Recursive identification of Hammerstein systems with application to electrically stimulated muscle
Recursive identification of Hammerstein systems with application to electrically stimulated muscle
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DOI：
10.1016/j.conengprac.2011.08.001
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发表时间：
2012-04-01
影响因子：
4.9
通讯作者：
Rogers, Eric
中科院分区：
文献类型：
--
作者：
Le, Fengmin;Markovsky, Ivan;Rogers, Eric
本文考虑电刺激肌肉的建模，其中选择Hammerstein结构来表示等距响应。基于肌肉系统的慢时变特性，研究了Hammerstein结构的递归辨识。然后开发递归算法来解决当前可用方法中的局限性。线性和非线性参数以并行方式递归分离和估计，每个更新算法都使用另一个算法在每个时刻产生的最新估计。因此，这个过程被称为交替递归最小二乘（ARLS）算法。与该应用领域的领先方法相比，ARLS在电刺激肌肉的数值模拟和实验测试中都表现出优越的性能。(c) 2012年Elsevier Ltd.出版。
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.