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Journal articles on the topic 'Quadrotors swarm'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Nakano, Reiichiro Christian S., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Edwin Sybingco, Elmer P. Dadios, and Argel A. Bandala. "Utilization of the Physicomimetics Framework for Achieving Local, Decentralized, and Emergent Behavior in a Swarm of Quadrotor Unmanned Aerial Vehicles (QUAV)." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 189–96. http://dx.doi.org/10.20965/jaciii.2017.p0189.

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This paper presents the implementation of the physicomimetics framework in governing the behavior of a swarm of quadrotors. Each quadrotor uses only local information about itself and the neighboring quadrotors to determine its own movement by applying the principles of physicomimetics. Through these localized and relatively simple interactions, the swarm of quadrotors was able to organize itself into various structures and exhibit different swarm behaviors such as aggregation, obstacle avoidance, lattice formation, and dispersion.

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Xie, Yichen, Yuzhu Li, and Wei Dong. "Behavior Prediction Based Trust Evaluation for Adaptive Consensus of Quadrotors." Drones 6, no. 12 (2022): 371. http://dx.doi.org/10.3390/drones6120371.

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Without proper treatment, a malfunctional quadrotor may bring severe consequences, e.g., becoming out of control, to the whole swarm. To tackle this problem, we develop a trust evaluations based consensus protocol. Specifically, each quadrotor in the swarm communicates with its connected neighbors, exchanging behavior predictions. By comparing the predicted and the actual behaviors of its neighbor regarding a pre-defined tolerance, each quadrotor assigns trust values to determine potentially legitimate or malfunctional companions. On this basis, an online adaptive controller adjusts each weigh

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Joelianto, Endra, Daniel Christian, and Agus Samsi. "Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm." Journal of Mechatronics, Electrical Power, and Vehicular Technology 11, no. 1 (2020): 1. http://dx.doi.org/10.14203/j.mev.2020.v11.1-10.

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Unmanned aerial vehicle (UAV) quadrotors have developed rapidly and continue to advance together with the development of new supporting technologies. However, the use of one quadrotor has many obstacles and compromises the ability of a UAV to complete complex missions that require the cooperation of more than one quadrotor. In nature, one interesting phenomenon is the behaviour of several organisms to always move in flocks (swarm), which allows them to find food more quickly and sustain life compared with when they move independently. In this paper, the swarm behaviour is applied to drive a sy

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Khodayari, Houri, Farshad Pazooki, and AliReza Khodayari. "Motion optimization algorithm designing for swarm quadrotors in application of grasping objects." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 11 (2018): 3938–51. http://dx.doi.org/10.1177/0954410018812615.

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In this study, the process of designing a motion optimization algorithm for swarm quadrotor robots is presented. Motions equations of swarm are written based on Lagrangian energy equations. A potential function is applied on the equations to optimize the swarm motion. The applied potential function enables each of the swarm members to move toward an independent target coordinate as motion starts and simultaneously connecting with other members. As a result, the necessity of having the members aggregated within an area close to the swarm center is eliminated. This algorithm is supposed to act o

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Apriaskar, Esa. "PURWARUPA SISTEM PENDETEKSI JARAK ANTAR QUADROTOR DENGAN SENSOR GPS." INOVTEK POLBENG 8, no. 2 (2018): 250. http://dx.doi.org/10.35314/ip.v8i2.768.

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Technology of UAV (Unmanned Aerial Vehicle) which is quite rapidly developing in recent years, is quadrotor. The increasing number of quadrotor utilization in various aspects of life is one of the factors driving the development of research on quadrotor technology. The ability of a quadrotor to determine its distance from other quadrotor is one of the important factors that can support the success of formation swarm of quadrotor. This research aimed to create a prototype of distance detection system capable of supporting the mission of the formation swarm of quadrotor. Two pairs of latitude an

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Faelden, Gerard Ely U., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Edwin Sybingco, Elmer P. Dadios, and Argel A. Bandala. "Implementation of Swarm Social Foraging Behavior in Unmanned Aerial Vehicle (UAV) Quadrotor Swarm." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 197–204. http://dx.doi.org/10.20965/jaciii.2017.p0197.

