Genetic Algorithms Based Approach for Designing Spring Brake Orthosis – Part Ii: Control of FES Induced Movement
Author(s) -
M. S. Huq,
M. O. Tokhi
Publication year - 2012
Publication title -
applied bionics and biomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.397
H-Index - 23
eISSN - 1754-2103
pISSN - 1176-2322
DOI - 10.1155/2012/275152
Subject(s) - brake , functional electrical stimulation , swing , control theory (sociology) , knee joint , computer science , torsion spring , movement (music) , torque , simulation , engineering , automotive engineering , structural engineering , control (management) , artificial intelligence , mechanical engineering , medicine , philosophy , physics , thermodynamics , surgery , neuroscience , stimulation , biology , aesthetics
Spring brake orthotic swing phase for paraplegic gait is initiated through releasing the brake on the knee mounted with a torsion spring. The stored potential energy in the spring, gained from the previous swing phase, is solely responsible for swing phase knee flexion. Hence the later part of the SBO operation, functional electrical stimulation (FES) assisted extension movement of the knee has to serve an additional purpose of restoring the spring potential energy on the fly. While control of FES induced movement as such is often a challenging task, a torsion spring, being antagonistically paired up with the muscle actuator, as in spring brake orthosis (SBO), only adds to the challenge. Two new schemes are proposed for the control of FES induced knee extension movement in SBO assisted swing phase. Even though the control schemes are closed-loop in nature, special attention is paid to accommodate the natural dynamics of the mechanical combination being controlled (the leg segment) as a major role playing feature. The schemes are thus found to be immune from some drawbacks associated with both closed-loop tracking as well as open-loop control of FES induced movement. A leg model including the FES knee joint model of the knee extensor muscle vasti along with the passive properties is used in the simulation. The optimized parameters for the SBO spring are obtained from the earlier part of this work. Genetic algorithm (GA) and multi-objective GA (MOGA) are used to optimize the parameters associated with the control schemes with minimum fatigue as one of the control objectives. The control schemes are evaluated in terms of three criteria based on their ability to cope with muscle fatigue.
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