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.

Vibration control systems using smart dampers (SmDs) such as magnetorheological and electrorheological dampers (MRD and ERD), which are classified as the integrated structure-SmD control systems (ISSmDCSs), have been actively researched and widely used. This work proposes a new controller for a class of ISSmDCSs in which high accuracy of SmD models as well as increment of control ability to deal with uncertainty and time delay are to be expected. In order to achieve this goal, two formualtion steps are required; a non-parametric SmD model based on an adaptive neuro-fuzzy inference system (ANFIS) and a novel fuzzy sliding mode controller (FSMC) which can weaken the model error of the ISSmDCSs and hence provide enhanced vibration control performances. As for the formulation of the proposed controller, first, an ANFIS controller is desgned to identify SmDs using the improved control algorithm named improved establishing neuro-fuzzy system (establishing neuro-fuzzy system). Second, a new control law for the FSMC is designed via Lyapunov stability analysis. An application to a semi-active MRD vehicle suspension system is then undertaken to illustrate and evaluate the effectiveness of the proposed control method. It is demonstrated through an experimental realization that the FSMC proposed in this work shows superior vibration control performance of the vehicle suspension compared to other surveyed controller which have similar structures to the FSMC, such as fuzzy logic and sliding mode control.