Online Adaptive Helicopter Control Using Incremental Dual Heuristic Programming

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Abstract

Reinforcement learning is an appealing approach for adaptive, fault-tolerant flight control, but is generally plagued by its need for accurate system models and lengthy offline training phases. The novel Incremental Dual Heuristic Programming (IDHP) method removes these dependencies by using an online-identified local system model. A recent implementation has shown to be capable of reliably learning near-optimal control policies for a fixed-wing aircraft in cruise by using outer loop PID and inner-loop IDHP rate controllers. However, fixed wing aircraft are inherently stable, enabling a trade-off between learning speed and learning stability which is not trivially extended to a physically unstable system. This paper presents an implementation of IDHP for control of a non-linear, six-degree-of-freedom simulation of an MBB Bo-105 helicopter. The proposed system uses two separate IDHP controllers for direct pitch angle and altitude control combined with outer loop and lateral PID controllers. After a short online training phase, the agent is shown to be able to fly a modified ADS-33 acceleration-deceleration manoeuvre as well as a one-engine-inoperative continued landing with high success rates.
Original languageEnglish
Title of host publicationAIAA Scitech 2021 Forum
Subtitle of host publication11–15 & 19–21 January 2021, Virtual Event
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Pages1-18
Number of pages18
ISBN (Electronic)978-1-62410-609-5
DOIs
Publication statusPublished - 2021
EventAIAA Scitech 2021 Forum - Virtual/online event due to COVID-19
Duration: 11 Jan 202121 Jan 2021

Publication series

NameAIAA Scitech 2021 Forum

Conference

ConferenceAIAA Scitech 2021 Forum
Period11/01/2121/01/21

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