Adaptive Attitude Control for a Tail-Sitter UAV with Single Thrust-Vectored Propeller

Wang, Wufan; Zhu, Jihong; Kuang, Minchi; Zhu, Xufei

doi:10.1109/icra.2018.8463158

Cited by 9 publications

(11 citation statements)

References 5 publications

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“…Additionally, a variety of nonlinear control strategies based on Lyapunov's stability concepts have been designed to hybrid MAVs. 4,28 Links with the model-free control algorithm…”

Section: Literature Reviewmentioning

confidence: 99%

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Barth

Condomines

Bronz

et al. 2020

International Journal of Micro Air Vehicles

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Micro air vehicles with transitioning flight capabilities, or simply hybrid micro air vehicles, combine the beneficial features of fixed-wing configurations, in terms of endurance, with vertical takeoff and landing capabilities of rotorcrafts to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. This promising micro air vehicle class has a wider flight envelope than conventional micro air vehicles, which implies new challenges for both control community and aerodynamic designers. One of the major challenges of hybrid micro air vehicles is the fast variation of aerodynamic forces and moments during the transition flight phase which is difficult to model accurately. To overcome this problem, we propose a flight control architecture that estimates and counteracts in real-time these fast dynamics with an intelligent feedback controller. The proposed flight controller is designed to stabilize the hybrid micro air vehicle attitude as well as its velocity and position during all flight phases. By using model-free control algorithms, the proposed flight control architecture bypasses the need for a precise hybrid micro air vehicle model that is costly and time consuming to obtain. A comprehensive set of flight simulations covering the entire flight envelope of tailsitter micro air vehicles is presented. Finally, real-world flight tests were conducted to compare the model-free control performance to that of the Incremental Nonlinear Dynamic Inversion controller, which has been applied to a variety of aircraft providing effective flight performances.

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“…Additionally, a variety of nonlinear control strategies based on Lyapunov's stability concepts have been designed to hybrid MAVs. 4,28 Links with the model-free control algorithm…”

Section: Literature Reviewmentioning

confidence: 99%

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Barth

Condomines

Bronz

et al. 2020

International Journal of Micro Air Vehicles

View full text Add to dashboard Cite

show abstract

“…Both simulation and practical flight test results verify the effectiveness and reliability of the proposed method. Additionally, we give a comparison result between the proposed method and an adaptive method mentioned in [29] in order to reveal the advantage of the proposed method.…”

Section: Figurementioning

confidence: 99%

“…Additionally, to show the advantage of the proposed NNs control scheme, we conduct a comparison between the proposed method and the adaptive method for tail-sitter UAV proposed in [29]. The main differences between these two methods are concluded as follows:…”

Section: Simulation 3: Transition Under Disturbance and Noisesmentioning

confidence: 99%

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Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

Cheng

Pei

2020

IEEE Access

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In this paper, we focus on the transition control of a ducted fan vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in highfrequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course. INDEX TERMS Ducted fan, fast convergence, high-speed flight, neural networks, transition control, unmanned aerial vehicle (UAV).

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“…In [13], [14], [15], [16] it is implemented a controller in for the transition maneuver of a tail-sitter UAV, the controller is designed in the 6-DOF, on the other hand, however [17] lacks of a theoretical proof which demonstrate the effectiveness of the transition maneuver. Also, several unified controllers which avoids control switching methods are investigated in [18], [19] In this paper we present a mathematical UAV model which considers aerodynamic effects. The proposed controller takes into account the usual available states in a real scenario.…”

Section: Introductionmentioning

confidence: 99%

A Simple Controller for the Transition Maneuver of a Tail-Sitter Drone

Flores

2018

2018 IEEE Conference on Decision and Control (CDC)

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This paper presents a controller for the transition maneuver of a tail-sitter drone. The tail-sitter model considers aerodynamic terms whereas the proposed controller considers the time-scale separation between drone attitude and position dynamics. The controller design is based on Lyapunov approach and linear saturation functions. Simulations experiments demonstrate the effectiveness of the derived theoretical results.

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Adaptive Attitude Control for a Tail-Sitter UAV with Single Thrust-Vectored Propeller

Cited by 9 publications

References 5 publications

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

A Simple Controller for the Transition Maneuver of a Tail-Sitter Drone

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