Reinforcement Learning-based Control Allocation for the Innovative Control Effectors Aircraft

Pieter Simke de Vries, Erik-jan van Kampen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)
201 Downloads (Pure)


Established Control Allocation (CA) methods rely on knowledge of the control effectiveness for distributing control effector utilization for control of (overactuated) systems. The Innovative Control Effectors (ICE) aircraft model is highly overactuated with its 13 control effectors, CA is a preferred method to distribute control effector utilization. In this paper it is envisioned to use Reinforcement Learning (RL) for distributing control effector utilization, which requires no knowledge of the control effectiveness. RL allows to pursue more abstract and timescale separated objectives. The ICE aircraft’s altitude, considering only longitudinal motion, is controlled by distributing the control effector utilization using RL, from an initial offset, while pursuing secondary objectives such as decreasing effector utilization and thrust.
Original languageEnglish
Title of host publicationAIAA Scitech 2019 Forum
Subtitle of host publication7-11 January 2019, San Diego, California, USA
Number of pages22
ISBN (Electronic)978-1-62410-578-4
Publication statusPublished - 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: 7 Jan 201911 Jan 2019


ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego
Internet address


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