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.