期刊文献+

基于STM32的踝关节控制系统

Ankle Joint Control System Based on STM32
下载PDF
导出
摘要 针对目前人体行走步态难以划分的问题,笔者提出并设计一种基于STM32的测控系统,并将系统整体分为三级控制器,分别负责测量、执行命令和生成模型。电机控制参数使用SVM算法对足底压力分类,再使用状态机实现对踝关节的实时控制。该系统以STM32F407为核心,通过FSR测定足底压力,并通过ESP8266模块发送至上位机,再通过上位机的应用程序,实现相应步态的电机控制。 Aiming at the problem that it is difficult to divide the walking gait of human body,the author proposes and designs a measurement and control system based on STM32,and divides the system into three levels of controllers,which are responsible for measurement,command execution and model generation.SVM algorithm is used to classify the foot pressure and state machine is used to realize the real-time control of ankle joint.Stm32f407 is the core of the system.The plantar pressure is measured by FSR,and sent to the upper computer by esp8266 module.Then the motor control of the corresponding gait is realized by the application program of the upper computer.
作者 刘仁学 牛磊 Liu Renxue;Niu Lei(North China University of Technology,Beijing 100043,China)
机构地区 北方工业大学
出处 《信息与电脑》 2020年第6期118-120,共3页 Information & Computer
关键词 STM32 SVM FSR 物联网 STM32 SVM FSR IOT
  • 相关文献

参考文献8

二级参考文献52

  • 1王广平,马选谋.速度反馈信号的检测和处理[J].机械与电子,2004,22(8):59-62. 被引量:5
  • 2沙占友,王彦朋,张永昌.单片偏航角速度陀螺仪的原理与应用[J].传感器世界,2004,10(9):31-34. 被引量:8
  • 3孙剑峰.变频调速驱动系统中常用现场总线综述[J].工业仪表与自动化装置,2006(4):9-12. 被引量:9
  • 4秦勇,臧希喆,王晓宇,赵杰,蔡鹤皋.基于MEMS惯性传感器的机器人姿态检测系统的研究[J].传感技术学报,2007,20(2):298-301. 被引量:62
  • 5Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995
  • 6Cortes C,Vapnik V. Support Vector Networks. Machine Learning,1995,20
  • 7Osuna E, Freund R, Girosi T. Training Support Vector Machines: An Application to Face Detection. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. New York,1997
  • 8Joachims T. Text Categorization with Support Vector Machines:Learning with Many Relevant Features. In: Proceedings of the European Conference on Machine Learning, Berlin, Springer ,1998
  • 9Yang Yiming, Liu Xin. A re-examination of text categorization methods. In:Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, 1999
  • 10Joachims T. Making Large-Scale SVM Learning Practical. In:Scholkopf B, Burges C, Smola A,eds. Advances in Kernel Methods Support Vector Learning. MIT Press, 1999

共引文献81

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部