A novel minimum-maximum data-clustering algorithm for vibration control of a semi-active vehicle suspension system

摘要

In this paper, a new control system for a semi-active vehicle suspension is presented by proposing a novel minimum-maximum (min-max) pure data-clustering algorithm. The min-max pure data-clustering algorithm is used to create pure data clusters which are the basic structure for building fuzzy sets and membership functions of an adaptive neuro-fuzzy inference system. Based on the data clusters created by the min-max pure data-clustering algorithm, an appropriate structure of the adaptive neuro-fuzzy inference system is established and used to identify the dynamic behaviour of a semi-active vehicle suspension system featuring a magnetorheological-fluid-based damper. In this control system, both the measured data and an inverse dynamic model of the damper are used. To calculate the desired damping force value corresponding to the road profile at a specific time, a fuzzy inference system is built on the basis of a genetic algorithm. In this work, the fitness function of the genetic algorithm is satisfactorily considered to create the optimal fuzzy inference system structure, which expresses the ride-comfort-oriented tendency. Based on the desired force value, the desired current value is obtained by the inverse dynamic model. This is the optimal current value to be applied to the magnetorheological-fluid-based damper to reduce the acceleration of the vehicle. The effectiveness of the proposed control algorithm is demonstrated by vibration control performances such as reducing the vertical acceleration of the vehicle body and increasing the road-holding ability of the vehicle tyre. In addition, a comparison between the proposed work and the previous work is undertaken in order to show the superior vibration control performance of the proposed control strategy.