JRM Vol.35 No.2 pp. 298-307
doi: 10.20965/jrm.2023.p0298


Continuous-Time Receding-Horizon Estimation via Primal-Dual Dynamics on Vehicle Path-Following Control

Kaito Sato and Kenji Sawada ORCID Icon

The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

October 18, 2022
January 12, 2023
April 20, 2023
vehicle, receding-horizon estimation, primal-dual dynamics, convex optimization, path-following control

In vehicle control, state estimation is essential even as the sensor accuracy improves with technological development. One of the vehicle estimation methods is receding-horizon estimation (RHE), which uses a past series of the measured state and input of the plant, and determines the estimated states based on linear or quadratic programming. It is known that RHE can estimate the vehicular state to which the extended Kalman filter cannot be applied owing to modeling errors. This study proposes a new computational form of the RHE based on primal-dual dynamics. The proposed form is expressed by a dynamic system; therefore, we can consider the computational stability based on the dynamic system theory. In this study, we propose a continuous-time representation of the RHE algorithm and redundant filters to improve the convergence performance of the estimation and demonstrate its effectiveness through a vehicle path-following control problem.

Configuration of continuous-time RHE

Configuration of continuous-time RHE

Cite this article as:
K. Sato and K. Sawada, “Continuous-Time Receding-Horizon Estimation via Primal-Dual Dynamics on Vehicle Path-Following Control,” J. Robot. Mechatron., Vol.35 No.2, pp. 298-307, 2023.
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