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

Authors: Wataru Nakamura, Tomoaki Hashimoto, Liang-Kuang Chen


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.

Keywords: State estimation, control systems, observer systems, unscented Kalman filter, nonlinear vehicle dynamics.

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[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.