Skip to Main content Skip to Navigation
Conference papers

Lower Limbs Movement Restoration using Input-Output Feedback Linearization and Model Predictive Control

Samer Mohammed 1 Philippe Poignet 2 Philippe Fraisse 1 David Guiraud 3
2 DEXTER - Conception et commande de robots pour la manipulation
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The main challenge that we face when applying Functional Electrical Stimulation (FES) to paralyzed lower limbs is to avoid hyperstimulation and to defer the muscular fatigue as much as possible. FES is used to excite paralyzed muscles that are under lesions and consequently no more controlled by paraplegic patients. We aimed in this study to compute the needed patterns stimulation necessary to perform a desired given motion of the knee joint. We coupled the exact Input Output Feedback Linearization with a Model predictive Controller (MPC). This latter enables us to incorporate explicitly constraints on inputs, outputs and system states. Internal dynamics stability was mathematically proved and MPC performances were compared to a classical pole placement controller in terms of robustness, stability and finite time convergence.
Document type :
Conference papers
Complete list of metadatas

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00194986
Contributor : Samer Mohammed <>
Submitted on : Friday, December 7, 2007 - 9:41:09 PM
Last modification on : Thursday, March 5, 2020 - 4:52:41 PM

Identifiers

  • HAL Id : lirmm-00194986, version 1

Citation

Samer Mohammed, Philippe Poignet, Philippe Fraisse, David Guiraud. Lower Limbs Movement Restoration using Input-Output Feedback Linearization and Model Predictive Control. IROS: Intelligent Robots and Systems, Oct 2007, San Diego, United States. pp.1945-1950. ⟨lirmm-00194986⟩

Share

Metrics

Record views

233