multiple models

Abstract: In this paper we present an adaptive controller implementation based on the multiple models, switching, and tuning (MMST) paradigm [13, 14, 15] for preventing un–tripped rollover in automotive vehicles. Our approach relies on differential-braking to keep the value of the Load Transfer Ratio (LT R) below a threshold. We first employ multiple models to infer the unknown center of gravity height and the suspension parameters of the vehicle, which are subsequently used to switch to the corresponding rollover controller. The proposed multi–controller switched scheme is shown via numerical simulations to result in better performance than its fixed robust counterpart.

Loading conditions of railway vehicles both affect the vehicle and substructure directly. There are approaches to determine the load of a railway vehicle, the first one is to statically weigh the vehicle, the second approach is to place sensors along the track and dynamically weigh the vehicle at certain sections and the third one is to design special sensors that can be implemented on the vehicle. In this study, a model based indirect estimation methodology for normal load is proposed. This approach is based on interpreting angular velocities of wheels and translational velocity measurements of a vehicle to determine the normal load. A swarm intelligence based evolution of multiple models is proposed for estimation. In order to validate the approach, measurements taken from a tram wheel test stand with an independently rotating wheel are considered. The proposed approach is promising to be used as a dynamic weighing system and cost--efficient since only vehicle--based sensors are used. Furthermore, a continuous monitoring of the normal load is made possible with high accuracy since this methodology is neither limited to track--based sensors nor it requires special sensors and instrumented wheelsets.