State Estimation Method Based on Unscented Kalman Filter for Vehicle Nonlinear Dynamics

This paper provides a state estimation method for
automatic control systems of nonlinear vehicle dynamics. A nonlinear
tire model is employed to represent the realistic behavior of a vehicle.
In general, all the state variables of control systems are not precisedly
known, because those variables are observed through output sensors
and limited parts of them might be only measurable. Hence, automatic
control systems must incorporate some type of state estimation. It is
needed to establish a state estimation method for nonlinear vehicle
dynamics with restricted measurable state variables. For this purpose,
unscented Kalman filter method is applied in this study for estimating
the state variables of nonlinear vehicle dynamics. The objective of
this paper is to propose a state estimation method using unscented
Kalman filter for nonlinear vehicle dynamics. The effectiveness of
the proposed method is verified by numerical simulations.

Authors:

• Wataru Nakamura
• Tomoaki Hashimoto
• Liang-Kuang Chen

Keywords:

• State estimation
• control systems
• observer systems
• unscented Kalman filter
• nonlinear vehicle dynamics.

References:

[1] J. Liu, P. Jayakumar, J.L. Stein and T. Ersal, A nonlinear model
predictive control formulation for obstacle avoidance in high-speed
autonomous ground vehicles in unstructured environments, Vehicle
System Dynamics, Vol. 56, Issue 6, pp. 853-882, 2018.
[2] M. Ataei, A. Khajepour and S. Jeon, Model predictive control
for integrated lateral stability, traction/braking control, and rollover
prevention of electric vehicles, Vehicle System Dynamics, Vol. 58, Issue
1, pp. 49-73, 2019.
[3] M.S. Basrah, E. Siampis, E. Velenis, D. Cao and S. Longo Wheel slip
control with torque blending using linear and nonlinear model predictive
control, Vehicle System Dynamics, Vol. 55, Issue 11, pp. 1665-1685,
2017.
[4] S.A. Sajadi-Alamdari, H. Voos and M. Darouach, Nonlinear Model
Predictive Control for Ecological Driver Assistance Systems in Electric
Vehicles, Robotics and Autonomous Systems, Vol. 112, No. 2, pp.
291-303, 2019.
[5] T. Baba, T. Hashimoto and Liang-Kuang Chen, Model Predictive Control
for Stabilization of Vehicle Nonlinear Dynamics to Avoid the Second
Collision Accident, Proceedings of the 22nd International Conference
on Advances in Materials and Processing Technology, TA1-6, 2019.
[6] T. Hashimoto, Y. Yoshioka and T. Ohtsuka, Receding Horizon Control
With Numerical Solution for Nonlinear Parabolic Partial Differential
Equations, IEEE Transactions on Automatic Control, Vol. 58, No. 3,
pp. 725-730, 2013.
[7] T. Hashimoto, R. Satoh and T. Ohtsuka, Receding Horizon Control
for Spatiotemporal Dynamic Systems, Mechanical Engineering Journal,
Vol. 3, No. 2, 15-00345, 2016.
[8] T, Shimizu and T. Hashimoto, Model Predictive Control with Unscented
Kalman Filter for Nonlinear Implicit Systems, International Journal of
Mathematical and Computational Sciences, Vol. 12, No. 7, pp. 147-151,
2018.
[9] H.W. Sorenson, Ed., Kalman Filtering: Theory and Application,
Piscataway, NJ: IEEE, 1985.
[10] S. Julier, J. Uhlmann and H.F. Durrant-Whyte, A New Method for
the Nonlinear Transformation of Means and Covariances in Filters and
Estimators, IEEE Transactions on Automatic Control, Vol. 45, 2000, pp.
477-482.
[11] H.B. Pacejka, A New Tire Model with an Application in Vehicle
Dynamics Studies, SAE Technical Paper, 890087, 1989.