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

In vehicle control, state estimation is essential even as the sensor accuracy improves with technological de-velopment. 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 er-rors. This study proposes a new computational form of the RHE based on primal-dual dynamics. The pro-posed form is expressed by a dynamic system; there-fore, we can consider the computational stability based on the dynamic system theory. In this study, we pro-pose a continuous-time representation of the RHE al-gorithm and redundant filters to improve the conver-gence performance of the estimation and demonstrate its effectiveness through a vehicle path-following con-trol problem.