Authors:
Abdennacer Ben Hmed
1
;
Toufik Bakir
2
;
Yoann Garnier
3
;
Stephane Binczak
2
and
Anis Sakly
4
Affiliations:
1
Laboratory Le2i UMR 6306, CNRS, Arts et Metiers, Univ. Bourgogne Franche-Comte, Research Unit ESIER in the National School of Engineers of Monastir and University of Monastir, France
;
2
Laboratory Le2i UMR 6306, CNRS, Arts et Metiers and Univ. Bourgogne Franche-Comte, France
;
3
Laboratory INSERM UMR 1093, CAPS and Univ. Bourgogne Franche-Comté, France
;
4
Research Unit ESIER in the National School of Engineers of Monastir and University of Monastir, Tunisia
Keyword(s):
Functional Electrical Stimulation(FES), Muscle Force Model, PID Controller, PSO Algorithm, Pulse Amplitude.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Components for Control
;
Optimization Problems in Signal Processing
;
Real-Time Systems Control
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Modeling
Abstract:
Adjusting stimulation parameters using control strategy based on mathematical model, that successfully predict muscle force, may improve the efficiency of Functional Electrical Stimulation (FES) systems. It present an interesting task in industrial FES systems applications. In the present study, we investigate the PID control tuning based on the Particle Swarm Optimization (PSO) algorithm at the first time in neuro-muscular systems for updating automatically the stimulation pulse amplitude to track a desired force profiles. In the beginning, The PSO algorithm is used to identify unknown force model parameters. Next, according to the identified model, optimal PID gains are found by the same intelligent algorithm. The preliminary obtained results showed promise of using intelligent algorithm on tuning PID to perform control sessions of FES systems.