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One of the novel approaches in multiple quadrotor control is swarm robotics. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm social foraging behavior in unmanned aerial vehicle quadrotors. To achieve this, it first explores the basic behavior of aggregation. It is implemented over a quadrotor swarm test-bed that makes use of external motion capture cameras. The completed algorithm makes use of the artificial potential function model combined with the environment resource profile model. Results show successful demonstration

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Cardona, Gustavo A., Juan Ramirez-Rugeles, Eduardo Mojica-Nava, and Juan M. Calderon. "Visual victim detection and quadrotor-swarm coordination control in search and rescue environment." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2079. http://dx.doi.org/10.11591/ijece.v11i3.pp2079-2089.

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We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Finally, im

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Gustavo, A. Cardona, Ramirez-Rugeles Juan, Mojica-Nava Eduardo, and M. Calderon Juan. "Visual victim detection and quadrotor-swarm coordination control in search and rescue environment." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2079–89. https://doi.org/10.11591/ijece.v11i3.pp2079-2089.

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We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Final

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Carbone, Carlos, Oscar Garibaldi, and Zohre Kurt. "Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture." KnE Engineering 3, no. 1 (2018): 552. http://dx.doi.org/10.18502/keg.v3i1.1459.

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This paper summarizes the concept of swarm robotics and its applicability to crop inspections. To increase the agricultural yield it is essential to monitor the crop health. Hence, precision agriculture is becoming a common practice for farmers providing a system that can inspect the state of the plants (Khosla and others, 2010). One of the rising technologies used for agricultural inspections is the use of unmaned air vehicles (UAVs) which are used to take aerial pictures of the farms so that the images could be processed to extract data about the state of the crops (Das et al., 2015). For th

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Hovell, Kirk, Steve Ulrich, and Murat Bronz. "Learned Multiagent Real-Time Guidance with Applications to Quadrotor Runway Inspection." Field Robotics 2, no. 1 (2022): 1105–33. http://dx.doi.org/10.55417/fr.2022036.

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Aircraft runways are periodically inspected for debris and damage. Instead of having pilots coordinate the motion of the quadrotors manually or hand-crafting the desired quadrotor behavior into a guidance law, this paper reports the use of deep reinforcement learning to learn a closed-loop multiagent real-time guidance strategy for quadrotors to autonomously perform such inspections. This yields a significant reduction in engineering effort while enabling highly-flexible real-time performance. The runway is discretized into a number of rectangular tiles, which must all be visited for the runwa

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Kurnaz, Muhammed Kivanc, Yagmur Olmez, and Gonca Ozmen Koca. "Altitude Control of Quadrotor Based on Metaheuristic Methods." International Journal of Innovative Engineering Applications 9, no. 1 (2025): 37–46. https://doi.org/10.46460/ijiea.1564844.

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Quadrotor, which is used in many fields and is still a challenge to control, has a complex kinematic and dynamic system, and its flight performance depends on many variables that need to be controlled simultaneously. In this study, the effective determination of PID parameters for altitude control of quadrotors, which presents a complex control problem, has been tested comparatively with innovative metaheuristic approaches. Among the strong metaheuristic algorithms, Crow Search Algorithm (CSA), Particle Swarm Optimization Algorithm (PSO), Golden Jackal Optimization Algorithm (GJO), and Jellyfi

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Bandala, Argel A., Elmer P. Dadios, Ryan Rhay P. Vicerra, and Laurence A. Gan Lim. "Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm Behavior for Aggregation, Foraging, Formation, and Tracking –." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 5 (2014): 745–51. http://dx.doi.org/10.20965/jaciii.2014.p0745.

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This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded th

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Kushleyev, Alex, Daniel Mellinger, Caitlin Powers, and Vijay Kumar. "Towards a swarm of agile micro quadrotors." Autonomous Robots 35, no. 4 (2013): 287–300. http://dx.doi.org/10.1007/s10514-013-9349-9.

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Joelianto, Endra, Winarendra Satya Rajasa, and Agus Samsi. "Sistem Kontrol Swarm untuk Flocking Wahana NR-Awak Quadrotor dengan Optimasi Algoritma Genetik." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 6 (2021): 1089. http://dx.doi.org/10.25126/jtiik.2021863467.

