Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2025Scopus SJRQ2/r/gumushane/16482
  Design of a fractional-order neural network-based fixed-time sliding mode controller for chaotic satellite attitude control and synchronization

  Bingöl, Ozhan

  PurposeThis study aims to propose a novel fractional-order neural network-based fixed-time sliding mode controller to address the challenges of chaotic satellite attitude control, synchronization and anti-synchronization. By integrating fractional-order dynamics and neural network-based estimation, the proposed approach enhances robustness and adaptability in stabilizing complex systems.Design/methodology/approachThe proposed control scheme introduces a novel sliding surface that leverages fractional calculus to improve convergence speed and control flexibility. A neural network is used to adaptively estimate unknown error dynamics, compensating for uncertainties in the system. The fixed-time stability property guarantees convergence within a predefined time, independent of initial conditions. Theoretical stability proofs and numerical simulations are conducted to validate the effectiveness of the proposed controller.FindingsThe results demonstrate that the proposed controller effectively stabilizes chaotic satellite attitude dynamics and ensures reliable synchronization and anti-synchronization. The approach exhibits strong robustness against system uncertainties and external disturbances while achieving fixed-time convergence.Originality/valueThis work advances the field by integrating fractional-order dynamics with a neural network-based adaptive mechanism in a fixed-time sliding mode control framework. The proposed method enhances convergence properties and robustness in controlling chaotic satellite attitude systems, providing a novel and effective solution for complex nonlinear dynamics.

  AtıfWoS4·OpenAlex3

  İncele
• MakaleYalnız künye2023Scopus SJRQ2/r/gumushane/16052
  Simultaneous 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.

  AtıfWoS48·OpenAlex47

  İncele
• MakaleYalnız künye2022Scopus SJRQ3/r/gumushane/16174
  Simultaneous 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.

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  AtıfWoS40·OpenAlex43

  İncele
• MakaleYalnız künye2020Scopus SJRQ3/r/gumushane/13015
  Simultaneous 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.

  AtıfScopus67·WoS64·OpenAlex70

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