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- MakaleYalnız künye2023Scopus SJRQ2Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA
Kose, Oguz; Oktay, Tugrul
PurposeThe 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/approachIn 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. FindingsWith 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/implicationsIt 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 implicationsDetermining 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 implicationsDetermining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability. Originality/valueThe 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.
AtıfWoS48OpenAlex47İncele - MakaleYalnız künye2022Scopus SJRQ2Simultaneous autonomous system and powerplant design for morphing quadrotors
Sahin, Huseyin; Kose, Oguz; Oktay, Tugrul
Purpose This study aims to optimize autonomous performance (i.e. both longitudinal and lateral) and endurance of the quadrotor type aerial vehicle simultaneously depending on the autopilot gain coefficients and battery weight. Design/methodology/approach Quadrotor design processes are critical to performance. Unmanned aerial vehicle durability is an important performance parameter. One of the factors affecting durability is the battery. Battery weight, energy capacity and discharge rate are important design parameters of the battery. In this study, proper autopilot gain coefficients and battery weight are obtained by using a stochastic optimization method named as simultaneous perturbation stochastic approximation (SPSA). Because there is no direct correlation between battery weight and battery energy density, artificial neural network (ANN) is benefited to obtain battery energy density corresponding to resulted battery weight found from SPSA algorithm. By using the SPSA algorithm optimum performance index is obtained, then obtained data is used for longitudinal and lateral autonomous flight simulations. Findings With SPSA, the best proportional integrator and derivative (PID) coefficients and battery weight, energy efficiency and endurance were obtained in case of morphing. Research limitations/implications It takes a long time to find the most suitable battery values depending on quadrotor endurance. However, this situation can be overcome with the proposed SPSA. Practical implications It is very useful to determine quadrotor endurance, PID coefficients and morphing rate using the optimization method. Social implications Determining quadrotor endurance, PID coefficients and morphing rate using the optimization method provides advantages in terms of time, cost and practicality. Originality/value The proposed method improves quadrotor endurance. In addition, with the SPSA optimization method and ANN, the parameters required for endurance will be obtained faster and more securely. In addition, the energy density according to the battery weight also contributes to the clean environment and energy efficiency.
AtıfWoS40OpenAlex43İncele - MakaleYalnız künye2022Hexarotor Yaw Flight Control with SPSA, PID Algorithm and Morphing
Kose, Oguz; Oktay, Tugrul
In recent years, the interest in unmanned aerial vehicles (UAV) has increased. They have penetrated into all areas of life. However, studies on UAV control have been a frequently discussed topic by researchers. In this study, a hexarotor in which yaw flight is tried to be controlled by changing the arm lengths during flight (morphing) is discussed. The hexarotor mathematical model is linearly derived using Newton's equations of motion. The equations of motion are modeled using the state space model approach. Hexarotor has been drawn in Solidworks program in accordance with the reality of all morphing states. Morphing estimation and accordingly proportional-integral-derivative (PID) coefficients were estimated using Simultaneous Perturbation Stochastic Approximation (SPSA). SPSA was preferred because it converged to the optimum result faster than similar algorithms. Hexarotor simulations were performed in Matlab/Simulink environment. Hexarotor yaw flight stability was achieved by minimizing the SPSA generated cost function based on design performance criteria. The cost function converged in 3-4 iterations and approached the optimum result. Accordingly, the design performance criteria have also improved and successfully followed the given trajectory.
AtıfScopus24İncele - MakaleYalnız künye2019Scopus SJRQ3Combined active flow and flight control systems design for morphing unmanned aerial vehicles
Kanat, Öztürk Özdemir; Karatay, Ertuğrul; Köse, Oğuz; Oktay, Tuğrul
In this article, combined active flow control system and flight control system design for morphing unmanned aerial vehicles is applied for the first time for autonomous flight performance maximization. For this purpose, longitudinal and lateral dynamics modeling of morphing unmanned aerial vehicle having active flow control manufactured in Erciyes University, Faculty of Aeronautics and Astronautics, Model Aircraft Laboratory is considered in order to obtain simulation environments. Our produced morphing unmanned aerial vehicle is called as ZANKA-II, which has a mass of 6.5 kg, range of 30 km, endurance of 0.5 h, and ceiling altitude of 6000 m. von Karman turbulence modeling 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 is also applied for the first time in order to obtain optimum dimensions of morphing parameters (i.e. extension ratios of wingspan and tail span), optimum positions of blowers, and optimum magnitudes of longitudinal and lateral controllers' gains (i.e. P, I, and D gains) while minimizing cost index capturing terms for both longitudinal and lateral autonomous flight performances and there exist lower and upper constraints on all optimization variables in the literature.
AtıfScopus17WoS14OpenAlex15İncele