Fuzzy sliding mode control based on hybrid Taguchi genetic algorithm for magneto-rheological suspension system

Abstract

Magneto-rheological (MR) suspension systems display non-linearity and parameter uncertainty and thus it is difficult to derive an accurate model for designing a model-based controller. In this study, a novel fuzzy sliding mode control (FSMC) approach based on the hybrid Taguchi genetic algorithm (HTGA) is proposed to suppress the vibration of the MR suspension system. As the first step, the MR absorber is designed and manufactured based on the damping force level and mechanical dimensions required for the test car. After experimentally measuring the current-dependent damping force characteristic, a precise inverse model of the MR absorber is formulated. Subsequently, FSMC based on the HTGA is formulated on the basis of a quarter car model incorporated with an MR absorber. The linguistic variables and control rules of the fuzzy logic controller are optimized using the HTGA. For comparison purposes, two representative controllers including a conventional sliding mode controller and a fuzzy logic controller are also proposed. Finally, simulations and a road test are performed to validate the effectiveness and robustness of the proposed FSMC.