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Motion rhythm control method of manipulator based on cpg neuron network

A neuron network and neuron technology, applied in the field of biomedical engineering, can solve problems such as low degree of simulation, inability of the controller to flexibly control the mechanical arm, slowness, etc., and achieve the effect of high computing speed

Active Publication Date: 2021-08-03
TIANJIN UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The existing technology has the following disadvantages: the existing control strategy mostly adopts the method of PWM to adjust the duty cycle, the simulation degree of the brain to control the arm movement is low, the control effect is relatively stiff and slow, and the controller cannot flexibly control the mechanical arm

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  • Motion rhythm control method of manipulator based on cpg neuron network
  • Motion rhythm control method of manipulator based on cpg neuron network
  • Motion rhythm control method of manipulator based on cpg neuron network

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Embodiment Construction

[0036] The technical solutions of the present invention will be clearly and completely described and illustrated below in conjunction with the embodiments and the accompanying drawings, but this is not intended to limit the scope of protection of the present application.

[0037] figure 1It is the general design block diagram of the system adopted by the present invention, and what the core FPGA chip adopts is Stratix III EP3SE260F1152C2N of Altera Company. Use the DSP Builder in MATLAB software to write the module-based CPG neuron network model, compile and synthesize it with Quartus II software, and download it to the chip to form the corresponding hardware circuit.

[0038] The motion rhythm control method of the manipulator based on the CPG neuron network is characterized in that the method is implemented with the help of the host computer and the FPGA development board, and the required relevant parameters are set in the host computer, and are transmitted to the FPGA deve...

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Abstract

The present invention relates to a kind of mechanical arm motion rhythm control method based on CPG neuron network, comprising the following steps: (1) the host computer sets the conductivity and back EMF parameters corresponding to the neuron continuous nanocurrent, potassium current and leakage current, and The upper and lower limit thresholds and the threshold cycle of variable threshold shaping are transmitted to the FPGA through USB communication; (2) FPGA establishes a CPG neuron network simulation model according to the set parameters for simulation, and outputs discharge waveforms during the simulation process. The waveform is transmitted to the host computer for display; (3) FPGA is used to realize variable threshold shaping, and the discharge waveform output by the CPG neuron network in step 2 is adjusted, and the control signal is output to the manipulator, so as to control and adjust the movement rhythm of the manipulator, And the angular displacement of the joints of the manipulator is transmitted to the host computer for display.

Description

technical field [0001] The invention relates to biomedical engineering technology, in particular to an experimental method for controlling the movement rhythm of a mechanical arm based on a CPG neuron network. Background technique [0002] In recent years, with the development of robot technology, the structure of manipulators with high speed, high precision and high load-to-weight ratio has attracted attention from various fields. With the improvement of people's requirements for the application of robotic arms, the trajectory of the traditional robotic arm generated by manual planning is very different from the real human arm in terms of the continuity of the trajectory and the naturalness of the movement. Therefore, the structural flexibility characteristics of the robot manipulator must be considered, and the dynamic characteristics of the system must be analyzed to effectively control the accuracy of the flexible manipulator. Simply relying on traditional design methods...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B25J9/16G06N3/063
CPCB25J9/16B25J9/161G06N3/063
Inventor 于海涛李凯张鲲孟紫寒王江
Owner TIANJIN UNIV
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