Multi-transmission-mechanical-hand photoetching machine based on neural network and cooperative control system of photoetching machine

A neural network and collaborative control technology, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve the problems of delayed manipulator information interaction and collaborative work efficiency, lack of processing power and fault tolerance, product cost and space waste, etc., to achieve Improved robustness and adaptability, improved stability and accuracy, and improved communication time

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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The common control system is a controller and supporting hardware to control a transmission manipulator, which causes waste of product cost and space, and also delays the information interaction and collaborative work efficiency of the two manipulators
The traditional controller system still lacks nonlinear processing ability and fault tolerance in design

Method used

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  • Multi-transmission-mechanical-hand photoetching machine based on neural network and cooperative control system of photoetching machine
  • Multi-transmission-mechanical-hand photoetching machine based on neural network and cooperative control system of photoetching machine
  • Multi-transmission-mechanical-hand photoetching machine based on neural network and cooperative control system of photoetching machine

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0031] A neural network-based multi-transmission manipulator lithography machine, including a body, a controller, a data input and output port io module, a servo motor, a robotic arm, an encoder, a voltage mode controller, an AC power supply, and a sensor. The controller is connected to the servo motor, Data input and output port io module, voltage mode controller, sensor connection, controller, data input and output port io module, servo motor, encoder, voltage mode controller, AC power supply, sensor installed inside the body, mechanical arm and body connection, servo The connection between the motor and the mechanical arm, the connection between the servo motor and the encoder, the connection between the encoder and the voltage mode controller, the connection between the voltage mode controller and the AC power supply, the sensor, the connection between the encoder and the data input and output port io module, the connection between the controller and the data input and outpu...

Embodiment 2

[0033] As the arm makes inferences for two, it is elaborated:

[0034] Such as figure 2 As shown, the dynamic equation of a single mechanical arm is mainly the conversion relationship between the joint control q and the output torque τ, l i i=1,2 is the length of the i-th link of the mechanical arm, q i i=1,2 is the joint control angle, and the dynamic equation of a single manipulator is obtained through the Lagrange-Euler formula:

[0035]

[0036] Then deduce the dynamic equation equation of multiple manipulator cooperative control system

[0037] M(x 0 ,q)x 0 +C(x 0 ,x 0 ,q,q)x 0 +G(x 0 ,q)=τ

[0038] According to the multi-manipulator cooperative dynamics model equation, the multi-manipulator cooperative control system designed on this basis can control the movement of a single manipulator, and is also compatible with the coordinated movement of two manipulators and a single manipulator;

[0039] The RBF neural network is a radial basis function RBF neural ne...

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Abstract

The invention discloses a multi-transmission-mechanical-hand photoetching machine based on a neural network and a cooperative control system of the photoetching machine. The multi-transmission-mechanical-hand photoetching machine based on the neural network is characterized by comprising a body, a controller, a data input/output port IO module, servo motors, mechanical arms, an encoder, a voltagemode controller, an AC power supply and a sensor, and the controller is connected with the servo motors, the data input/output port IO module, the voltage mode controller and the sensor. By means of one controller and the two servo motors, the control is performed on the two mechanical arms, and then extends to the multiple mechanical arms. On the basis of a multi-mechanical-arm dynamics model function, a radial basis function (RBF) neural network function and a Lyapunov function, a multi-mechanical-arm system software algorithm is designed, and the multi-mechanical-arm cooperative control andstability of the system are realized. Only by means of one controller and the multiple servo motors, the simultaneous control over the multiple mechanical arms is realized, and the product cost and resources for space layout are reduced.

Description

technical field [0001] The invention relates to the field of lithography machine control, in particular to a neural network-based multi-transmission manipulator lithography machine and its cooperative control system. Background technique [0002] The lithography machine is the core equipment of semiconductor manufacturing, and the silicon wafer transfer manipulator is a special automatic material processing equipment inside it. Its working speed, positioning accuracy, reliability, and cleanliness directly affect the production efficiency and manufacturing quality. The common control system is a controller and supporting hardware to control a transmission manipulator, which causes waste of product cost and space, and also delays the information interaction and collaborative work efficiency of the two manipulators. The traditional controller system still lacks the ability to deal with nonlinearity and fault tolerance in design. Contents of the invention [0003] According t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B25J9/16
CPCB25J9/161B25J9/1682
Inventor 朱书伟
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