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Grey box model parameter identification method and system for robot system

A technology of robot system and gray box model, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve problems such as design and control applications, failure of robot system analysis, and low accuracy of dynamic models

Active Publication Date: 2020-08-18
BEIHANG UNIV
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

When the operating point extends to the full workspace, the nonlinear factors in industrial robots become more complex and diverse. For this strong nonlinear rigid-flexible coupling dynamic model, the traditional time-domain identification method needs to solve rigid and large differential equations , and the optimization is very sensitive to the initial value, so it has the disadvantages of strong numerical rigidity, density and sensitivity, which makes the accuracy of the dynamic model not high, and cannot accurately analyze, design and control the robot system

Method used

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  • Grey box model parameter identification method and system for robot system
  • Grey box model parameter identification method and system for robot system
  • Grey box model parameter identification method and system for robot system

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Embodiment 1

[0058] The idea of ​​the gray box model parameter identification method for the robot system provided in this embodiment is as follows:

[0059] (1) Excite the robot system to be identified to obtain time domain response data.

[0060] Firstly, multiple operating points are selected, and each operating point corresponds to an initial configuration of the robot, so that these operating points cover the entire working space of the robot as much as possible. At each operating point, the closed-loop excitation of the signal in the expected frequency band is performed on the industrial robot system to be identified, that is, the reference signal of the drive joint angle position is provided to the system, and the excitation signal is selected from the superposition of the quadrature random phase multi-sine signal and the low-frequency sinusoidal signal, Therefore, the damping effect of nonlinear friction in the transmission system is reduced, and the measurement signal-to-noise rat...

Embodiment 2

[0116] figure 2 It is a schematic diagram of the dual-arm planar robot system in Embodiment 2 of the present invention. see figure 2 , the planar robot is in the horizontal plane and has two arms, each with a motor at the joint of the arms. Each motor constitutes a drive joint, which can characterize the flexibility characteristics of the joint rotation direction. In order to pursue the accuracy of the dynamic model, a non-driven joint is added in the middle of the arm 2, which is equivalent to "dividing the arm 2 into two". Thus, an extended flexible joint configuration including 2 driven joints and 1 non-driven joint is obtained. Each boom parameter has mass m i ,i=1,2,3, the position of the center of mass Inertia tensor around the center of mass length Each parameter is described on the body coordinate system fixed on the boom. Drive the joint angular position with θ i ,i=1,2 means, the angle position of the non-driven joint is represented by θ e express. Fo...

Embodiment 3

[0153] The present invention also provides a gray box model parameter identification system for a robot system, Figure 6 It is a schematic structural diagram of a gray box model parameter identification system for a robot system according to Embodiment 3 of the present invention.

[0154] The gray box model parameter identification system for the robot system of the present embodiment includes:

[0155] The operating point selection module 201 is used to select multiple operating points in the working space of the robot system; one operating point corresponds to an initial configuration of the robot; multiple operating points cover the entire working space of the robot system.

[0156] The closed-loop excitation module 202 is configured to perform multiple closed-loop excitations on the robot system at each of the operating points to obtain time-domain input and output data of each closed-loop excitation of the operating points; the excitation signal of the closed-loop excita...

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Abstract

The invention discloses a grey box model parameter identification method and system for a robot system. The method comprises the steps that multiple times of closed-loop excitation are carried out onthe robot system at each operation point selected in a working space to obtain time domain input and output data of each time of closed-loop excitation of the operation points; nonlinear suppression and discrete Fourier transform are carried out on the time domain input and output data to determine a test frequency response function; according to state data corresponding to the operation points, and rigid body parameters, joint rigidity parameters and joint damping parameters of the robot system, an extended flexible joint dynamical model of the robot system is constructed, and a grey box model frequency response function is determined; a target optimization function is constructed according to a logarithmic error between the test frequency response function and the grey box model frequency response function; and the target optimization function is solved to obtain an optimal joint rigidity parameter and an optimal joint damping parameter. According to the method, the accurate identification of unknown joint elastic parameters in the dynamical model of the robot system can be realized.

Description

technical field [0001] The invention relates to the field of robot system parameter identification, in particular to a gray box model parameter identification method and system for a robot system. Background technique [0002] With the transformation and upgrading of my country's manufacturing industry, the development and application of automation equipment platforms represented by industrial robots is of great strategic significance for the transformation and upgrading of production models and the improvement of advanced equipment manufacturing capabilities. Modern manufacturing, especially high-tech industries, put forward strict requirements on the working cycle, load capacity and working precision of industrial robots, making them face extreme working conditions such as high speed and heavy load, and the lightweight design of industrial robots has become a development trend , so that it has complex rigid-flexible coupling dynamic characteristics, establishing a high-pre...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B25J9/16
CPCB25J9/1607B25J9/1615B25J9/163
Inventor 楚中毅沈涛张晓东孙立宁陈国栋
Owner BEIHANG UNIV
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