the mechanical arm joint is established according to friction moment, and the method comprises the following steps:
wherein delta si As non-linear parameter, alpha si Is an index parameter of Stribeck nonlinearity, alpha vi Is an exponential parameter that forms the nonlinearity of viscous friction, K ci ,K si ,K vi Indicating the friction of the joint.
Further, in the mechanical arm kinetic parameter identification method based on iterative optimization, the method for iteratively identifying the basic kinetic parameters by using a weighted least square method comprises the following steps:
m data points are collected, basic kinetic parameters II ls are identified through a standard linear square method,
further identifying by weighted least square method to obtain basic kinetic parameter pi wls ，
The basic kinetic parameters pi are identified by an iterative weighted least squares method (IRLS).
Further, a mechanical arm dynamic parameter identification method based on iterative optimization is provided, wherein the friction force F is obtained according to basic dynamic parameters est The Stribeck nonlinear friction parameters include a nonlinear parameter delta si And the index parameter alpha of the Stribeck nonlinearity si Iteration is carried out by adopting an ellipsoidal method to obtain nonlinear parameter delta si The formula is as follows:
further, a mechanical arm dynamic parameter identification method based on iterative optimization is provided, wherein the friction force F is obtained according to basic dynamic parameters est An ellipsoidal method is adopted for iteration to obtain an index parameter alpha of nonlinear viscous friction vi The formula is as follows:
the invention also provides a mechanical arm dynamic parameter identification system based on iterative optimization, which comprises a parameter identification analysis module,
the parameter identification and analysis module establishes a friction model of the mechanical arm joint according to the friction moment in the dynamics model of the multi-degree-of-freedom serial mechanical arm,
aiming at a linearization kinetic model in a multi-degree-of-freedom serial mechanical arm kinetic model, a weighted least square method is utilized to iteratively identify basic kinetic parameters,
and respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in a friction model by utilizing an ellipsoid method according to the friction force obtained in the basic dynamic parameters, and constraining the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamic parameters.
The invention also provides a mechanical arm dynamic parameter identification device based on iterative optimization, which comprises the following steps: at least one memory and at least one processor;
the at least one memory for storing a machine readable program;
the at least one processor is configured to invoke the machine-readable program and execute the mechanical arm dynamics parameter identification method based on iterative optimization.
The invention also provides a computer readable medium, wherein the computer readable medium is stored with computer instructions, and when the computer instructions are executed by a processor, the processor is caused to execute the mechanical arm dynamic parameter identification method based on 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, and can utilize a three-loop iterative format, in the inner loop execution process, firstly identify linear components of dynamic parameters, then respectively consider nonlinear friction parameters and nonlinear viscous friction parameters of the Stribeck effect of a friction model in a middle loop and an outer loop, and further determine the dynamic parameters corresponding to the nonlinear friction parameters and the nonlinear viscous friction parameters, thereby greatly improving the identification efficiency of the parameters.
Drawings
FIG. 1 is a schematic flow chart of the method of the invention.
FIG. 2 is a schematic diagram of the application flow of the method of the present invention.
Fig. 3 to 8 are fitting comparison diagrams of friction force velocity models of the six-degree-of-freedom tandem type mechanical arm joints 1 to 6, respectively.
Fig. 9-14 are graphs comparing actual moment and fitting moment of the six-degree-of-freedom tandem mechanical arm joints 1-6, respectively.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
The invention provides a mechanical arm dynamic parameter identification method based on iterative optimization, which establishes a friction model of a mechanical arm joint according to friction moment in a dynamic model of a multi-degree-of-freedom serial mechanical arm,
aiming at a linearization kinetic model in a multi-degree-of-freedom serial mechanical arm kinetic model, a weighted least square method is utilized to iteratively identify basic kinetic parameters,
and respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in a friction model by utilizing an ellipsoid method according to the friction force obtained in the basic dynamic parameters, and constraining the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamic parameters.
