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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article describes a quadcopter drone with foldable arms, which we have named the Peregrine Falcon. It can take off from a moving vehicle by barrel launching and perform its mission in the air after autonomously spreading its arms. The design helps the drone to be put into operation quickly, which is a great advantage for applications in industries such as forest fire reconnaissance. This paper introduces the design process of the Peregrine Falcon from the aspects of structure, avionics and control algorithm, and proposes control compensation methods for the large attitude change generated during the barrel launching process and the vertical drift problem after impact, respectively. The stability and feasibility of the Peregrine Falcon in the static and dynamic launching process are verified by flying experiments.

This paper presents a study on the design, fabrication, and performance evaluation of a quadcopter with toroidal propellers manufactured using 3D printing technology. The quadcopter's propellers were designed to have a toroidal shape, which is believed to improve the aerodynamic efficiency of the propellers compared to conventional propellers. To evaluate the performance of the quadcopter, finite element analysis (FEA) was used to simulate the propeller's behavior under different load conditions. In addition, thrust analysis was performed using ANSYS Fluent software to evaluate the thrust produced by the toroidal propeller compared to conventional propeller. These results provide insight into the potential benefits of toroidal propellers and 3D printing technology for the design and development of efficient quadcopters. [ABSTRACT FROM AUTHOR]

This paper presents a study on the design, fabrication, and performance evaluation of a quadcopter with toroidal propellers manufactured using 3D printing technology. The quadcopter's propellers were designed to have a toroidal shape, which is believed to improve the aerodynamic efficiency of the propellers compared to conventional propellers. To evaluate the performance of the quadcopter, finite element analysis (FEA) was used to simulate the propeller's behavior under different load conditions. In addition, thrust analysis was performed using ANSYS Fluent software to evaluate the thrust produced by the toroidal propeller compared to conventional propeller. These results provide insight into the potential benefits of toroidal propellers and 3D printing technology for the design and development of efficient quadcopters.

Quadcopter drones are among the most common and widely used drones due to their ability to hover, which is essential in some applications, in addition to their easy control and their high stability to perform mechanical tasks in areas that can only be reached from the air. This work aims to provide a solution to the problem of the flight time of a quadcopter by proposing a model of an alternative energy system composed of a photovoltaic solar panel, which constitutes hybridization energy besides the Lithium-Ion battery. A fuel cell power generation system can be integrated into the proposed source to give additional flight time. Furthermore, each component of the quadcopter has been carefully selected, by taking into account the weight factor, propulsion efficiency, and energy density. [ABSTRACT FROM AUTHOR]

The aim of this publication is to conclude on the interest of vibratory energy harvesting on classic quadcopter drone for autonomous applications (battery charging in real time, autonomous sensors), monitoring or even vibration control applications. A complete dynamic analysis allows to quantify the amount of electrical power that is possible to produce during the hovering flight of a quadcopter drone. These results have been obtained by substitution of the inert parts of the drone by piezoelectric components. For that purpose, different types of piezoelectric structures have been tested, including some commercial transducers (DuraAct from Piezoelectric Instrument and Murata buzzers) and some home-made such as a piezoelectric paint. Our original piezoelectric smart arms have been able to scavenge up to 5.35 mW during a stationary flight which remains quite enough to supply low-consumption sensors for monitoring applications. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of the electromagnetic signature and the coupling mechanism of quadcopter drones with incident electromagnetic (EM) wave and radar cross section (RCS) analysis. Coupling analysis is performed based on the dominant coupling path: when an incident EM wave with a magnitude of 50 kV/m contacts a commercial quadcopter drone, its motor power wires are identified as the dominant coupling path. Higher coupling voltages are obtained for frequencies that have large impedance values at both ends of the load on the motor power wire. This induced voltage can affect the integrated circuit chip on a printed circuit board, as well as parallel plate resonances. Furthermore, the RCS of a quadcopter drone is measured in the frequency range of 0.5-3 GHz. The internal-component vulnerability characteristics of quadcopters can spike at specific frequencies with high RCS values and can be analyzed with or without motor power wires. We verified these hypotheses via 2D inverse synthetic aperture radar images, and we analyzed the results by comparing the empirical and full-wave simulation values.

