Design of active suspension controller for train cars based on sliding mode control, uncertainty observer and neuro-fuzzy system

Abstract

This paper focuses on building a controller for active suspension system of train cars in the case that the sprung mass and model error are uncertainty parameters. The sprung mass is always varied due to many reasons such as changing of the passengers and load or impacting of wind on the operating train while an unknown difference between the suspension model used for survey and the real suspension system also always exists. The controller is built based on an adaptive neuro-fuzzy inference system (ANFIS), sliding mode control, uncertainty observer (NFSmUoC) and a magnetorheological damper (MRD) which can be seen as an actuator for applying active force. A nonlinear uncertainty observer (NUO), a sliding mode controller (SMC) together with an inverse model of the MRD are designed in order to calculate the current value by which the MRD creates the required active control force u(t). An ANFIS and measured MR-damper-dynamic-response data sets are used to identify the MRD as an inverse MRD model (ANFIS-I-MRD). Based on dynamic response of the suspension, firstly the active control force u(t) is calculated by NUO and SMC, in which the impact of the uncertainty load on the system is estimated by the NUO. The ANFIS-I-MRD is then used to estimate applied current for the MRD in order to create the calculated active control force to control vertical vibration status of the train cars. Simulation surveys are carried out to evaluate the effectiveness of the proposed method.