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<p class="Abstrak">Quadrotor merupakan wahana udara nir-awak jenis lepas landas atau pendaratan vertikal berbentuk silang dan memiliki sebuah rotor pada setiap ujung lengannya dengan kemampuan manuver yang tinggi. <em>Swarm</em> quadrotor yang terdiri dari sekumpulan quadrotor akan menjadi suatu <em>swarm</em> yang baik, sesuai dengan kriteria <em>swarm</em> oleh Reynold yaitu dapat menghindari tumbukan, menyamakan kecepatan, dan pemusatan <em>swarm</em>. Pengontrolan <em>s</em><em>warm</em> quadrotor memiliki tingka

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Weinstein, Aaron, Adam Cho, Giuseppe Loianno, and Vijay Kumar. "Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors." IEEE Robotics and Automation Letters 3, no. 3 (2018): 1801–7. http://dx.doi.org/10.1109/lra.2018.2800119.

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Li, Zhichen, Qiaoran Wang, and Huaicheng Yan. "Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance." Applied Sciences 15, no. 13 (2025): 7021. https://doi.org/10.3390/app15137021.

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This study proposes an event-triggered fault-tolerant active disturbance rejection control (ADRC) method for variable-load quadrotors with prescribed performance. The quadrotor, as a nonlinear and underactuated system, faces challenges such as payload variations, actuator faults, and external disturbances, which degrade trajectory tracking accuracy and stability. The proposed approach integrates a cascaded ADRC framework, decoupling the system into position and velocity subsystems, each equipped with extended state observers (ESOs) for real-time disturbance estimation and compensation. To enha

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Leonard, J., A. Savvaris, and A. Tsourdos. "Distributed reactive collision avoidance for a swarm of quadrotors." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 6 (2016): 1035–55. http://dx.doi.org/10.1177/0954410016647074.

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The large-scale of unmanned aerial vehicle applications has escalated significantly within the last few years, and the current research is slowly hinting at a move from single vehicle applications to multivehicle systems. As the number of agents operating in the same environment grows, conflict detection and resolution becomes one of the most important factors of the autonomous system to ensure the vehicles’ safety throughout the completion of their missions. The work presented in this paper describes the implementation of the novel distributed reactive collision avoidance algorithm proposed i

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Yañez-Badillo, Hugo, Francisco Beltran-Carbajal, Ruben Tapia-Olvera, Antonio Favela-Contreras, Carlos Sotelo, and David Sotelo. "Adaptive Robust Motion Control of Quadrotor Systems Using Artificial Neural Networks and Particle Swarm Optimization." Mathematics 9, no. 19 (2021): 2367. http://dx.doi.org/10.3390/math9192367.

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Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introdu

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Trizuljak, Adam, Frantiek Duchoň, Jozef Rodina, Andrej Babinec, Martin Dekan, and Roman Mykhailyshyn. "Control of a small quadrotor for swarm operation." Journal of Electrical Engineering 70, no. 1 (2019): 3–15. http://dx.doi.org/10.2478/jee-2019-0001.

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Abstract Small quadrotors, or so-called nanoquads, are widely available, typically have small take-off mass (between 12–50 g), and a flight time of about 5–10 minutes. The aim of this article is the proposal of control and development of the basic infrastructure for controlling a swarm nanoquads from an external computer and obtaining measurements from an onboard sensor. Control of nanoquad attitude and position is proposed and control allocation problem is addressed. Additionally, landing and collision detection is implemented using external disturbance force estimation. Results of the propos

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McGuire, K. N., C. De Wagter, K. Tuyls, H. J. Kappen, and G. C. H. E. de Croon. "Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment." Science Robotics 4, no. 35 (2019): eaaw9710. http://dx.doi.org/10.1126/scirobotics.aaw9710.

