A new AI-surrogate model for dynamics analysis of a magnetorheological damper in the semi-active seat suspension
Xinhua Liu, Ningning Wang, Kun Wang, Shumei Chen, S. S. Sun, Zhixiong Li, Weihua Li
Abstract
Abstract This paper aims to develop a surrogate model for dynamics analysis of a magnetorheological damper (MRD) in the semi-active seat suspension system. An improved fruit fly optimization algorithm (IFOA) which enhances the global search capability of the original FOA is proposed to optimize the structure of a back propagation neural network (BPNN) in establishing the surrogate model. An MRD platform was fabricated to generate experimental data to feed the IFOA-BPNN model. Intrinsic patterns about the MRD dynamics behind the datasets have been discovered to establish a reliable MRD surrogate model. The outputs of the surrogate model demonstrate satisfactory dynamics characteristics in consistence with the experimental results. Moreover, the performance of the IFOA-BPNN based surrogate model was compared with that produced by the BPNN based, genetic algorithm-BPNN based, and FOA-BPNN based surrogate models. The comparison result shows better tracking capacity of the proposed method on the hysteresis behaviors of the MRD. As a result, the newly developed surrogate model can be used as the basis for advanced controller design of the semi-active seat suspension system.