详情

原文传递 Vehicle Sprung Mass Estimation for Rough Terrain.

题名：	Vehicle Sprung Mass Estimation for Rough Terrain.
作者：	Fathy, H.; Hays, J.; Pence, B.; Sandu, C.; Stein, J.
关键词：	Accuracy; Algorithms; Ground Vehicles; Kalman Filtering; Least Squares Method; Maximum Likelihood Estimation; Polynomials; Roughness; Terrain
摘要：	This paper provides methods and experimental results for recursively estimating the sprung mass of a vehicle driving on rough terrain. It presents a base-excitation model of vertical ride dynamics which treats the unsprung vertical accelerations, instead of the terrain profile, as the ride dynamics model input. It employs recently developed methods based on polynomial chaos theory and on the maximum likelihood approach to estimate the most likely value of the vehicle sprung mass. The polynomial chaos estimator is compared to benchmark algorithms including recursive least squares, recursive total least squares, extended Kalman filtering, and unscented Kalman filtering approaches. The paper experimentally demonstrates the proposed method. The results of the experimental study suggest that the proposed approach provides accurate outputs and the proposed method is less sensitive to tuning parameters when compared with the benchmark algorithms.
报告类型：	科技报告