Yaw, Pitch and Roll controller design for fixed-wing UAV under uncertainty and perturbed condition

Warsi, Faizan A.; Hazry, D.; Ahmed, Syed Faiz; Joyo, M. Kamran; Tanveer, M. Hassan; Kamarudin, H.; Razlan, Z. M.

doi:10.1109/cspa.2014.6805738

Cited by 25 publications

(13 citation statements)

References 4 publications

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“…The two sets of rotors rotate in the opposite direction to counteract the anti-torsion moment to maintain the attitude stability. The total lift in the vertical direction is generated by four rotors [20], and the rotational speed difference of all the rotors produces the torque of horizontal direction to cause a yawing motion; the difference in rotational speed between the front and rear rotors controls the pitching motion; the left and right rotors controls the rolling motion [21]. The lift force are expressed as follows:…”

Section: Four-rotor Uav Modelmentioning

confidence: 99%

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Xiong

et al. 2019

Electronics

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The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

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Section: Four-rotor Uav Modelmentioning

confidence: 99%

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Xiong

et al. 2019

Electronics

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“…PID is the widely used compensator for controlling the stability of quadrotor [2], and also PID is commonly employed in fixed-wing UAV [3]. In this research, PID control is utilized to control roll and pitch angles of fixed-wing UAV.…”

Section: Flight Testmentioning

confidence: 99%

Design of a Low-Cost Fixed Wing UAV

Ariyanto

Setiawan

Prabowo

et al. 2018

MATEC Web of Conferences

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This research will try to design a low cost of fixed-wing unmanned aerial vehicle (UAV) using low-cost material that able to fly autonomously. Six parameters of UAV’s structure will be optimized based on basic airframe configuration, wing configuration, straight wing, tail configuration, fuselage material, and propeller location. The resulted and manufactured prototype of fixed-wing UAV will be tested in autonomous fight tests. Based on the flight test, the developed UAV can successfully fly autonomously following the trajectory command. The result shows that low-cost material can be used as a body part of fixed-wing UAV.

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“…To the authors' best knowledge, although the heading control methods for fixed-wing UAVs have been reported in many applications (Warsi et al 2014;Beard et al 2014;Ren and Beard 2004;Luo et al 2011;Cabecinhas et al 2007;Mystkowski 2013;Poksawat et al 2016;Cai et al 2008;Guo and Liu 2012), this is the first time the MFFAC has been applied to fixed-wing UAVs.…”

Section: Introductionmentioning

confidence: 98%

“…Unmanned aerial vehicles (UAVs) have been widely used in both civilian and military missions. A variety of control algorithms have been developed to ensure that the UAV moves smoothly and steadily (Warsi et al 2014;Chao et al 2007Chao et al , 2010Wang et al 2016a). Fixed-wing vehicles, each equipped with a propeller and a set of control surfaces (elevator, ailerons, and rudder), have been employed in a variety of cases (Sujit et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Model-Free Fuzzy Adaptive Control of the Heading Angle of Fixed-Wing Unmanned Aerial Vehicles

2017

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In this paper, a novel model-free fuzzy adaptive control (MFFAC) scheme is developed to control the heading angle of fixed-wing unmanned aerial vehicles (UAVs). It is common knowledge that the aerodynamics of the heading angle of fixed-wing UAVs are difficult to accurately model and are subject to wind disturbances. Therefore, it is difficult to implement conventional model-based control to the heading angle control problem. To overcome this difficulty, the authors propose a novel data-driven control approach. First, an adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the pseudo partial derivative (PPD), which is described as an equivalent dynamic linearization (EDL) technique for unknown nonlinear systems. Secondly, an extended model-free adaptive control (MFAC) strategy is proposed to control the heading angle of fixed-wing UAVs with wind disturbances. Finally, a discrete Lyapunov-based stability analysis is presented to prove the globally asymptotic stability of the proposed control scheme. The high-fidelity semiphysical simulations illustrate that accurate and stable control is achieved in this designed control strategy.

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Yaw, Pitch and Roll controller design for fixed-wing UAV under uncertainty and perturbed condition

Cited by 25 publications

References 4 publications

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Design of a Low-Cost Fixed Wing UAV

Model-Free Fuzzy Adaptive Control of the Heading Angle of Fixed-Wing Unmanned Aerial Vehicles

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