NDLI: Intelligent algorithm tuning PID method of function electrical stimulation using knee joint angle

Content Provider	IEEE Xplore Digital Library
Author	Shuang Qiu Feng He Jiabei Tang Jiapeng Xu Lixin Zhang Xin Zhao Hongzhi Qi Peng Zhou Xiaoman Cheng Baikun Wan Dong Ming
Copyright Year	2014
Description	Author affiliation: Dept. of Biomed. Eng., Tianjin Univ., Tianjin, China (Shuang Qiu; Feng He; Jiabei Tang; Jiapeng Xu; Lixin Zhang; Xin Zhao; Hongzhi Qi; Peng Zhou; Baikun Wan; Dong Ming) \|\| Sch. of Sci., Tianjin Univ. of Technol., Tianjin, China (Xiaoman Cheng)
Abstract	Functional electrical stimulation (FES) could restore motor functions for individuals with spinal cord injury (SCI). By applying electric current pulses, FES system could produce muscle contractions, generate joint torques, and thus, achieve joint movements automatically. Since the muscle system is highly nonlinear and time-varying, feedback control is quite necessary for precision control of the preset action. In the present study, we applied two methods (Proportional Integral Derivative (PID) controller based on Back Propagation (BP) neural network and that based on Genetic Algorithm (GA)), to control the knee joint angle for the FES system, while the traditional Ziegler-Nichols method was used in the control group for comparison. They were tested using a muscle model of the quadriceps. The results showed that intelligent algorithm tuning PID controller displayed superior performance than classic Ziegler-Nichols method with constant parameters. More particularly, PID controller tuned by BP neural network was superior on controlling precision to make the feedback signal track the desired trajectory whose error was less than 1.2°±0.16°, while GA-PID controller, seeking the optimal parameters from multipoint simultaneity, resulted in shortened delay in the response. Both strategies showed promise in application of intelligent algorithm tuning PID methods in FES system.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	2561
Ending Page	2564
File Size	784204
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Joints Muscles Knee Genetic algorithms Neural networks Tuning Trajectory
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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