Road-map assisted standoff tracking of moving ground vehicle using nonlinear model predictive control
journal contributionposted on 2014-11-13, 14:08 authored by Hd Oh, Seungkeun Kim, Antonios Tsourdos
This paper presents road-map–assisted standoff tracking of a ground vehicle using nonlinear model predictive control. In model predictive control, since the prediction of target movement plays an important role in tracking performance, this paper focuses on utilizing road-map information to enhance the estimation accuracy. For this, a practical road approximation algorithm is first proposed using constant curvature segments, and then nonlinear road-constrained Kalman filtering is followed. To address nonlinearity from road constraints and provide good estimation performance, both an extended Kalman filter and unscented Kalman filter are implemented along with the state-vector fusion technique for cooperative unmanned aerial vehicles. Lastly, nonlinear model predictive control standoff tracking guidance is given. To verify the feasibility and benefits of the proposed approach, numerical simulations are performed using realistic car trajectory data in city traffic.
This study was supported by 1) the UK Engineering and Physical Science Research Council (EPSRC) under the Grant EP/J011525/1 and 2) “Guidance/Control Study for Take-off and Landing on a Ship” program through the Agency for Defense Development (ADD) of KOREA (UD1130053JD).
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Published inIEEE Transactions on Aerospace and Electronic Systems
Pages0 - 0 (0)
CitationOH, H., KIM, A. and TSOURDOS, A., 2015. Road-map assisted standoff tracking of moving ground vehicle using nonlinear model predictive control. IEEE Transactions on Aerospace and Electronic Systems, 15 (2), pp.975-986.
- VoR (Version of Record)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/
NotesThis paper was published by IEEE as Open Access.