Journal
CONTROL ENGINEERING PRACTICE
Volume 20, Issue 4, Pages 386-396Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2011.08.001
Keywords
Recursive identification; Hammerstein system; Muscle model; Functional electrical stimulation
Funding
- Engineering and Physical Sciences Research Council [EP/I01909X/1] Funding Source: researchfish
- EPSRC [EP/I01909X/1] Funding Source: UKRI
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Modeling of electrically stimulated muscle is considered in this paper where a Hammerstein structure is selected to represent the isometric response. Motivated by the slowly time-varying properties of the muscle system, recursive identification of Hammerstein structures is investigated. A recursive algorithm is then developed to address limitations in the approaches currently available. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Hence the procedure is termed the alternately recursive least square (ARLS) algorithm. When compared with the leading approach in this application area, ARLS exhibits superior performance in both numerical simulations and experimental tests with electrically stimulated muscle. (c) 2012 Published by Elsevier Ltd.
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