Sensing Information Forecasting for Power Assist Walking Legs Based on Time Series Analysis

摘要：

The Power Assist Walking Legs (PAWL) is an autonomous exoskeleton robot which is designed for assisting activities of daily life. In order to improve the dynamic response of the exoskeleton robot, a novel sensing information forecasting algorithm is proposed based on the time series analysis. The algorithm is built up with the autoregressive (AR) model, the recursive least square (RLS) method and the final prediction error (FPE) criterion. The method of RLS is used to make the on-line parameters estimation, and the FPE criterion is used to select the order of AR model. Because of the real-time requirement, the forecasting algorithm is designed to be used on-line and to make predictions of force sensors information to ensure the real-time quality of the whole system. According to requirements, the algorithm can be categorized into two types: one step forecasting method and multi-step forecasting method. Meanwhile, we make some correlative simulations and experiments, and the experiments demonstrate the sensing information forecasting algorithm can predict the value and the trend of the sensing signal, the results of simulations and experiments illustrate the validity and effectiveness of the algorithm.

关键词： sensing information forecasting algorithm Autoregressive model dynamic response time series analysis

作者: Zhaojun Sun Yong Yu Yunjian Ge

作者单位: Department of Automation,University of Science and Technology of China Institute of Intelligent Mach Department of Mechanical Engineering,Kagoshima University,Kagoshima 890-0065,Japan Institute of Intelligent Machines,Chinese Academy of Sciences,Hefei,Anhui Province,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)

会议地点: 珠海、澳门

会议语种:英文

页码: 830-836

在线出版日期: 2009-06-22（万方平台首次上网日期，不代表论文的发表时间）