This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models
Road resistance is commonly divided into three different components; rolling resistance, wind resist...
The goal of regression and classification methods in supervised learning is to minimize the empirica...
We introduce a recursive generalized total least-squares (RGTLS) algorithm with exponential forgetti...
This work provides novel robust and regularized algorithms for parameter estimation with application...
This dissertation uses polynomial chaos theory to address recursive parameter estimation in state sp...
This Master’s thesis describes a method for real-time estimation of a vehicle’s mass for automobiles...
The gross vehicle mass (GVM) and the road grade are two factors that both have a substantial influen...
An accurate estimate of the mass of a passenger vehicle is important for several safety systems and ...
Several methods have been developed to identify forces moving on a beam from measured responses. The...
To solve the vehicle mass estimation problems, two vehicle mass estimation methods based on both com...
This thesis describes development of a real-time-implementable algorithm for simultaneous estimation...
Abstract: Good estimates of vehicle mass and road grade are important in automation of heavy duty ve...
ii Acknowledgments I’d like to acknowledge and express gratitude to the people that have inspired an...
This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering wit...
Statistical spatial repeatability (SSR) is an extension to the well known concept of spatial repeata...
Road resistance is commonly divided into three different components; rolling resistance, wind resist...
The goal of regression and classification methods in supervised learning is to minimize the empirica...
We introduce a recursive generalized total least-squares (RGTLS) algorithm with exponential forgetti...
This work provides novel robust and regularized algorithms for parameter estimation with application...
This dissertation uses polynomial chaos theory to address recursive parameter estimation in state sp...
This Master’s thesis describes a method for real-time estimation of a vehicle’s mass for automobiles...
The gross vehicle mass (GVM) and the road grade are two factors that both have a substantial influen...
An accurate estimate of the mass of a passenger vehicle is important for several safety systems and ...
Several methods have been developed to identify forces moving on a beam from measured responses. The...
To solve the vehicle mass estimation problems, two vehicle mass estimation methods based on both com...
This thesis describes development of a real-time-implementable algorithm for simultaneous estimation...
Abstract: Good estimates of vehicle mass and road grade are important in automation of heavy duty ve...
ii Acknowledgments I’d like to acknowledge and express gratitude to the people that have inspired an...
This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering wit...
Statistical spatial repeatability (SSR) is an extension to the well known concept of spatial repeata...
Road resistance is commonly divided into three different components; rolling resistance, wind resist...
The goal of regression and classification methods in supervised learning is to minimize the empirica...
We introduce a recursive generalized total least-squares (RGTLS) algorithm with exponential forgetti...