Economic_Model_Predictive_Control_for_Aircraft_Forced_Landing_Framework_and_Two-level_Implementation.pdf (5.98 MB)
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Economic model predictive control for aircraft forced landing: Framework and two-level implementation

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This paper presents an optimization-based control framework for the autonomous forced landing of a fixed-wing Unmanned Aircraft (UA). A two-level MPC scheme is proposed to realize this framework, where an EMPC in a long piece-wise constant fashion is proposed at the high-level while a short fixedhorizon linear time-varying MPC at the low-level responds to fast dynamics of UA and tracks the optimal path provided by the high-level controller, alleviating computational burden compared to the high frequency single-layer MPC scheme. Comparing with single EMPC setup with high sampling frequency, this hierarchical EMPC controller can significantly reduce the computational complexity and make it feasible to be implemented in realtime. In addition, it also responds to disturbances (e.g. wind) and aircraft fast dynamics in a timely manner. The recursive feasibility and stability of the high and low-level MPC schemes are established. The performance of the proposed EMPC forced landing function is illustrated by simulation case studies on both Aerosonde and Skywalker X8, compared favorably with competing schemes.

Funding

Goal-Oriented Control Systems (GOCS): Disturbance, Uncertainty and Constraints

Engineering and Physical Sciences Research Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Aerospace and Electronic Systems

Pages

1 - 1

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Publication date

2021-10-04

ISSN

0018-9251

eISSN

1557-9603

Language

en

Depositor

Deposit date: 23 November 2021