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The purpose of this paper is to improve autonomous flight performance of an unmanned aerial vehicle (UAV) having actively sweep angle morphing wing using simultaneous UAV and flight control system (FCS) design.

An UAV is remanufactured in the ISTE Unmanned Aerial Vehicle Laboratory. Its wing sweep angle can vary actively during flight. FCS parameters and wing sweep angle are simultaneously designed to optimize autonomous flight performance index using a stochastic optimization method called as simultaneous perturbation stochastic approximation (SPSA). Results obtained are applied for flight simulations.

Using simultaneous design process of an UAV having actively sweep angle morphing wing and FCS design, autonomous flight performance index is maximized.

Authorization of Directorate General of Civil Aviation in Turkey is crucial for real-time UAV flights.

Simultaneous UAV having actively sweep angle morphing wing and FCS design process is so beneficial for recovering UAV autonomous flight performance index.

Simultaneous UAV having actively sweep angle morphing wing and FCS design process achieves confidence, high autonomous performance index and simple service demands of UAV operators.

Composing a novel approach to improve autonomous flight performance index (e.g. less settling and rise time, less overshoot meanwhile trajectory tracking) of an UAV and creating an original procedure carrying out simultaneous UAV having actively sweep angle morphing wing and FCS design idea.

This paper aims to offer a simultaneous design approach for helicopter having swept anhedral blade tip shape and helicopter flight control system (HFCS) to minimize controller cost.

By considering previously stated offer, control-oriented models and a stochastic optimization method are applied to minimize controller cost of the HFCS.

Using simultaneous design approach for helicopters having blade tip swept and blade tip anhedral causes considerably less control effort than the helicopters not benefiting this related design approach.

Simultaneous design approach for helicopters having blade tip swept and blade tip anhedral is applicable to consider fuel economy.

One important novelty of this paper is using simultaneous approach for determining optimum shape of blade tip swept and anhedral. Another considerable novelty of this paper is also using a stochastic optimization method called simultaneous perturbation stochastic approximation for previously mentioned purpose. In this paper, it is also reached that using simultaneous design approach for swept anhedral helicopter blade tip shape and HFCS causes less control effort than the helicopters not using this approach. This leads to less fuel consumption and green environment.

The purpose of this research paper is to recover the autonomous flight performance of a mini unmanned aerial vehicle (UAV) via stochastically optimizing the wing over certain parameters (i.e. wing taper ratio and wing aspect ratio) while there are lower and upper constraints on these redesign parameters.

A mini UAV is produced in the Iskenderun Technical University (ISTE) Unmanned Aerial Vehicle Laboratory. Its complete wing can vary passively before the flight with respect to the result of the stochastic redesign of the wing while maximizing autonomous flight performance. Flight control system (FCS) parameters (i.e. gains of longitudinal and lateral proportional-integral-derivative controllers) and wing redesign parameters mentioned before are simultaneously designed to maximize autonomous flight performance index using a certain stochastic optimization strategy named as simultaneous perturbation stochastic approximation (SPSA). Found results are used while composing UAV flight simulations.

Using stochastic redesign of mini UAV and simultaneously designing mini ISTE UAV over previously mentioned wing parameters and FCS, it obtained a maximum UAV autonomous flight performance.

Permission of the directorate general of civil aviation in the Republic of Türkiye is essential for real-time UAV autonomous flights.

Stochastic redesign of mini UAV and simultaneously designing mini ISTE UAV wing parameters and FCS approach is very useful for improving any mini UAV autonomous flight performance cost index.

Stochastic redesign of mini UAV and simultaneously designing mini ISTE UAV wing parameters and FCS approach succeeds confidence, highly improved autonomous flight performance cost index and easy service demands of mini UAV operators.

Creating a new approach to recover autonomous flight performance cost index (e.g. satisfying less settling time and less rise time, less overshoot during flight trajectory tracking) of a mini UAV and composing a novel procedure performing simultaneous mini UAV having passively morphing wing over certain parameters while there are upper and lower constraints and FCS design idea.

The purpose of this paper is to increase flight performance of small unmanned aerial vehicle (UAV) using simultaneous UAV and autopilot system design.

A small UAV is manufactured in Erciyes University, College of Aviation, Model Aircraft Laboratory. Its wing and tail is able to move forward and backward in the nose-to-tail direction in prescribed interval. Autopilot parameters and assembly position of wing and tail to fuselage are simultaneously designed to maximize flight performance using a stochastic optimization method. Results are obtained are used for simulations.

Using simultaneous UAV and autopilot system design idea, flight performance is maximized.

Permission of Directorate General of Civil Aviation in Turkey is required for testing UAVs in long range.

Simultaneous design idea is very beneficial for improving UAV flight performance.

Creating a novel method to improve flight performance of UAV and developing an algorithm performing simultaneous design idea.

In reservoir history matching the least square objective function is usually used to minimize the mismatch between the predicted production data and the observations. However, as history matching is an ill-posed inverse problem with non-unique solutions, the reservoir model after calibrating may be far from the real geology model by only matching the production data. In order to solve this problem, a regularization method for reservoir history matching is implemented, in which not only the production data is matched, but prior geological information is also used to correct and update the current reservoir model so that the updated model will be consistent with the geologic model. In this paper, the simultaneous perturbation stochastic approximation method (SPSA) coupled with fast streamline simulation provides an effective method (SLSPSA) to optimize the objective function. As a stochastic approximation algorithm, SLSPSA can guarantee the convergence of the algorithm. Compared to the gradient-based algorithms, it avoids the massive calculation and storage for adjoint or sensitivity matrix. In the calculation process of algorithm, parallel computing is implemented, which reduces the simulation time and improves the computational efficiency. The method was verified by matching an example test.

The purpose of this paper is to rise the autonomous flight performance of the small unmanned aerial vehicle (UAV) using simultaneous tailplane of UAV and autopilot system design.

A small UAV is remanufactured in the UAV laboratory. Its tailplane can be changed before the flight. Autopilot parameters and some parameters of tailplane are instantaneously designed to maximize autonomous flight performance using a stochastic optimization method. Results found are applied for simulations.

Benefitting simultaneous tailplane of UAV and autopilot system design process, autonomous flight performance is maximized.

Authorization of Directorate General of Civil Aviation in Turkey is required for UAV flights.

Simultaneous tailplane and autopilot system design process is so useful for refining UAV autonomous flight performance.

Simultaneous tailplane and autopilot system design process fulfills confidence, high autonomous performance, and easy service demands of UAV users. By that way, UAV users will be able to use better UAVs.

Creating a novel technique to recover autonomous flight performance (e.g. less overshoot, less settling time and less rise time during trajectory tracking) of UAV and developing a novel procedure performing simultaneous tailplane of UAV and autopilot system design idea.

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).

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.

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.

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.

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.

Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability.

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.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

The purpose of this paper is to design a quadrotor with collective morphing using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm.

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.

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.

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.

Optimization with SPSA is very useful in determining the amount of morphing and PID coefficients for quadrotors.

SPSA optimization method is useful in terms of cost, time and practicality.

It is released to improve performance with morphing, to determine morphing rate with SPSA algorithm and to determine PID coefficients accordingly.

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.

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.

With SPSA, the best proportional integrator and derivative (PID) coefficients and battery weight, energy efficiency and endurance were obtained in case of morphing.

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.

It is very useful to determine quadrotor endurance, PID coefficients and morphing rate using the optimization method.

Determining quadrotor endurance, PID coefficients and morphing rate using the optimization method provides advantages in terms of time, cost and practicality.

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.