Modelling and Dynamic Analysis of Adaptive Neuro-Fuzzy Inference System-Based Intelligent Control Suspension System for Passenger Rail Vehicles Using Magnetorheological Damper for Improving Ride Index

Abstract

The ride comfort and safety of passenger rail vehicles depend on the performance of the suspension system in attenuating vibrations induced by track irregularities. This paper investigates the effectiveness of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based semi-active controlled suspension system using a magnetorheological fluid damper in reducing nonlinear lateral vibrations of a passenger rail vehicle. A complete rail vehicle model is developed, including the carbody, front and rear bogies, and the passive suspension system’s nonlinear stiffness and damping characteristics are considered from experimental data. The passive suspension model is validated through experiments, and an ANFIS-based controller is incorporated with the secondary vertical suspension system to improve ride behavior. Three semi-active suspension strategies are considered under varying speeds and track irregularities, and their effectiveness is compared to the nonlinear passive suspension system in terms of rms acceleration, rms displacement, ride quality, and comfort. The results shows that the ANFIS-based semi-active suspension system with a magnetorheological fluid damper outperforms the passive suspension system and semi-active strategies in all tested conditions. There is a reduction in rms acceleration by approximately 11.11% to 23.64% and rms displacement by about 5.36% to 32.06%. Moreover, it significantly improves ride quality (9.20% to 31.02%) and comfort (9.96% to 31.50%). The rms acceleration and displacement are reduced, and the Sperling ride index and Percentage Reduction Index values demonstrate that the ANFIS-based semi-active suspension effectively minimizes the impact of rail irregularities and vibrations, resulting in a significant gain in ride quality and passenger comfort.