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Online Identification of Electrically Stimulated Muscle Models

Online Identification of Electrically Stimulated Muscle Models
Online Identification of Electrically Stimulated Muscle Models
Online identification of electrically stimulated muscle under isometric conditions, modeled as a Hammerstein structure, is investigated in this paper. Motivated by the significant time-varying properties of muscle, a novel recursive algorithm for Hammerstein structure is developed. 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 Recursive Least Squares (RLS) algorithm applied to the over-parametric representations of the Hammerstein structure, ARLS exhibits superior performance on experimental data from electrically stimulated muscles and a faster computational time for a single updating step. Performance is further augmented through use of two separate forgetting factors.
90-95
Le, Fengmin
3e44aa4d-33ea-4697-a9c2-b2bff35cee3c
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Le, Fengmin
3e44aa4d-33ea-4697-a9c2-b2bff35cee3c
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72

Le, Fengmin, Markovsky, Ivan, Freeman, Christopher and Rogers, Eric (2011) Online Identification of Electrically Stimulated Muscle Models. American Control Conference 2011, San Francisco, California, USA, June 29 - July 1, 2011. pp. 90-95 .

Record type: Conference or Workshop Item (Paper)

Abstract

Online identification of electrically stimulated muscle under isometric conditions, modeled as a Hammerstein structure, is investigated in this paper. Motivated by the significant time-varying properties of muscle, a novel recursive algorithm for Hammerstein structure is developed. 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 Recursive Least Squares (RLS) algorithm applied to the over-parametric representations of the Hammerstein structure, ARLS exhibits superior performance on experimental data from electrically stimulated muscles and a faster computational time for a single updating step. Performance is further augmented through use of two separate forgetting factors.

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Published date: 2011
Venue - Dates: American Control Conference 2011, San Francisco, California, USA, June 29 - July 1, 2011, 2011-01-01
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 271584
URI: http://eprints.soton.ac.uk/id/eprint/271584
PURE UUID: 30cc13e2-5448-4ba2-b67a-b2eaf9e478e9
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

Catalogue record

Date deposited: 23 Sep 2010 15:34
Last modified: 20 Jul 2019 01:23

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