Purpose The purpose of this paper is to increase flight performance of small unmanned aerial vehicle (UAV) using simultaneous UAV and autopilot system design. Design/methodology/approach 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. Findings Using simultaneous UAV and autopilot system design idea, flight performance is maximized. Research limitations/implications Permission of Directorate General of Civil Aviation in Turkey is required for testing UAVs in long range. Practical implications Simultaneous design idea is very beneficial for improving UAV flight performance. Originality/value Creating a novel method to improve flight performance of UAV and developing an algorithm performing simultaneous design idea.
This paper is an extended version of the paper presented at TOK 2014 (Turkish Automatic Control National Meeting) which examined the determination of Sugeno type fuzzy model parameters optimized by the artificial bee colony (ABC) algorithm for a microstrip antenna. This paper presents a performance comparison of the Sugeno and Mamdani type fuzzy models proposed for nonlinear system modelling. To determine the parameters of the fuzzy models, the ABC algorithm is used. For this purpose, several nonlinear system examples which given in the literature were considered, and the results obtained by the optimized fuzzy models were compared with the other modelling approaches in the literature. Simulation results demonstrate that the use of the ABC algorithm provides a remarkable contribution to the model's performance.
This paper presents the results of the nonlinear system modelling approach based on the use of fuzzy rules optimized by different population based optimization algorithms. Fuzzy rule based models with different number of the rules are used to describe the some nonlinear systems in the literature. Firstly, parameters of the fuzzy models are determined by the artificial bee colony (ABC) algorithm. To demonstrate the efficiency of the ABC algorithm, its modelling ability is compared with the other two powerful population based algorithms, particle swarm optimization (PSO) and differential evolution algorithm (DEA). Simulation results show that a successful model performance with good description ability in the modelling of nonlinear or complex systems can be obtained by using one of the population based algorithms in design of the fuzzy rule based models.
Purpose The purpose of this paper is to present a novel approach based on the artificial bee colony (ABC) algorithm aiming to achieve maximum acceleration and maximum endurance for morphing unmanned aerial vehicle (UAV) design. Design/methodology/approach Some of the most important issues in the design of UAV are the design of thrust system and determination of the endurance of the UAV. Although propeller selection is very important for the thrust system design, battery selection has the utmost importance for the determination of UAV endurance. In this study, the calculations of maximum acceleration and endurance required by ZANKA-II during the flight are considered simultaneously. For this purpose, a model based on the ABC algorithm is proposed for the morphing UAV design, aiming to achieve the maximum acceleration and endurance. In the proposed model, the propeller diameter, propeller pitch and battery values used in morphing UAV's power system design are selected as the input parameters; maximum acceleration and endurance are selected as the output parameters. To obtain the maximum acceleration and endurance, the optimum input parameters are determined through the ABC algorithm-based model. Findings Considerable improvements on maximum acceleration and endurance of morphing UAV with ABC algorithm-based model are obtained. Research limitations/implications The endurance and acceleration due to the thrust are two separate parameters that are not normally proportional to each other. In this study, optimization of UAV’s endurance and acceleration is considered with equal importance. Practical implications Using artificial intelligence techniques causes fast and simple optimization for determination of UAV’s endurance and acceleration with equal importance. In the simulation studies with ABC algorithm, satisfactory results are obtained. Social implications The results of the study have showed that the proposed approach could be an alternative method for UAV designers. Originality/value Providing a new and efficient method saves time and reduces cost in calculations of maximum acceleration and endurance of the UAV.
Purpose The purpose of this paper is to present a novel approach based on the differential search (DS) algorithm integrated with the adaptive network-based fuzzy inference system (ANFIS) for unmanned aerial vehicle (UAV) winglet design. Design/methodology/approach The winglet design of UAV, which was produced at Faculty of Aeronautics and Astronautics in Erciyes University, was redesigned using artificial intelligence techniques. This approach proposed for winglet redesign is based on the integration of ANFIS into the DS algorithm. For this purpose, the cant angle (c), the twist angle (t) and taper ratio (λ) of winglet are selected as input parameters; the maximum value of lift/drag ratio (Emax) is selected as the output parameter for ANFIS. For the selected input and output parameters, the optimum ANFIS parameters are determined by the DS algorithm. Then the objective function based on optimum ANFIS structure is integrated with the DS algorithm. With this integration, the input parameters for the Emax value are obtained by the DS algorithm. That is, the DS algorithm is used to obtain both the optimization of the ANFIS structure and the necessary parameters for the winglet design. Thus, the UAV was reshaped and the maximum value of lift/drag ratio was calculated based on new design. Findings Considerable improvements on the max E are obtained through winglet redesign on morphing UAVs with artificial intelligence techniques. Research limitations/implications It takes a long time to obtain the optimum Emax value by the computational fluid dynamics method. Practical implications Using artificial intelligence techniques saves time and reduces cost in maximizing Emax value. The simulation results showed that satisfactory Emax values were obtained, and an optimum winglet design was achieved. Thus, the presented method based on ANFIS and DS algorithm is faster and simpler. Social implications The application of artificial intelligence methods could be used in designing more efficient aircrafts. Originality/value The study provides a new and efficient method that saves time and reduces cost in redesigning UAV winglets.
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