{"title":"Optimized Proportional-Integral-Derivative Control Strategies and Simulation for Lower Limb Functional Electrical Stimulation","authors":"Gu Chengwei, Jun-Ning Qian","doi":"10.1109/ICIC.2011.91","DOIUrl":null,"url":null,"abstract":"The aim of this paper was to propose a new control method of lower limb functional electrical stimulation (FES). Most traditional FES systems are controlled by open-loop pattern or feed for word pattern, here we presented a proportional-integral-derivative (PID) controller which was a closed-loop feedback controller and the parameters were optimized by back-propagation (BP) artificial neural network. Due to the nonlinear and time-varying characteristics of muscle and joint in FES, a novel musculoskeletal model was employed and the control algorithm was tested via simulation experiments. The control effect was evaluated by the position deviation between the target angle and real-time measured angle of knee joint. The results validated that the BP-PID controller performed fast and accurate in simulation tests.","PeriodicalId":6397,"journal":{"name":"2011 Fourth International Conference on Information and Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Fourth International Conference on Information and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIC.2011.91","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The aim of this paper was to propose a new control method of lower limb functional electrical stimulation (FES). Most traditional FES systems are controlled by open-loop pattern or feed for word pattern, here we presented a proportional-integral-derivative (PID) controller which was a closed-loop feedback controller and the parameters were optimized by back-propagation (BP) artificial neural network. Due to the nonlinear and time-varying characteristics of muscle and joint in FES, a novel musculoskeletal model was employed and the control algorithm was tested via simulation experiments. The control effect was evaluated by the position deviation between the target angle and real-time measured angle of knee joint. The results validated that the BP-PID controller performed fast and accurate in simulation tests.