Structural state feedback gain-scheduled tracking control based on linear parameter varying system of morphing wing UAV

Stabilization and control in the morphing process is critical for the safety of morphing wing unmanned aerial vehicles (MWUAVs). In this paper, a Linear Parameter Varying (LPV)-based structural state feedback gain-scheduled control design method is proposed and applied to longitudinal tracking control of a bio-inspired MWUAVs in the morphing process. Firstly, the LPV model for longitudinal dynamics of MWUAVs is developed, after that the flight envelopes and dynamical stability are analyzed based on the model, in which an unstable region is found. According to the analysis results, the mission profile is designed, and the tracking control problem is formulated as an optimization problem with input constraints. Then, a structural matrix is proposed which makes the states to feedback can be specified according to the signals to be tracked. Compared to the traditional all-state feedback control method, the proposed method has a simpler structure and is more applicable in real-world applications, especially when all states are not fully available. To avoid the numerical problems in the Bilinear Matrix Inequalities(BMIs) which are arisen from the solving process of the proposed control design method, we propose a solution approach that combines Linear Matrix Inequalities (LMIs) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for control design based on the structural matrix. Finally, we apply the proposed method to the MWUAVs for its longitudinal tracking controller design and comparative simulation with existing methods are performed. The simulation results verify the effectiveness of the proposed method and its advantages over existing methods.

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Publication Details

Journal
Journal of the Franklin Institute
Published
2024-11-07
DOI
https://doi.org/10.1016/j.jfranklin.2024.107355
Citations
1
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
0.33

Funders

Controls
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article

Structural state feedback gain-scheduled tracking control based on linear parameter varying system of morphing wing UAV

Gaomin Qu, Chengfu Wu, Bing Yan, Pengyuan Shao et al.
1 citations
Journal of the Franklin Institute
Adaptive Control of Nonlinear Systems
0.33
article

Structural state feedback gain-scheduled tracking control based on linear parameter varying system of morphing wing UAV

Gaomin Qu, Chengfu Wu, Bing Yan, Pengyuan Shao, Jian Tan, Yanfei Dong, Xiao Ma
article en
1 citations

Abstract

Stabilization and control in the morphing process is critical for the safety of morphing wing unmanned aerial vehicles (MWUAVs). In this paper, a Linear Parameter Varying (LPV)-based structural state feedback gain-scheduled control design method is proposed and applied to longitudinal tracking control of a bio-inspired MWUAVs in the morphing process. Firstly, the LPV model for longitudinal dynamics of MWUAVs is developed, after that the flight envelopes and dynamical stability are analyzed based on the model, in which an unstable region is found. According to the analysis results, the mission profile is designed, and the tracking control problem is formulated as an optimization problem with input constraints. Then, a structural matrix is proposed which makes the states to feedback can be specified according to the signals to be tracked. Compared to the traditional all-state feedback control method, the proposed method has a simpler structure and is more applicable in real-world applications, especially when all states are not fully available. To avoid the numerical problems in the Bilinear Matrix Inequalities(BMIs) which are arisen from the solving process of the proposed control design method, we propose a solution approach that combines Linear Matrix Inequalities (LMIs) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for control design based on the structural matrix. Finally, we apply the proposed method to the MWUAVs for its longitudinal tracking controller design and comparative simulation with existing methods are performed. The simulation results verify the effectiveness of the proposed method and its advantages over existing methods.

Journal of the Franklin InstituteVol. 362(1)
Northwestern Polytechnical University (CN), Xi'an Aeronautical University (CN), The University of Adelaide (AU)
Natural Science Foundation of Shaanxi Province, Education Department of Shaanxi Province
Affordable and clean energy
Openalex Percentile: Top 40%
Adaptive Control of Nonlinear Systems
0.33
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