DESIGNING OPTIMAL FUZZY CONTROLLER FOR MRD-BASED TRAIN-CAR SUSPENSION SYSTEMS

Abstract

Random time-varying chassis mass, which consists of passenger and cargo mass as well as the normalized wind force, causes reducing the effectiveness of smart vehicle suspensions. In order to deal with this, we develop a novel fuzzy-based dynamic inversion controller (FDIC) for the control of a train-car suspension system using a magneto-rheological damper (MRD) or MRDs. The FDIC is constituted of three main parts: i) an inverse MRD model (ANFIS-I-MRD) via a measured data set and an adaptive neuro-fuzzy inference system (ANFIS), ii) a fuzzy-based sliding mode controller (FSMC) and iii) a disturbance and uncertainty observer (DUO). The FSMC is designed via the two following steps. The first one is to establish and optimize parameters of a sliding mode controller (SMC). The next is to design a fuzzy logic system to expand the ability of the SMC to face with the larger ranges of the load variation. The DUO is used to compensate for disturbance and uncertainty. By using the ANFIS-I-MRD and the control force estimated by the FDIC, current for the MRD at each time for stamping out chassis vibration is specified. The stability of the FDIC is analyzed via Lyapunov stability theory. Surveys shown that the FDIC could provide the improved control competence to reduce unwanted vibrations in an enlarged range of the varying chassis load.