Farkli Uçuş Durumlari Için Quadcopter Dinamik Modeli ve Simulasyonu

Oktay, Tugrul; Köse, Oğuz

doi:10.31590/ejosat.507222

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…During longitudinal, lateral and hover flight, satiation function on the control surface obeyed successfully. During longitudinal, lateral and hover flight, other state variables did not demonstrate catastrophic behavior [10]. The PID algorithm was used to control the istecopter.…”

Section: Discussionmentioning

confidence: 99%

Designing, Dynamic Modeling and Simulation of ISTECOPTER

Çoban

Bilgiç²,

Oktay³

2019

Journal of Aviation

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In this study, ISTECOPTER was modeled, performance evaluations were made and control system was designed. ISTECOPTER is a four-rotor Unmanned Aerial Vehicle (UAV) consisting of the abbreviation of Iskenderun Technical University and the combination of the words copter. For the control system, lateral, longitudinal and vertical takeoff of the UAV (Unmanned Aerial Vehicle) and the separate mathematical model of the descent operations were extracted and expressed as the state space model. The mathematical model of the wind distortions that will affect the aircraft during the flight was created and the situation was added to the space model. Proportional Integral Derivative (PID) control algorithm was used as a control. The unmanned aircraft was modeled in Solidworks and the simulations in Matlab / Simulink program. the İSTECOPTER is planned to have 4 brushless DC motors and has a weight of approximately 1.5 kg without load and 1 kg of payload is planned.

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Section: Discussionmentioning

confidence: 99%

Designing, Dynamic Modeling and Simulation of ISTECOPTER

Çoban

Bilgiç²,

Oktay³

2019

Journal of Aviation

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“…In particular, studies have been carried out on the morphing state of the quadrotor, which belongs to the family of multirotor UAVs. Oktay and Kose (2019b, 2019c, 2019a worked on quadrotor collective morphing and control. In their studies, quadrotor control was performed in longitudinal, lateral and vertical flight according to the morphing status with PID control.…”

Section: Hexarotor Modeling and Morphingmentioning

confidence: 99%

Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA

Köse

Oktay²

2023

AEAT

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Purpose The purpose of this paper is to optimize the simultaneous flight performance of a hexarotor unmanned aerial vehicle (UAV) by using simultaneous perturbation stochastic approximation (i.e. SPSA), deep neural network and proportional integral derivative (i.e. PID) according to varying arm length (i.e. morphing). Design/methodology/approach In this paper, proper PID gain coefficients and morphing ratio were obtained using the stochastic optimization method, also known as SPSA to maximize flight efficiency. Because it is difficult to establish an analytical connection between the morphing ratio and hexarotor moments of inertia, the deep neural network was used to obtain the moments of inertia according to the morphing ratio. By using SPSA and deep neural network, the best performance indexes were obtained and both longitudinal and lateral flight simulations were performed with the obtained data. Findings With SPSA, the best PID coefficients and morphing ratio are obtained for both longitudinal and lateral flight. Because the hexarotor solid body model changes according to the morphing ratio, the moment of inertia values used in the simulations also change. According to the morphing ratio, the moment of inertia values was obtained with the deep neural network over a created data set. Research limitations/implications It takes a long time to obtain the morphing ratio suitable for the hexarotor model and the PID gain coefficients suitable for this morphing ratio. However, this situation can be overcome with the proposed SPSA. In addition, it takes a long time to obtain the appropriate moments of inertia according to the morphing ratio. However, in this case, it was overcome using the deep neural network. Practical implications Determining the morphing ratio and PID gain coefficients using the optimization method, as well as determining the moments of inertia using the deep neural network, is very useful as it can increase the efficiency of hexarotor flight and flight efficiently with different arm lengths. With the proposed method, the hexarotor design performance criteria (i.e. rise time, settling time and overshoot) values were significantly improved compared to similar studies. Social implications Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability. Originality/value The hexarotor flight efficiency is improved with the proposed SPSA and deep neural network approaches. In addition, the desired flight parameters can be obtained more quickly and reliably with the proposed approaches. The design performance criteria were also improved, enabling the hexarotor UAV to follow the given trajectory in the best way and providing convenience for end users. SPSA was preferred because it converged faster than other methods. While other methods perform 2n operations per iteration, SPSA only performs two operations. To obtain the moment of inertia, many physical parameter values of the UAV are required in the existing methods. In the proposed method, by creating a date set, only arm length and moment of inertia were estimated without the need to obtain physical parameters with the deep neural network structure.

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“…In order to be fed into the PID controller, these outputs have to be converted into crisp or numerical numbers. For Mamdani-type Fuzzy controllers, the most common defuzzification method is the gravity center method, and the specific calculation is defined in equation (14).…”

Section: Fuzzy Controllermentioning

confidence: 99%

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Baharuddin,

Basri

2023

IJRCS

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Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

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Farkli Uçuş Durumlari Için Quadcopter Dinamik Modeli ve Simulasyonu

Cited by 14 publications

References 22 publications

Designing, Dynamic Modeling and Simulation of ISTECOPTER

Designing, Dynamic Modeling and Simulation of ISTECOPTER

Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

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