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

Xiufang Lin,Shumei Chen

doi:10.1177/1687814016662770

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 frog-leaping 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 frog-leaping algorithm-based adaptive-network-based fuzzy inference system strategy outperforms the other two methods in terms of modeling (training) accuracy and checking accuracy.

Highlights

Nowadays, it is challenging to find an effective means of protecting structures from dynamic hazards such as earthquakes and strong winds that may cause casualties and tremendous economic loss
The results showed that shuffled frogleaping algorithm (SFLA) was faster and more accurate in comparison with other evolutionary algorithms (EAs) including particle swarm optimization (PSO), genetic algorithm (GA), differential evolution, and simulated annealing
SFLA is employed to determine the premise parameters of the adaptive neuro-fuzzy inference system (ANFIS) while whose consequent parameters are tuned by least square estimation (LSE)

Summary

Introduction

It is challenging to find an effective means of protecting structures from dynamic hazards such as earthquakes and strong winds that may cause casualties and tremendous economic loss. Building up the optimized ANFIS and calculating the predicted voltage and damping force: first, use the optimal premise and consequent parameters to construct an ANFIS, obtaining the optimal inverse model with the proposed strategy. To insure a valid inverse model, the size of input– output data must be large enough and cover all frequency and amplitude ranges for fine tuning the parameters For this reason, the input displacement is generated by using band-limited, Gaussian white-noise signals with amplitude of 4 cm and frequency between 0 and 3 Hz. The velocity is obtained from the displacement signals according to a second-order backward difference method. If the number of the input data sets is increased, the inverse model will become very complex and the training time will be increased enormously, especially when the model is optimized with EAs. Besides, the damping force can be calculated through the forward model if the displacement, velocity, and command voltage are given. RMSE: root-mean-square error; SFLA: shuffled frog-leaping algorithm; ANFIS: adaptive neuro-fuzzy inference system

Objective value

Findings

Conclusion