Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

Modeling of unmanned aerial vehicle (UAV) with system identification is very important in terms of its model-based effective control. The modeling of UAV is required for aircraft crashes, analyzing autonomous aircrafts, preventing external disturbances, pre-flight analysis. However, since UAV has nonlinear inherent dynamics including inherent chaoticity and fractality, it becomes difficult to obtain a mathematical model under external disturbance. In this study, some of the inherent nonlinear dynamics of UAV are linearized and the model of UAV is obtained by system identification approaches under external disturbance. The linearized lateral dynamics of a fixed wing UAV is used in this study. Further, the flight motion equations applied to fixed wing UAV have been utilized for obtaining the coefficients of lateral model for straight and level flight. The roll angles are calculated using transfer functions for aileron, rudder and deflections inputs. The autoregressive exogenous (ARX), autoregressive moving average with exogenous (ARMAX) and output error (OE) parametric system identification approaches are performed to estimate UAV lateral dynamic system response as using empirical input-output data sets. The accuracy of parametric model estimation and model degrees are compared for different external disturbance effects.

Depending on the intended use, the Unmanned Aerial Vehicle (UAV) must either be able to calculate the route itself to follow or be loyal to the predetermined route. In addition, in some cases, it is of paramount importance to follow the route, reduce the cost and follow the route in the most accurate way, especially under difficult conditions. The aim of this study is to investigate the system modeling of quadrotor to design the position and route following control algorithms of the system which is based on this modeling and to simulate the mentioned algorithms with adaptive proportionalintegral-derivative (PID) controller. Firstly, system modeling and mathematical equations has been developed. Secondly, the simulation environment has been created through the MATLAB program. Route tracking in this simulation environment has been performed on three different geometries, rectangle, lemniscate, spiral route tracking and the rate of the quadrotor on these routes and the amount of error has been determined. The comparison of these geometric shapes revealed the necessity of adaptive PID approaches in cases of sudden maneuvers.

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Swarm Unmanned Aerial Vehicles (UAVs) comprise of a group of aircraft that come together to achieve a specific goal. In recent years, the Swarm UAVs have been used in commercial, civil and military fields such as search and rescue operations, cargo transportation, sensitive agricultural practices, and ammunition delivery to war zones. Swarm UAVs can scan large areas in a short time in both military and civilian use. Swarm UAVs, which have the ability to communicate synchronously with each other, can perform complex tasks in a minimum energy and time by collaborating with respect to a single UAV. It is very important that swarm UAVs can follow the desired route with minimum error in order to perform the task in the shortest time and with least energy. In this study, the fuzzy logic controller is proposed for swarm quadrotors to follow the desired route with minimum error. The system modeling and mathematical equations of quadrotor have been developed in simulation environment. The performance of swarm UAVs to follow the rectangular and circular routes with minimum error is analyzed in this simulation. The fuzzy logic controller proposed for route tracking of the swarm UAVs is handled comparatively with the classical proportional-integral-derivative (PID) controller. The fuzzy logic controller developed in this simulation study increases the UAV's sudden maneuverability and ability to complete the task with minimum energy compared to the classical PID controller. The classical PID and fuzzy controller performance of each UAV in the swarm is analyzed graphically and it is observed that the performance of the fuzzy logic controller to follow the reference route is higher than the classical PID controller.