Comparative robustness study of multivariable controller of fixed wing Ultrastick25-e UAV

Mobarez, Eslam Nabil; Sarhan, Amr; Mohamed, Amr

doi:10.1109/icenco.2018.8636142

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…In [81], three control techniques were used to enhance the control response of a fixedwing UAV ultra stick. The first control technique used a Proportional-Integral-Derivative PID controller designed and tuned using a genetic algorithm optimization technique.…”

Section: Fixed-wing Uav Controllersmentioning

confidence: 99%

A Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications

2021

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Recently, unmanned aerial vehicles (UAVs), also known as drones, have gained widespread interest in civilian and military applications, which has led to the development of novel UAVs that can perform various operations. UAVs are aircraft that can fly without the need of a human pilot onboard, meaning they can fly either autonomously or be remotely piloted. They can be equipped with multiple sensors, including cameras, inertial measurement units (IMUs), LiDAR, and GPS, to collect and transmit data in real time. Due to the demand for UAVs in various applications such as precision agriculture, search and rescue, wireless communications, and surveillance, several types of UAVs have been invented with different specifications for their size, weight, range and endurance, engine type, and configuration. Because of this variety, the design process and analysis are based on the type of UAV, with the availability of several control techniques that could be used to improve the flight of the UAV in order to avoid obstacles and potential collisions, as well as find the shortest path to save the battery life with the support of optimization techniques. However, UAVs face several challenges in order to fly smoothly, including collision avoidance, battery life, and intruders. This review paper presents UAVs’ classification, control applications, and future directions in industry and research interest. For the design process, fabrication, and analysis, various control approaches are discussed in detail. Furthermore, the challenges for UAVs, including battery charging, collision avoidance, and security, are also presented and discussed.

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Section: Fixed-wing Uav Controllersmentioning

confidence: 99%

A Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications

2021

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“…According to many previous comparison studies [11][12][13], it has been proven that optimal control approaches provide far more acceptable performance of ground vehicle control as compared with traditional model-free approaches and geometric path tracking algorithms [14]. Although different types of optimal control algorithms have been used in vehicle control, model predictive control (MPC) is the most usable method [15].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments

2021

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In this study, the hardware and software design and implementation of an autonomous electric vehicle are addressed. We aimed to develop an autonomous electric vehicle for path tracking. Control and navigation algorithms are developed and implemented. The vehicle is able to perform path-tracking maneuvers under environments in which the positioning signals from the Global Navigation Satellite System (GNSS) are not accessible. The proposed control approach uses a modified constrained input-output nonlinear model predictive controller (NMPC) for path-tracking control. The proposed localization algorithm used in this study guarantees almost accurate position estimation under GNSS-denied environments. We discuss the procedure for designing the vehicle hardware, electronic drivers, communication architecture, localization algorithm, and controller architecture. The system’s full state is estimated by fusing visual inertial odometry (VIO) measurements with wheel odometry data using an extended Kalman filter (EKF). Simulation and real-time experiments are performed. The obtained results demonstrate that our designed autonomous vehicle is capable of performing path-tracking maneuvers without using Global Navigation Satellite System positioning data. The designed vehicle can perform challenging path tracking maneuvers with a speed of up to 1 m per second.

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PID vs. Backstepping Control for Cooperative Quadrotors Unmanned Aerial Vehicles

Mahfouz¹,

Taiomour

Mohamed³

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Formation reconfiguration is one of the most important tactics used in the field of cooperative Unmanned Air Vehicles. In this paper, formation reconfiguration for a team of vertical takeoff and landing quadrotors is managed by a classical approach of proportional-integral-derivative (PID) controller. PID controller is designed to regulate the attitude and the altitude for every quadrotor of a cooperative team respecting the separating span and velocity constraints. PID controller results are compared with a backstepping controller developed for the same system. The mathematical model of the propositioned system is derived initially, and then a PID controller using simplex and genetic algorithms is designed qualifying the cooperative quadrotors to track the desired trajectories. Simulation results present the assessment of PID control strategy along with backstepping control strategy in different scenarios including proposal flight mission in obstacle-free surroundings, and obstacle-laden surroundings. Noise attenuation and disturbance rejection are examined for both controllers to check the robustness of the system.

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Comparative robustness study of multivariable controller of fixed wing Ultrastick25-e UAV

Cited by 6 publications

References 4 publications

A Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications

A Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications

Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments

PID vs. Backstepping Control for Cooperative Quadrotors Unmanned Aerial Vehicles

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