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Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies. The absence of external infrastructure implies that any positioning attempts must be performed by the robots themselves. State-of-the-art solutions, such as simultaneous localization and mapping, are still too resource demanding. This article presents the swarm gradient bug algorithm (SGB

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Srisamosorn, Veerachart, Noriaki Kuwahara, Atsushi Yamashita, Taiki Ogata, and Jun Ota. "Human-tracking system using quadrotors and multiple environmental cameras for face-tracking application." International Journal of Advanced Robotic Systems 14, no. 5 (2017): 172988141772735. http://dx.doi.org/10.1177/1729881417727357.

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In this article, a system for tracking human’s position and orientation in indoor environment was developed utilizing environmental cameras. The system consists of cameras installed in the environment at fixed locations and orientations, called environmental cameras, and a moving robot which mounts a camera, called moving camera. The environmental cameras detect the location and direction of each person in the space, as well as the position of the moving robot. The robot is then controlled to move and follow the person’s movement based on the person’s location and orientation, mimicking the ac

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Tsykunov, Evgeny, Ruslan Agishev, Roman Ibrahimov, Luiza Labazanova, Akerke Tleugazy, and Dzmitry Tsetserukou. "SwarmTouch: Guiding a Swarm of Micro-Quadrotors With Impedance Control Using a Wearable Tactile Interface." IEEE Transactions on Haptics 12, no. 3 (2019): 363–74. http://dx.doi.org/10.1109/toh.2019.2927338.

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Elmokadem, Taha, and Andrey V. Savkin. "Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking." Drones 5, no. 4 (2021): 124. http://dx.doi.org/10.3390/drones5040124.

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This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robu

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Zhang, Yahui, Peng Yi, and Yiguang Hong. "Cooperative Safe Trajectory Planning for Quadrotor Swarms." Sensors 24, no. 2 (2024): 707. http://dx.doi.org/10.3390/s24020707.

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In this paper, we propose a novel distributed algorithm based on model predictive control and alternating direction multiplier method (DMPC-ADMM) for cooperative trajectory planning of quadrotor swarms. First, a receding horizon trajectory planning optimization problem is constructed, in which the differential flatness property is used to deal with the nonlinear dynamics of quadrotors while we design a relaxed form of the discrete-time control barrier function (DCBF) constraint to balance feasibility and safety. Then, we decompose the original trajectory planning problem by ADMM and solve it i

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El gmili, Nada, Mostafa Mjahed, Abdeljalil El kari, and Hassan Ayad. "Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach." Computational Intelligence and Neuroscience 2019 (July 24, 2019): 1–10. http://dx.doi.org/10.1155/2019/8925165.

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This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS). The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS. To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor. After defining the quadrotor dynamic modelling using Newton–Euler formalism, the quadrotor model’s parameters are extracted by usin

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Maningo, Jose Martin Z., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Edwin Sybingco, Elmer P. Dadios, and Argel A. Bandala. "Smoothed Particle Hydrodynamics Approach to Aggregation of Quadrotor Unmanned Aerial Vehicle Swarm." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 181–88. http://dx.doi.org/10.20965/jaciii.2017.p0181.

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This paper uses a fluid mechanics approach to perform swarming aggregation on a quadrotor unmanned aerial vehicle (QUAV) swarm platform. This is done by adapting the Smoothed Particle Hydrodynamics (SPH) technique. An algorithm benchmarking is conducted to see how well SPH performs. Simulations of varying set-ups are experimented to compare different algorithms with SPH. The position error of SPH is 30% less than the benchmark algorithm when a target enclosure is introduce. SPH is implemented using Crazyflie quadrotor swarm. The aggregation behavior exhibited successfully in the said platform.

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Zhou, De Xin, Xin Chao Ma, and Teng Da Ma. "Path Planning of Quadrotor Based on Quantum Particle Swarm Optimization Algorithm." Advanced Materials Research 760-762 (September 2013): 2018–22. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2018.

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Nowadays, it becomes a hot research topic for autonomous flight of Quadrotor in the complex environment and the realization of fully autonomous flight is still a big challenge. The path planning of unmanned aerial vehicle is a key problem for its autonomous flight. For the path planning of Quadrotor, using the quantum particle swarm optimization algorithm, and made a lot of simulation and actual flight experiments. The results of simulation and actual flight experiment show that the using of QPSO for the path planning of Quadrotor is able to obtain a satisfactory result.

