Mechanical arm movement rhythm control method based on CPG neural network

Abstract

The invention relates to a mechanical arm movement rhythm control method based on a CPG neural network. The method comprises the following steps that (1) an upper computer is used for setting electricconductivity and reverse potential parameters corresponding to nerve cell continuous sodium current, potassium current and leakage current as well as an upper limit threshold value, a lower limit threshold value and a threshold value period during variable-threshold shaping, and transmitting the electric conductivity, the reverse potential parameters, the upper limit threshold value, the lower limit threshold value and the threshold value period to the FPGA through USB communication; (2) the FPGA is used for establishing a CPG neural network simulation model according to the set parameters, simulation is carried out, a discharge waveform is output in the simulation process, and the waveform is transmitted to the upper computer to be displayed; (3) the FPGA is used for achieving variable-threshold shaping, the discharge waveform output by the CPG neural network in the step 2 is set, moreover, a control signal is output to a mechanical arm so as to control and adjust the movement rhythmof the mechanical arm, and the angular displacement of a mechanical arm joint is transmitted to the upper computer to be displayed.

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...

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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