In particular applications, in some embodiments of the method of the invention, the steps are as follows:
s1, establishing a friction model of a mechanical arm joint according to friction moment in a dynamics model of a multi-degree-of-freedom serial mechanical arm, wherein the dynamics model of the multi-degree-of-freedom serial mechanical arm is as follows:
wherein M (q) ∈R n×n And n is the inertial matrix and the number of joints;and G (q) ∈R n×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q, & gt>Is a vector of angular displacement, angular velocity and angular acceleration of the joint n x 1; τ ε R n×1 And τ f ∈R n×1 The driving moment and the friction moment of the joint in the traditional dynamics model are respectively,
for the i (i=1, 2, …, n) th joint, a friction model of the mechanical arm joint is built according to the friction moment, as follows:
wherein delta si As non-linear parameter, alpha si Is an index parameter of Stribeck nonlinearity, alpha vi Is an exponential parameter that forms the nonlinearity of viscous friction, K ci ,K si ,K vi Indicating the friction of the joint.
Aiming at a linearization kinetic model in a multi-degree-of-freedom serial mechanical arm kinetic model, the method comprises the following steps:
wherein Y εR n×(b+3n) Is a regression matrix, pi epsilon R b+3n Is a kinetic parameter, Y b Is a regression matrix of the fundamental dynamics part, Y f Is a regression matrix of friction force part, pi b Is the basic kinetic parameter, pi f Is a parameter of the friction force, and the friction force is a parameter of the friction force,
s2, iteratively identifying basic kinetic parameters by using a weighted least square method, wherein the basic kinetic parameters comprise:
m data points are collected, and basic kinetic parameters pi are identified through a standard linear squaring method ls ，
Further identifying by weighted least square method to obtain basic kinetic parameter pi wls ，
The basic kinetic parameters pi are 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,pi is basic kinetic parameter, T is 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 standard Linear Squaring (LS) method can be expressed as
Improved recognition performance by Weighted Least Squares (WLS) technique, formulated as
R＝T-Y m ·Π Ls (6)
Wherein R is R mn Is the residual error E R n×m In the form of a deformation matrix of R
Wherein the method comprises the steps ofVariance matrix representing residual error, E j Is row j of E. To meet the dimension requirement, we get a block diagonal matrix Σ m ∈R mn×mn It 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 that the off-diagonal covariance matrix Ω e R can be calculated n×n The following are provided:
similarly, Ω m ∈R nm×nm Also a pair of blocksAn angular 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 loop, reference is made to table 1 below,
TABLE 1
S3, respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in a friction model by an ellipsoid method according to the friction force obtained in the basic dynamic parameters, and constraining the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamic parameters.
Further, the friction force F obtained in S3 according to 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 parameters include a nonlinear parameter delta si And the index parameter alpha of the Stribeck nonlinearity si Iteration is carried out by adopting an ellipsoidal method to obtain nonlinear parameter delta 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 friction force F obtained in S3 according to the fundamental kinetic parameters est Estimated molesThe approximation is made to the difference between the measured torque and the identified torque, as in equation (11),
in a similar manner, an ellipsoidal method is adopted to iteratively obtain an index parameter alpha of nonlinear viscous friction vi The formula is as follows:
each iteration alpha vi Updated in the outer loop, and both the inner and middle loops of the inner loop are 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 kinetic parameter pi.
Taking a 6-degree-of-freedom serial mechanical arm dynamics model as an example, the dynamics model is as follows:
wherein M (q) ∈R 6×6 And n is the inertial matrix and the number of joints respectively;and G (q) ∈R 6×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q, & gt>Is a vector of angular displacement, angular velocity and angular acceleration of the joint of 6 x 1; τ ε R 6×1 And τ f ∈R 6×1 Respectively the driving moment and the friction moment of the joint in the traditional dynamics model.
