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- MakaleYalnız künye2022Scopus SJRQ3Simultaneous 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.
AtıfWoS40OpenAlex43
İncele - MakaleAçık Erişim2021Scopus SJRQ4Quadrotor Flight System Design using Collective and Differential Morphing with SPSA and ANN
Kose, Oguz; Oktay, Tugrul
Quadrotor modeling has been done with collective and differential morphing. Quadrotor initial state and morphing states are drawn in the Solidworks program. Newton-Euler approximation was used for quadrotor modeling. The mass and moment of inertia values required for modeling and simulation were obtained from the Solidworks program. Matlab / Simulink environment and state-space model approaches are used for simulations. A simultaneous perturbation stochastic approximation (SPSA) algorithm was used to determine the quadrotor morphing rates. If the morphing state obtained by SPSA is not included in the values obtained from the drawings, here it is provided to find the moments of inertia with the method based on learning by using the data obtained with the Artificial Neural Network(ANN). Proportional Integral Derivative (PID) is used as the quadrotor control algorithm. PID coefficients are also determined by SPSA.
AtıfScopus13OpenAlex13
İncele - MakaleYalnız künye2020Scopus SJRQ3Simultaneous quadrotor autopilot system and collective morphing system design
Kose, Oguz; Oktay, Tugrul
Purpose: The purpose of this paper is to design a quadrotor with collective morphing using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm. Design/methodology/approach: Quadrotor design is made by using Solidworks drawing program and some mathematical performance relations. Modelling and simulation are performed in Matlab/Simulink program by using the state space model approaches with the parameters mostly taken from Solidworks. Proportional integral derivative (PID) approach is used as control technique. Morphing amount and the best PID coefficients are determined by using SPSA algorithm. Findings: By using SPSA algorithm, the amount of morphing and the best PID coefficients are determined, and the quadrotor longitudinal and lateral flights are made most stable via morphing. Research limitations/implications: It takes quite a long time to model the quadrotor in Solidworks and Matlab/Simulink with the state space model and using the SPSA algorithm. However, this situation is overcome with the proposed model. Practical implications: Optimization with SPSA is very useful in determining the amount of morphing and PID coefficients for quadrotors. Social implications: SPSA optimization method is useful in terms of cost, time and practicality. Originality/value: It is released to improve performance with morphing, to determine morphing rate with SPSA algorithm and to determine PID coefficients accordingly.
AtıfScopus67WoS64OpenAlex70İncele