Complementary Filter-Based Incremental Nonlinear Model Following Control Design for a Tilt-Wing UAV

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Johannes Autenrieb, Hyo-Sang Shin
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Abstract

This article presents an incremental nonlinear model following control (INMFC) strategy for a tilt-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To ensure a good and robust regulation performance, a two-loop feedback controller, based on the incremental backstepping (IBS) methodology, is used to handle uncertainties and external disturbances robustly. In order to ensure a good mode following and to aid the tracking performance of the flight control system, the first and second-time derivatives of the desired model response, computed by a model reference (MR) system, are either directly used as feedforward signals for the outer loop or are additionally transformed for the inner-loop by using an incremental nonlinear dynamic inversion (INDI) grounded approach to transform the signals based on known systems kinematics. In order to uniformly handle the overactuated flight vehicle in both mission model and during the transition, an operation mode-based weighted incremental linear control allocation approach is applied for safe operation. The performance of the suggested control approach is investigated by utilizing a high-fidelity nonlinear flight dynamics model of the tilt-wing system. The results presented in this paper demonstrate that the proposed control approach provides significant benefits for the robust control of the considered tilt-wing UAV.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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