The friction model for the i (i=1, 2, …, 6) th joint used is as follows:
wherein delta si For the Stribeck speed, alpha si Is an exponential parameter of the Stribeck nonlinearity. Alpha vi Is an exponential parameter that creates non-linearity of viscous friction.
The linearization dynamics model is as follows, the linearization process reduces the original 60 parameters of the basic dynamics to 36:
wherein Y εR 6×(36+3*6) Is a regression matrix, pi epsilon R 36+3*6 Is a kinetic parameter, Y b Is a regression matrix of the fundamental dynamics part, Y f Is a regression matrix of friction force part, pi b Is basic kinetic parameter (36), pi f Is a friction parameter (18).
S2 when m data points are sampled, data of 1500 data points are collected at the collection frequency fs=1khz, i.e. m is 1500, and the parameters are identified as follows:
T＝Y m ·Π (4)
wherein,pi is a dynamic parameter of the device,
the inner loop of the linear least squares estimation is as follows:
a simple method of identifying the basic parameters is by the standard Linear Squaring (LS) method, which can be expressed as
Improved recognition performance by Weighted Least Squares (WLS) technique, formulated as
R＝T-Y m ·Π LS (6)
Wherein R is R mn Is the residual error E R n×m In the form of a deformation matrix of R
Wherein the method comprises the steps ofVariance matrix representing residual error, E j Is row j of E. To meet the dimension requirement, we get a block diagonal matrix Σ m ∈R mn×mn It has m identical diagonal blocks sigma.
In the WLS method, a non-diagonal covariance matrix Ω εR is calculated n×n The following are provided:
similarly, Ω m ∈R nm×nm Also a block diagonal matrix. Furthermore, to improve the accuracy of the recognition model, an iterative weighted least squares method (IRLS) is introduced to iterate the recognition base parameters. Each step in the inner loop is referred to in table 1,
s3, middle ring of Stribeck effect estimation:
based on the linear part of the basic parameters that have been determined, the friction model is considered. The estimated friction being approximated by the difference between the measured torque and the identified torque, e.g
Determination of Stribeck non-linearity parameters
For the nonlinear parameter delta in the friction model of formula (2) si The ellipsometry is used here for estimation:
n is 6, all of these parameters are constrained to be greater than zero during the optimization process, and the inner loop needs to be restarted in each iteration of the intermediate loop,
an outer loop of nonlinear viscous friction estimation:
estimating an exponential parameter alpha of non-linear viscous friction in an outer ring using a similar approach vi As shown in (12), and the residual constraint is also similar to (11), the formula is as follows:
it is evident that each iteration α vi Updated in the outer loop, both the other two loops inside 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 kinetic parameter pi.
The nonlinear parameters in the friction model and 54 parameters in the linearization dynamics model are determined through experiments, and in order to verify the effectiveness of the friction model, a fitting diagram of the friction-speed model is provided. As shown in fig. 3-8, because torque is a multiple of current, current is used instead of torque, the side is in mA and the lower is in m/s. The graph shows the current of the actual friction force, and the nearly middle broken line graph shows the friction force current fitted using the friction force model of the present invention. Parameter alpha in joint-identifying friction model s1 ＝1.1271，α v1 = 0.5009; parameter alpha in joint two-recognition friction model s2 ＝1.0023，α v2 = 0.4076; parameter alpha in joint three-recognition friction model s3 ＝0.9879，α v3 = 0.6077; parameter alpha in joint four-recognition friction model s4 ＝1.2032，α v4 = 0.5980; five-joint identification frictionParameter alpha in force model s5 ＝1.3142，α v5 = 0.5899; parameter alpha in joint six-recognition friction model s6 ＝1.2587，α v6 ＝0.4399。
In order to verify the effectiveness of the identification method, the invention provides a fitting chart for comparing the measured current with the identified current predicted value. Fig. 9-14 show the results of the verification of joints 1-6. The dashed line represents the measured current; the solid line represents the current predicted by model recognition; the middle solid line represents the error. The unit of the side surface is mA, and the graph shows that the current predicted by the invention is identified to be highly consistent with the trend of the actually measured current, which proves that the invention greatly improves the identification efficiency and precision of the parameters.
