Simultaneous small UAV and autopilot system design

Oktay, Tugrul; Konar, Mehmet; Onay, Murat; Aydin, Murat; Mohamed, Mohamed Abdallah

doi:10.1108/aeat-04-2015-0097

Cited by 36 publications

(34 citation statements)

References 20 publications

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“…The acceleration and angular velocity measurement undergo a two-layer function relationship between the voltage signal, displacement of the measuring element, actual acceleration, and Coriolis acceleration, as shown in Table 4. A triaxial magnetometer is composed of three monoaxial magnetic fields mounted orthogonally, in which combining the projection of geomagnetic direction on the body coordinate system gives the heading information [18]. The height and airspeed can be measured by static and dynamic pressure signals.…”

Section: Sensorsmentioning

confidence: 99%

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

et al. 2020

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This paper describes a low-cost flight control system of a small aileron-less hand-launched solar-powered unmanned aerial vehicle (UAV). In order to improve the accuracy of the whole system model and quantify the influence of each subsystem, detailed modeling of UAV energy and a control system including a solar model, engine, energy storage, sensors, state estimation, control law, and actuator module are established in accordance with the experiment and component principles. A whole system numerical simulation combined with the 6 degree-of-freedom (DOF) simulation model is constructed based on the typical mission route, and the parameter precision sequence and energy balance are obtained. Then, a hardware-in-the-loop (HIL) experiment scheme based on the Stewart platform (SP) is proposed, and three modes of acceleration, angular velocity, and attitude are designed to verify the control system through the inner and boundary states of the flight envelope. The whole system scheme is verified by flight tests at different altitudes, and the aerodynamic force coefficient and sensor error are corrected by flight data. With the increase of altitude, the cruise power increases from 47 W to 78 W, the trajectory tracking precision increases from 23 m to 44 m, the sensor measurement noise increases, and the bias decreases.

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Section: Sensorsmentioning

confidence: 99%

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

et al. 2020

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“…Stability and control derivatives define how force and moment aspects on an UAV alter when other stability parameters change for a reference flight condition. By assuming rigid body dynamics, and after using geometrical data of our camera mounted UAV, 17 Using equation 2, flight dynamic modes with that dynamic stability of our UAV can be validated are obtained. These results were validated using the existing data in related literature.…”

Section: Uav Dynamicsmentioning

confidence: 99%

Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images

Oktay

Çelik

Turkmen

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this study, reducing motion blur in images taken by our unmanned aerial vehicle is investigated. Since shakes of unmanned aerial vehicle cause motion blur in taken images, autonomous performance of our unmanned aerial vehicle is maximized to prevent it from shakes. In order to maximize autonomous performance of unmanned aerial vehicle (i.e. to reduce motion blur), initially, camera mounted unmanned aerial vehicle dynamics are obtained. Then, optimum location of unmanned aerial vehicle camera is estimated by considering unmanned aerial vehicle dynamics and autopilot parameters. After improving unmanned aerial vehicle by optimum camera location, dynamics and controller parameters, it is called as improved autonomous controlled unmanned aerial vehicle. Also, unmanned aerial vehicle with camera fixed at the closest point to center of gravity is called as standard autonomous controlled unmanned aerial vehicle. Both improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles are performed in real time flights, and approximately same trajectories are tracked. In order to compare performance of improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles in reducing motion blur, a motion blur kernel model which is derived using recorded roll, pitch and yaw angles of unmanned aerial vehicle is improved. Finally, taken images are simulated to examine effect of unmanned aerial vehicle shakes. In comparison with standard autonomous controlled flight, important improvements on reducing motion blur are demonstrated by improved autonomous controlled unmanned aerial vehicle.

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“…simultaneous perturbation stochastic approximation) is applied. It was successfully benefited in similar highly-complex, constrained optimization problems before [14], [18]. SPSA has many advantages with respect to the other present methods in the literature.…”

Section: Optimization and Simultaneous Designmentioning

confidence: 99%

“…First of all, it is low cost since it applies only two evaluations of the objective in order to estimate the gradient [19]. In addition to previous advantage, it is also effective in solving constrained optimization problems [14]- [18].…”

Section: Optimization and Simultaneous Designmentioning

confidence: 99%

Simultaneous Longitudinal and Lateral Flight Control Systems Design for Both Passive and Active Morphing TUAVs

Oktay¹,

Çoban²

2017

ElAEE

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In this article, simultaneous longitudinal and lateral flight control systems design for both passive and active morphing tactical unmanned aerial vehicles (TUAVs) is first time applied for autonomous flight performance maximization in the literature. For this purpose longitudinal and lateral dynamics modelling of TUAVs produced in Erciyes University, Faculty of Aeronautics and Astronautics, Model Aircraft Laboratory are considered in order to obtain simulation environments. Our produced TUAV is called as ZANKA-III which has weight of 50 kg, range of around 3000 km, endurance of around 28 hour, and ceiling altitude of around 12500 m. Von-Karman turbulence modelling is used in order to model atmospheric turbulence during flight in both longitudinal and lateral simulation environments. A stochastic optimization method called as simultaneous perturbation stochastic approximation (i.e. SPSA) is also first time applied in order to obtain optimum dimensions of morphing parameters (i.e. extension ratios of wingspan and tailspan, assembly positions of wing and tailplane to fuselage) and optimum magnitudes of longitudinal and lateral controllers' gains (i.e. P, I and D gains) while minimizing cost index capturing terms about both longitudinal and lateral autonomous flight performances and there exists lower and upper constraints on all optimization variables in the literature.

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Simultaneous small UAV and autopilot system design

Cited by 36 publications

References 20 publications

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images

Simultaneous Longitudinal and Lateral Flight Control Systems Design for Both Passive and Active Morphing TUAVs

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