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Zhang, Qi, Yaoxing Wei, and Xiao Li. "Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control." Applied Sciences 11, no. 24 (2021): 11583. http://dx.doi.org/10.3390/app112411583.

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In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization

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Mohammed, Mohammed, Abduladhem Ali, and Mofeed Rashid. "Fuzzy Petri Net Controller for Quadrotor System using Particle Swam Optimization." Iraqi Journal for Electrical and Electronic Engineering 11, no. 1 (2015): 132–44. http://dx.doi.org/10.37917/ijeee.11.1.14.

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In this paper, fuzzy Petri Net controller is used for Quadrotor system. The fuzzy Petrinet controller is arranged in the velocity PID form. The optimal values for the fuzzy Petri Net controller parameters have been achieved by using particle swarm optimization algorithm. In this paper, the reference trajectory is obtained from a reference model that can be designed to have the ideal required response of the Quadrotor, also using the quadrotor equations to find decoupling controller is first designed to reduce the effect of coupling between different inputs and outputs of quadrotor. The system

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Honig, Wolfgang, James A. Preiss, T. K. Satish Kumar, Gaurav S. Sukhatme, and Nora Ayanian. "Trajectory Planning for Quadrotor Swarms." IEEE Transactions on Robotics 34, no. 4 (2018): 856–69. http://dx.doi.org/10.1109/tro.2018.2853613.

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Selma, Boumediene, Samira Chouraqui, and Hassane Abouaïssa. "Fuzzy swarm trajectory tracking control of unmanned aerial vehicle." Journal of Computational Design and Engineering 7, no. 4 (2020): 435–47. http://dx.doi.org/10.1093/jcde/qwaa036.

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Abstract Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment o

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Xu, Linxing, and Yang Li. "Distributed Robust Formation Tracking Control for Quadrotor UAVs with Unknown Parameters and Uncertain Disturbances." Aerospace 10, no. 10 (2023): 845. http://dx.doi.org/10.3390/aerospace10100845.

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In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles is considered. Adaptive backstepping inherently accommodates model uncertainties and external disturbances, making it a robust choice for the dynamic and unpredictable environments in which unmanned aerial vehicles operate. This paper designs a formation flight control scheme for quadrotor unmanned aerial vehicles based on adaptive backstepping technology. The proposed control scheme is divided into two parts. For the position subsystem, a distributed robust formation tracking control scheme

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El Gmili, Mjahed, El Kari, and Ayad. "Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking." Applied Sciences 9, no. 8 (2019): 1719. http://dx.doi.org/10.3390/app9081719.

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This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) byexploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search(CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposedPSO-CS algorithm combines the ability of social thinking in PSO with the local search capability inCS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotordynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral,and Derivative) controllers are optimized by u

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Shen, Suiyuan, and Jinfa Xu. "Attitude Active Disturbance Rejection Control of the Quadrotor and Its Parameter Tuning." International Journal of Aerospace Engineering 2020 (November 16, 2020): 1–15. http://dx.doi.org/10.1155/2020/8876177.

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The internal uncertainty and external disturbance of the quadrotor will have a significant impact on flight control. Therefore, to improve the control system’s dynamic performance and robustness, the attitude active disturbance rejection controller (ADRC) of the quadrotor is established. Simultaneously, an adaptive genetic algorithm-particle swarm optimization (AGA-PSO) is used to optimize the controller parameters to solve the problem that the controller parameters are difficult to tune. The performance of the proposed ADRC is compared with that of the sliding mode controller (SMC). The simul

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Gursoy Demir, Habibe. "Grey Wolf Optimization- and Particle Swarm Optimization-Based PD/I Controllers and DC/DC Buck Converters Designed for PEM Fuel Cell-Powered Quadrotor." Drones 9, no. 5 (2025): 330. https://doi.org/10.3390/drones9050330.

