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Predictor‐based tracking for neuromuscular electrical stimulation
Author(s) -
Karafyllis Iasson,
Malisoff Michael,
de Queiroz Marcio,
Krstic Miroslav,
Yang Ruzhou
Publication year - 2014
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3211
Subject(s) - novelty , control theory (sociology) , constraint (computer aided design) , tracking error , controller (irrigation) , tracking (education) , sampling (signal processing) , work (physics) , computer science , state (computer science) , control engineering , mathematics , engineering , control (management) , artificial intelligence , algorithm , psychology , computer vision , mechanical engineering , social psychology , pedagogy , geometry , filter (signal processing) , agronomy , biology
Summary We present a new tracking controller for neuromuscular electrical stimulation (NMES), which is an emerging technology that artificially stimulates skeletal muscles to help restore functionality to human limbs. The novelty of our work is that we prove that the tracking error globally asymptotically and locally exponentially converges to zero for any positive input delay, coupled with our ability to satisfy a state constraint imposed by the physical system. Also, our controller only requires sampled measurements of the states instead of continuous measurements and allows perturbed sampling schedules, which can be important for practical purposes. Our work is based on a new method for constructing predictor maps for a large class of time‐varying systems, which is of independent interest. Copyright © 2014 John Wiley & Sons, Ltd.