Optimal inverse magnetorheological damper modeling using shuffled frog-leaping algorithm–based adaptive neuro-fuzzy inference system approach

Abstract

Magnetorheological dampers have become prominent semi-active control devices for vibration mitigation of structures which are subjected to severe loads. However, the damping force cannot be controlled directly due to the inherent nonlinear characteristics of the magnetorheological dampers. Therefore, for fully exploiting the capabilities of the magnetorheological dampers, one of the challenging aspects is to develop an accurate inverse model which can appropriately predict the input voltage to control the damping force. In this article, a hybrid modeling strategy combining shuffled frogleaping algorithm and adaptive-network-based fuzzy inference system is proposed to model the inverse dynamic characteristics of the magnetorheological dampers for improving the modeling accuracy. The shuffled frog-leaping algorithm is employed to optimize the premise parameters of the adaptive-network-based fuzzy inference system while the consequent parameters are tuned by a least square estimation method, here known as shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach. To evaluate the effectiveness of the proposed approach, the inverse modeling results based on the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach are compared with those based on the adaptive-network-based fuzzy inference system and genetic algorithm-based adaptive-network-based fuzzy inference system approaches. Analysis of variance test is carried out to statistically compare the performance of the proposed methods and the results demonstrate that the shuffled frogleaping algorithm-based adaptive-network-based fuzzy inference system strategy outperforms the other two methods in terms of modeling (training) accuracy and checking accuracy.

Data-driven prediction-control system modeling for magnetorheological damping force

“…An artificial neural network modeling approach to reproduce MR damper or MR elastomer base isolator’s hysteretic nonlinear behavior was verified to achieve appropriate accuracy and good forecasting results (Khalid et al, 2014; Luong et al, 2020; Yu et al, 2015). Also, a fuzzy inference based model, which is well known to be an excellent model for uncertain dynamic systems was usually employed in study on control link (Askari et al, 2016; Lin and Chen, 2016; Nguyen et al, 2017; Song et al, 2017). The literature demonstrates that the fuzzy model based current/voltage prediction strategy is also the mainstream of current research on semi-active control of magnetorheological damper.…”

Modelagem Matemática Inversa de um Amortecedor Magnetoreológico

“…[12] propuseram um modelo inverso simples baseado no modelo modificado de Dahl, de forma a obter precisamente a intensidade de corrente de controle esperada. [5] desenvolveram um modelo inverso híbrido, combinando sistemas de inferência nebulosa adaptativa (ANFIS ) e algoritmo frog-leaping.…”