Robustness in the face of uncertainties is an important aspect in designing high performance control systems. This paper addresses the problem of accurate trajectory tracking of a small quadcopter unmanned aerial vehicle in the face of uncertainties. Accommodating the worst-case scenario, we propose a hybrid feedback and feedforward autopilot that has the capability to eliminate the cross-coupling disturbance between the lateral and the longitudinal loops with respect to the vertical loop as well as external disturbances (e.g., wind gusts). The proposed control system leverages on the technical benefits of both the nonlinear model predictive control and the fuzzy feedforward compensator. We highlight the efficacy of our hybrid autopilot system with respect to the performance of the conventional PD control systems through rigorous comparative studies. We also present stability analysis of our hybrid control system. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Sciences / Zemès ukio Mokslai is the property of Lithuanian Academy of Sciences Publishers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Trajectory tracking control of a quadcopter drone is a challenging work due to highly-nonlinear dynamics of the system, coupled with uncertainties in the flight environment (e.g. unpredictable wind gusts, measurement noise, modelling errors, etc). This paper addresses the aforementioned research challenges by proposing evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang-type fuzzy logic controller (FLC). We consider three major state-of-the-art optimisation algorithms, namely, Genetic Algorithm, Particle Swarm Optimisation, and Artificial Bee Colony to facilitate automatic tuning. The effectiveness of the proposed control schemes is tested and compared under several different flight conditions, such as, constant, varying step and sine functions. The results show that the ABC-FLC outperforms the GA-FLC and PSO-FLC in terms of minimising the settling time in the absence of overshoots. Highlights • Evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots are proposed. The proposed controllers are applied for position control of quadcopter drone, which is a multi-input multi-output (MIMO) system, with highly non-linear rigid body dynamics and severe cross-couplings. • Given constant and varying step functions, ABC-FLC has slower rise and peak time, and faster settling time in the absence of overshoots. On the contrary, GA-FLC with a mutation rate of 0.1 has faster rise time and bigger overshoots compared to GA-FLC with a mutation rate of 0.4 and PSO-FLC. • Given constant and varying step functions, all proposed controllers yield ununiformity in the shape and position of the fuzzy sets although there are overlap zones among several tuned fuzzy sets. However, under sine function, ABC-FLC yields uniformity in the shape of fuzzy sets, and the overlap zone is almost fifty percent. • Overall, all three-dimensional control surfaces appear to have the same tendency, which is nonlinear and the volume of ABC-FLC is smaller than the other controllers. However, under constant and varying step functions, the shapes of control surface of ABC-FLC has less distinctive small bumps in the plane of e and Δ e , while under sine function, ABC-FLC has distinctive smaller bumps compared to other controllers. Graphical abstract [ABSTRACT FROM AUTHOR]

The present paper recalls a formulation of non-conservative system dynamics through the Lagrange−d’Alembert principle expressed through a generalized Euler−Poincaré form of the system equation on a Lie group. The paper illustrates applications of the generalized Euler−Poincaré equations on the rotation groups to a gyrostat satellite and a quadcopter drone. The numerical solution of the dynamical equations on the rotation groups is tackled via a generalized forward Euler method and an explicit Runge−Kutta integration method tailored to Lie groups.

In this paper, we present a multi-drone system featured with a decentralized model predictive control (DMPC) flocking algorithm. The drones gather localized information from neighbors and update their velocities using the DMPC flocking algorithm. In the multi-drone system, data packages are transmitted through XBee ® wireless modules in broadcast mode, yielding such an anonymous and decentralized system where all the calculations and controls are completed on an onboard minicomputer of each drone. Each drone is a double-layered agent system with the coordination layer running multi-drone flocking algorithms and the flight control layer navigating the drone, and the final formation of the flock relies on both the communication range and the desired inter-drone distance. We give both numerical simulations and field tests with a flock of five drones, showing that the DMPC flocking algorithm performs well on the presented multi-drone system in both the convergence rate and the ability of tracking a desired path. [ABSTRACT FROM AUTHOR]

Uncrewed aerial vehicles (UAVs) for last-mile deliveries will affect the energy productivity of delivery and require new methods to understand energy consumption and greenhouse gas (GHG) emissions. We combine empirical testing of 188 quadcopter flights across a range of speeds with a first-principles analysis to develop a usable energy model and a machine-learning algorithm to assess energy across takeoff, cruise, and landing. Our model shows that an electric quadcopter drone with a very small package (0.5 kg) would consume approximately 0.08 MJ/km and result in 70 g of CO 2 e per package in the United States. We compare drone delivery with other vehicles and show that energy per package delivered by drones (0.33 MJ/package) can be up to 94% lower than conventional transportation modes, with only electric cargo bicycles providing lower GHGs/package. Our open model and coefficients can assist stakeholders in understanding and improving the sustainability of small package delivery., Competing Interests: The authors declare no competing interests. C.S. has a patent for a system, method, and computer program product for transporting an unmanned vehicle, US patent 11300978B1, 2022. The patent’s methods were not used this study., (© 2022 The Author(s).)

The present paper recalls a formulation of non-conservative system dynamics through the Lagrange–d'Alembert principle expressed through a generalized Euler–Poincaré form of the system equation on a Lie group. The paper illustrates applications of the generalized Euler–Poincaré equations on the rotation groups to a gyrostat satellite and a quadcopter drone. The numerical solution of the dynamical equations on the rotation groups is tackled via a generalized forward Euler method and an explicit Runge–Kutta integration method tailored to Lie groups. [ABSTRACT FROM AUTHOR]