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The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to design controllers and DC/DC buck converters for optimizing the energy consumption and path following error of a PEM fuel cell-powered quadrotor system. Hence, the system consists of two PSO- and GWO-based optimizers. Optimizer I is used for determining the parameters of the PD controller

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Benbrahim, Meriem, Moufid Bouhentala, Mouna Ghanai, Kheireddine Chafaa, and Najib Essounbouli. "Generalization to Type 2 of PSO-Optimized Type 1 PD Fuzzy Controller and its Application to a Quadrotor UAV." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21658–64. https://doi.org/10.48084/etasr.9777.

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This study presents a new Type 2 fuzzy logic (interval-valued fuzzy logic) control system for a Vertical Take Off and Landing (VTOL) quadrotor. The goal of the control design is to obtain robust and stable tracking of the desired angles in the presence of disturbances and noises. The membership functions relative to the linguistic variables of the fuzzy if-then rules are chosen to control the quadrotor to track a reference trajectory. The Particle Swarm Optimization (PSO) method is used to optimize controller-free parameters. The results obtained from an extensive simulation study show that th

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Can, Muharrem Selim, and Hamdi Ercan. "Real-time tuning of PID controller based on optimization algorithms for a quadrotor." Aircraft Engineering and Aerospace Technology 94, no. 3 (2021): 418–30. http://dx.doi.org/10.1108/aeat-06-2021-0173.

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Purpose This study aims to develop a quadrotor with a robust control system against weight variations. A Proportional-Integral-Derivative (PID) controller based on Particle Swarm Optimization and Differential Evaluation to tune the parameters of PID has been implemented with real-time simulations of the quadrotor. Design/methodology/approach The optimization algorithms are combined with the PID control mechanism of the quadrotor to increase the performance of the trajectory tracking for a quadrotor. The dynamical model of the quadrotor is derived by using Newton-Euler equations. Findings In th

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Wang, Yingxun, Yan Ma, Zhihao Cai, and Jiang Zhao. "Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer." Transactions of the Institute of Measurement and Control 42, no. 9 (2020): 1675–89. http://dx.doi.org/10.1177/0142331219894401.

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In this paper, a new swarm intelligent-based backstepping control scheme is proposed for quadrotor trajectory tracking and obstacle avoidance. First, the sliding mode extended state observer (SMESO) is used to estimate different disturbances, and the tracking differentiator (TD) is integrated to enhance the performance of backstepping control scheme. Then, the chaotic grey wolf optimization (CGWO) is developed with chaotic initialization and chaotic search to optimize the parameters of attitude and position controllers. Further, the virtual target guidance approach is proposed for quadrotor tr

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Basri, Mohd Ariffanan Mohd. "Design and application of an adaptive backstepping sliding mode controller for a six-DOF quadrotor aerial robot." Robotica 36, no. 11 (2018): 1701–27. http://dx.doi.org/10.1017/s0263574718000668.

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SUMMARYThe quadrotor aerial robot is a complex system and its dynamics involve nonlinearity, uncertainty, and coupling. In this paper, an adaptive backstepping sliding mode control (ABSMC) is presented for stabilizing, tracking, and position control of a quadrotor aerial robot subjected to external disturbances. The developed control structure integrates a backstepping and a sliding mode control approach. A sliding surface is introduced in a Lyapunov function of backstepping design in order to further improve robustness of the system. To attenuate a chattering problem, a saturation function is

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El, Ayachi Chater, Housny Halima, and El Fadil Hassan. "Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3607–19. https://doi.org/10.11591/ijece.v12i4.pp3607-3619.

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When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hi

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Oliveira, Josenalde, Paulo Moura Oliveira, José Boaventura-Cunha, and Tatiana Pinho. "Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller." Robotics 9, no. 2 (2020): 22. http://dx.doi.org/10.3390/robotics9020022.

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The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its stri

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Bandala, Argel A., and Elmer P. Dadios. "Dynamic Aggregation Method for Target Enclosure Using Smoothed Particle Hydrodynamics Technique – An Implementation in Quadrotor Unmanned Aerial Vehicles (QUAV) Swarm –." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 1 (2016): 84–91. http://dx.doi.org/10.20965/jaciii.2016.p0084.