The invention also provides a mechanical arm dynamic parameter identification system based on iterative optimization, which comprises a parameter identification analysis module,
the parameter identification and analysis module establishes a friction model of the mechanical arm joint according to the friction moment in the dynamics model of the multi-degree-of-freedom serial mechanical arm,
aiming at a linearization kinetic model in a multi-degree-of-freedom serial mechanical arm kinetic model, a weighted least square method is utilized to iteratively identify basic kinetic parameters,
and respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in a friction model by utilizing an ellipsoid method according to the friction force obtained in the basic dynamic parameters, and constraining the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamic parameters.
The content of information interaction and execution process between the modules in the system is based on the same concept as the method embodiment of the present invention, and specific content can be referred to the description in the method embodiment of the present invention, which is not repeated here.
Likewise, the system can relatively quickly obtain an accurate dynamic model, and can utilize a three-loop iterative format, in the inner loop execution process, firstly, the linear component of the dynamic parameter is identified, then, the nonlinear friction parameter and the nonlinear viscous friction parameter of the Stribeck effect of the friction model are respectively considered in the middle loop and the outer loop, and further, the dynamic parameters corresponding to the nonlinear friction parameter and the nonlinear viscous friction parameter are determined, so that the identification efficiency of the parameters is greatly improved.
It should be noted that not all the steps and modules in the above processes and the system structures are necessary, and some steps or modules may be omitted according to actual needs. The execution sequence 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 multiple physical entities, or may be implemented jointly by some components in multiple independent devices.
The invention also provides a mechanical arm dynamic parameter identification device based on iterative optimization, which comprises the following steps: at least one memory and at least one processor;
the at least one memory for storing a machine readable program;
the at least one processor is configured to invoke the machine-readable program and execute the mechanical arm dynamics parameter identification method based on iterative optimization.
The content of information interaction and execution process of the processor in the device is based on the same concept as the embodiment of the method of the present invention, and specific content can be referred to the description in the embodiment of the method of the present invention, which is not repeated here.
Similarly, the device can relatively quickly obtain an accurate dynamic model, and can utilize a three-ring iterative format, in the inner ring execution process, firstly, the linear component of the dynamic parameter is identified, then, the nonlinear friction parameter and the nonlinear viscous friction parameter of the Stribeck effect of the friction model are respectively considered in the middle ring and the outer ring, and further, the dynamic parameters corresponding to the nonlinear friction parameter and the nonlinear viscous friction parameter are determined, so that the identification efficiency of the parameters is greatly improved.
The invention also provides a computer readable medium, wherein the computer readable medium is stored with computer instructions, and when the computer instructions are executed by a processor, the processor is caused to execute the mechanical arm dynamic parameter identification method based on iterative optimization. Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of the storage medium for providing 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 by a communication network.
Further, it should be apparent that the functions of any 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 part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion unit connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion unit is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
The above-described embodiments are merely preferred embodiments for fully explaining the present invention, and the scope of the present invention is not limited thereto. Equivalent substitutions and modifications will occur to those skilled in the art based on the present invention, and are intended to be within the scope of the present 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 model of a mechanical arm joint is established according to friction moment in a dynamic model of a multi-degree-of-freedom serial mechanical arm,
aiming at a linearization kinetic model in a multi-degree-of-freedom serial mechanical arm kinetic model, a weighted least square method is utilized to iteratively identify basic kinetic parameters,
and respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in a friction model by utilizing an ellipsoid method according to the friction force obtained in the basic dynamic parameters, and constraining the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamic parameters.
