Real-Time Ground Vehicle Parameter Estimation and System Identification for Improved Stability Controllers

Vehicle characteristics have a significant impact on handling, stability, and rollover propensity. This research is dedicated to furthering the research in and modeling of vehicle dynamics and parameter estimation. Parameter estimation is a challenging problem. Many different elements play into the stability of a parameter estimation algorithm. The primary trade-off is robustness for accuracy. Lyapunov estimation techniques, for instance, guarantee stability but do not guarantee parameter accuracy. The ability to observe the states of the system, whether by sensors or observers is a key problem. This research significantly improves the Generalized Polynomial Chaos Extended Kalman Filter (gPC-EKF) for state-space systems. Here it is also expanded to parameter regression, where it shows excellent capabilities for estimating parameters in linear regression problems. The modeling of ground vehicles has many challenges. Compounding the problems in the parameter estimation methods, the modeling of ground vehicles is very complex and contains many difficulties. Full multibody dynamics models may be able to accurately represent most of the dynamics of the suspension and vehicle body, but the computational time and required knowledge is too significant for real-time and realistic implementation. The literature is filled with different models to represent the dynamics of the ground vehicle, but these models were primarily designed for controller use or to simplify the understanding of the vehicle’s dynamics, and are not suitable for parameter estimation. A model is devised that can be utilized for the parameter estimation. The parameters in the model are updated through the aforementioned gPC-EKF method as applies to polynomial systems. The mass and the horizontal center of gravity (CG) position of the vehicle are estimated to high accuracy. The culmination of this work is the estimation of the normal forces at the tire contact patch. These forces are estimated through a mapping of the suspension kinematics in conjunction with the previously estimated vehicle parameters. A proof of concept study is shown, where the system is mapped and the forces are recreated and verified for several different scenarios and for changing vehicle mass.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2014

Author dc:creator

• Kolansky, Jeremy Joseph

Chair dc:contributor.committeechair

• Sandu, Corina

Committee members dc:contributor.committeemember

• Ahmadian, Mehdi
• Southward, Steve C.
• Fathy, Hosam K.
• Tarazaga, Pablo Alberto
• Els, Pieter Schalk

• Parameter Estimation
• Signal Processing
• Kalman Filter
• Polynomial Chaos
• Ground Vehicle
• Stability Control
• System Identification
• Real-Time Estimation

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:2433

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/47351