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Self-learning model predictive control method for assembly of robot electronic components

A technology of model predictive control and electronic components, applied in the field of self-learning model predictive control, can solve the problems of small size and easy damage of electronic components, and achieve the effect of high precision and fast speed

Active Publication Date: 2020-05-29
YANCHENG TEACHERS UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Therefore, due to the small size and strong fragility of electronic components, they are easily damaged during the assembly process. In order to ensure safer and more efficient assembly of electronic components, higher precision robot electronic component assembly is required.

Method used

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  • Self-learning model predictive control method for assembly of robot electronic components
  • Self-learning model predictive control method for assembly of robot electronic components
  • Self-learning model predictive control method for assembly of robot electronic components

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0025] like figure 1 As shown, the robotic electronic component assembly process includes the following steps:

[0026]Step 1. Construction of electronic component assembly feature knowledge base S01: Determine the specific weight, center of gravity, shape, density and other characteristics of electronic components, and then build an electronic component assembly feature knowledge base to determine the assembly characteristics in the robot assembly process. The size, direction and load change of angle and assembly force. The electronic components in this embodiment are RS-05K1002FT color ring resistors.

[0027] Step 2. Planning S02 of expected trajectory (position) of electronic component assembly: planning the expected trajectory (position) of electronic component assembly according to the sequence of electronic component assembly and the b...

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Abstract

The invention discloses a self-learning model predictive control method for assembly of robot electronic components. The self-learning model predictive control method includes the steps that the difference between the position, speed and acceleration output of an end effector and a joint and an expected value is input in real time; a model predictive controller is obtained through self-learning training, and the controlling torque delay difference is output; delta[tau] is further used as feedback and as the input of the assembly robot of the electronic components so as to output the actualtarget position, speed and acceleration of the robot joint and the robot end effector in the assembly process of the electronic components; the difference between the above and the expected value is fed back to a model predictive controller to realize the self-learning of the predictive controller; compared with an existing robot assembly control method, according to the self-learning model predictive control method, the self-learning model predictive controller with the difference as input and output has the characteristics of high speed and high precision, and the needs of high precision on-line assembly of the electronic components under various interference factors can be met.

Description

technical field [0001] The invention is applicable to the field of robot intelligent assembly, and in particular relates to a self-learning model predictive control method for robot electronic component assembly. Background technique [0002] Assembly robot is a multi-input, multi-output nonlinear, strongly coupled dynamical system with time-varying position. The movement of each joint is affected by other joints, and the inertial load acting on each joint varies in a wide range with the shape of the manipulator arm. In order to solve the uncertainty of the environment and the uncertainty of the dynamic characteristics of the robot In order to achieve precise assembly control, it is necessary to design a corresponding efficient control algorithm for the dynamics of the robot and its various uncertainties, and apply it to the actual control process. [0003] The robot has skillful assembly actions such as clamping, pressing, inserting, rotating, and pushing. The completion o...

Claims

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/1687B25J9/163
Inventor 唐仕喜汤克明郭威王超王远
Owner YANCHENG TEACHERS UNIV
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