基于sEMG信号和BPNN算法的机械臂控制系统设计  

Design of Robot Arm Control System Based on sEMG Signal and BPNN Algorithm

作　　者：韩团军[1] 张晶 黄朝军[1] 王桂宝 HAN Tuanjun;ZHANG Jing;HUANG Chaojun;WANG Guibao(Physics&Telecommunications Engineering Department,Shaanxi University of Technology,Hanzhong Shaanxi 723000,China)

机构地区：[1]陕西理工大学物理与电信工程学院,陕西汉中723000

出　　处：《机床与液压》2023年第19期106-111,共6页Machine Tool & Hydraulics

基　　金：国家自然科学基金项目(61972239);陕西省教育厅专项科学研究计划项目(18JK0160);陕西省教育厅专项科研计划(18JK0154);陕西理工大学科研基金项目(SLGKYXM2308);陕西省科技厅研究项目(2022GY-122)。

摘　　要：为了解决市场康复假肢功能单一、使用效果极差和价格昂贵等缺点,提出一种基于表面肌电信号的机械手控制系统。该系统主要分为两部分:一部分是基于Cortex-M4系列的肌电信号采集、预处理、BP神经网络分类的信号处理系统;另一部分是基于Cortex-M3系列的机械手臂控制系统。信号处理系统发出控制命令无线传输到机械臂,控制6舵机自由度的机械臂,实现6个动作的展示。试验结果证明:该系统能够实现6个动作的自学习,成功率在80%以上,系统有一定的应用价值。In order to solve the shortcomings of single function,extremely poor use effect and high price of market rehabilitation prosthesis,a control system of the manipulator was proposed based on surface EMG signals.The system was mainly divided into two parts.The one part was a signal processing system based on Cortex-M4 series EMG signal acquisition,preprocessing,BP neural network classification.The other part was the robotic arm control system based on the Cortex-M3 series.The signal was sent by the processing system to control the robotic arm with 6 DOF to realize the display of six actions through wireless transmission.The test results show that the system can realize self-learning of six actions,the success rate is over 80%,and the system has certain application value.

关 键 词：表面肌电信号 BP神经网络 机械臂 控制系统 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]