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This paper presents an aggregation behavior derived from fluid characteristics by adapting Smoothed Particle Hydrodynamics (SPH) Technique. The most basic behavior in a swarm-like system is aggregation. The essential requirement of a swarm is to aggregate or collect itself in proximity to a singular point in order to execute higher level swarm behaviors. The aggregation behavior is further put into use by initiating a near convergence status in a single target enclosing it by the swarm with a given specific distance by using different fluid containers. In this paper, there are three fluid cont

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Ayachi Chater, El, Halima Housny, and Hassan El Fadil. "Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3607. http://dx.doi.org/10.11591/ijece.v12i4.pp3607-3619.

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<span>When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy u

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Abera, Nardos Belay, Chala Merga Abdissa, and Lebsework Negash Lemma. "An improved nonsingular adaptive super twisting sliding mode controller for quadcopter." PLOS ONE 19, no. 10 (2024): e0309098. http://dx.doi.org/10.1371/journal.pone.0309098.

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This paper presents an improved nonsingular adaptive super twisting sliding mode control for tracking of a quadrotor system in the presence of external disturbances and uncertainty. The initial step involves developing a dynamic model for the quadrotor that is free from singularities, achieved through the utilization of the Newton-Quaternion formalism. Then, the super twisting algorithm is used to develop a novel sliding mode control that mitigates chattering. Particle Swarm Optimization (PSO) is employed for the adjustment of the controller gains. Moreover, to maintain stable control of the q

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Qin, Zhenhao. "PID Control Algorithm Based on Particle Swarm Optimization for Quadrotor UAV with Tip Defect." Academic Journal of Science and Technology 7, no. 2 (2023): 101–5. http://dx.doi.org/10.54097/ajst.v7i2.11951.

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For the four-rotor UAV (Unmanned Aerial Vehicle), blade is one of the most important actuator, the four-rotor UAV is prone to blade tip defect during use, which will directly affect the reasoning size of the four-rotor UAV, resulting in the flight quality or performance decline of the four-rotor UAV, ordinary PID control in the case of blade tip defect, it can still be optimized by other algorithms. In this paper, particle swarm optimization will be used to optimize PID parameters in the case of tip defect of quadrotor UAV, and simulation experiments will be conducted in MATLAB Simulink to ver

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Abdul-Samed, Baqir, and Ammar Aldair. "Design Tunable Robust Controllers for Unmanned Aerial Vehicle Based on Particle Swarm Optimization Algorithm." Iraqi Journal for Electrical and Electronic Engineering 15, no. 2 (2019): 89–100. http://dx.doi.org/10.37917/ijeee.15.2.10.

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PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ

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Abdelghany, Muhammad Bakr, Ahmed M. Moustafa, and Mohammed Moness. "Benchmarking Tracking Autopilots for Quadrotor Aerial Robotic System Using Heuristic Nonlinear Controllers." Drones 6, no. 12 (2022): 379. http://dx.doi.org/10.3390/drones6120379.

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This paper investigates and benchmarks quadrotor navigation and hold autopilots’ global control performance using heuristic optimization algorithms. The compared methods offer advantages in terms of computational effectiveness and efficiency to tune the optimum controller gains for highly nonlinear systems. A nonlinear dynamical model of the quadrotor using the Newton–Euler equations is modeled and validated. Using a modified particle swarm optimization (MPSO) and genetic algorithm (GA) from the heuristic paradigm, an offline optimization problem is formulated and solved for three different co

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Hong, Youkyung, Suseong Kim, and Jihun Cha. "Integrated Global and Local Path Planning for Quadrotor Using Particle Swarm Optimization." IFAC-PapersOnLine 53, no. 2 (2020): 15621–25. http://dx.doi.org/10.1016/j.ifacol.2020.12.2497.

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Bouallègue, Soufiene, and Rabii Fessi. "LQG controller design for a quadrotor UAV based on particle swarm optimisation." International Journal of Automation and Control 13, no. 5 (2019): 569. http://dx.doi.org/10.1504/ijaac.2019.10021363.

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Fessi, Rabii, and Soufiene Bouallègue. "LQG controller design for a quadrotor UAV based on particle swarm optimisation." International Journal of Automation and Control 13, no. 5 (2019): 569. http://dx.doi.org/10.1504/ijaac.2019.101910.

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