2. The method for identifying the mechanical arm kinetic parameters based on iterative optimization according to claim 1, wherein the mechanical arm kinetic model of the multi-degree-of-freedom serial mechanical arm is as follows:
wherein M (q) ∈R n×n And n is the inertial matrix and the number of joints;and G (q) ∈R n×1 Respectively representing a Coriolis centrifugal force matrix and a gravity moment vector; q, & gt>Is a vector of angular displacement, angular velocity and angular acceleration of the joint n x 1; r epsilon R n×1 And τ f ∈R n×1 Respectively are provided withThe driving moment and the friction moment of the joint in the traditional dynamics model are adopted.
3. The method for identifying mechanical arm dynamic parameters based on iterative optimization according to claim 2, wherein a friction model of the mechanical arm joint is established according to friction moment, and is as follows:
wherein delta si As non-linear parameter, alpha si Is an index parameter of Stribeck nonlinearity, alpha vi Index parameter of non-linear viscous friction, K ci ，K si ，K vi Indicating the friction of the joint.
4. A method for identifying mechanical arm dynamics parameters based on iterative optimization according to any one of claims 1-3, characterized in that said identifying basic dynamics parameters using weighted least square method comprises:
m data points are collected, and basic kinetic parameters are identified through a standard linear squaring method
Further identifying by weighted least square method to obtain basic kinetic parameters
Obtaining basic kinetic parameters by iterative weighted least square method identification
5. The method for identifying dynamic parameters of a mechanical arm based on iterative optimization according to claim 3, wherein the method is based on basic powerFriction force F obtained in the mathematical parameters est Iteration is carried out by adopting an ellipsoidal method to obtain nonlinear parameter delta 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 for identifying dynamic parameters of a mechanical arm based on iterative optimization according to claim 3, wherein the friction force F obtained from basic dynamic parameters est An ellipsoidal method is adopted for iteration to obtain an index parameter alpha of nonlinear viscous friction 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. an iteration optimization-based mechanical arm dynamic parameter identification system is characterized by comprising a parameter identification analysis module,
the parameter identification and analysis module establishes a friction model of the mechanical arm joint according to the friction moment in the dynamics model of the multi-degree-of-freedom serial mechanical arm,
aiming at a linearization kinetic model in a multi-degree-of-freedom serial mechanical arm kinetic model, a weighted least square method is utilized to iteratively identify basic kinetic parameters,
and respectively and iteratively obtaining a Stribeck nonlinear friction parameter and a nonlinear viscous friction parameter in a friction model by utilizing an ellipsoid method according to the friction force obtained in the basic dynamic parameters, and constraining the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter until the parameters are stable, wherein the basic dynamic parameters corresponding to the Stribeck nonlinear friction parameter and the nonlinear viscous friction parameter are used as the identified mechanical arm dynamic parameters.
8. 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 for storing a machine readable program;
the at least one processor is configured to invoke the machine readable program and perform an iterative optimization-based mechanical arm kinetic parameter identification method according to any of claims 1 to 6.
9. A computer readable medium, characterized in that the computer readable medium has stored thereon computer instructions, which when executed by a processor, cause the processor to perform a method for identifying mechanical arm dynamics parameters based on iterative optimization according to any one of claims 1 to 6.
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Citations (13)
| 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 |
| 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 |
| 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 |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI604208B (en) * | 2017-01-19 | 2017-11-01 | 義守大學 | Method of estimating dc machine parameters by least squares method |
| CN108972558B (en) * | 2018-08-16 | 2020-02-21 | 居鹤华 | Multi-axis robot dynamics modeling method based on axis invariants |
-
2022
- 2022-05-19 CN CN202210544014.4A patent/CN114888803B/en active Active
Patent Citations (13)
| 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 |
| 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 |
| 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 |
|---|
| 基于粒子群算法的机器人动力学参数辨识;傅霞君;袁建军;王帅;王宁;张伟军;;机电一体化(第02期);第3-8页 * |
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