Grey Fuzzy sliding Mode controller for Vehicle Suspension System

Abstract

This paper presents a Grey Fuzzy Sliding Mode controller (GFSMC) for enhancing the ride comfort of Vehicle Suspension System (VSS). Grey Prediction algorithm is used to predict the output or the error of the system with small amount of data. The prediction error in Grey Model (GM) is minimized by changing the initial condition and optimizing the weight factor in the data matrix of grey prediction algorithm using Particle Swarm Optimization (PSO). The aim of this paper is to design a Sliding Mode Controller (SMC) for vehicle suspension system. The chattering phenomena that occur in SMC, is eliminated by combining Fuzzy Logic with SMC.  For further enhancement the Fuzzy Sliding Mode Controller is combined with Grey Prediction algorithm to develop GFSMC. The proposed controller is simulated for a Quarter Car model of VSS. Simulation results show that the proposed controller enhances ride comfort. Even under perturbed conditions the proposed controller offers robust performance. Power Spectral Density also proves the effectiveness of GFSMC in terms of ride comfort.