A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization
A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization
复制标题
DOI：
10.1016/j.sna.2006.03.015
复制
发表时间：
2006-11-20
影响因子：
4.6
通讯作者：
Samali, B.
中科院分区：
文献类型：
--
作者：
Kwok, N. M.;Ha, Q. P.;Samali, B.
非线性迟滞是磁流变阻尼器的一个复杂现象。本文提出了一种新的磁流变阻尼器模型。对于这一点，计算上容易处理的代数表达式，建议在这里对比常用的Bouc-Wen模型，其中涉及内部动态表示的非线性微分方程。此外，模型参数可以明确地与滞后现象。为了识别模型参数，粒子群优化（PSO）算法采用实验力-速度数据从各种操作条件下获得。在我们的算法中，它是可能的，以放宽对先验知识的参数的需要，并降低算法的复杂性。在这里，粒子群优化算法是通过引入一个终止标准，统计假设检验的基础上，以保证用户指定的置信水平停止算法。参数识别结果包括证明模型的准确性和识别过程的有效性。(c)2006 Elsevier B. V.保留所有权利。
Non-linear hysteresis is a complicated phenomenon associated with magnetorheological (MR) fluid dampers. A new model for MR dampers is proposed in this paper. For this, computationally-tractable algebraic expressions are suggested here in contrast to the commonly-used Bouc-Wen model, which involves internal dynamics represented by a non-linear differential equation. In addition, the model parameters can be explicitly related to the hysteretic phenomenon. To identify the model parameters, a particle swarm optimization (PSO) algorithm is employed using experimental force-velocity data obtained from various operating conditions. In our algorithm, it is possible to relax the need for a priori knowledge on the parameters and to reduce the algorithmic complexity. Here, the PSO algorithm is enhanced by introducing a termination criterion, based on the statistical hypothesis testing to guarantee a user-specified confidence level in stopping the algorithm. Parameter identification results are included to demonstrate the accuracy of the model and the effectiveness of the identification process. (c) 2006 Elsevier B.V. All rights reserved.