Mechanical arm movement rhythm control method based on CPG neural network

A neuron network and neuron technology, applied in the field of biomedical engineering, can solve the problems of low simulation degree, rigid control effect, slowness, etc., and achieve the effect of high computing speed

Active Publication Date: 2019-02-19
TIANJIN UNIV
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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 simulati...

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  • Mechanical arm movement rhythm control method based on CPG neural network
  • Mechanical arm movement rhythm control method based on CPG neural network
  • Mechanical arm movement rhythm control method based on CPG neural 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 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...

Claims

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Application Information

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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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