Observer-Based Time-Varying Backstepping Control for a Quadrotor Multi-Agent System

Rosaldo-Serrano, M.A.; Santiaguillo-Salinas, J.; Aranda-Bricaire, E.

doi:10.1007/s10846-018-0867-8

Cited by 23 publications

(6 citation statements)

References 16 publications

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“…En Bristeau et al (2011) los diseñadores del Ar Drone 2.0 publicaron detalles del controlador interno atrayendo a más usuarios de la comunidad de control y de otras áreas afines. El Ar Drone 2.0 se ha utilizado para validar experimentalmente algoritmos de control para sistemas multiagente Rosaldo-Serrano et al (2019), Rojo-Rodriguez et al (2019). La última versión de este dron, el Bebop, ya implementa algoritmos de localización y mapeo simultáneo (SLAM) por lo que se utiliza en tareas avanzadas como carreras de drones, manipulación aérea, inspección y navegación autónoma Rojas-Perez and Martinez-Carranza (2021), Lopez Luna et al (2020).…”

Section: Los Sensores Smem Se Masificanunclassified

Aportaciones al control de vehículos aéreos no tripulados en México

Rodriguez-Cortes

2022

Rev. iberoam. autom. inform. ind.

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El avance en el desarrollo de sistemas microelectromecánicos (MEMS) hizo posible el uso de sensores inerciales y visuales en vehículos aéreos no tripulados de pequeñas dimensiones y bajo costo. Este hecho potenciado con herramientas de la teoría de control automático y las ingenierías aeronáutica, mecatrónica e informática ha permitido lograr niveles de autonomía sorprendentes en este tipo de vehículos. Este trabajo describe una cronología sobre el desarrollo de vehículos aéreos no tripulados desarrollados por investigadores mexicanos. Además, se presenta una solución al problema de seguimiento de trayectorias para el vehículo multi rotor conocido como cuatrirotor. El algoritmo de control se sintetiza siguiendo la metodología conocida como sistema de control total de la energía (TECS). Finalmente, la estrategia de control propuesta se evalúa experimentalmente en tiempo real estricto utilizando un cuatrirotor hecho en casa.

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Section: Los Sensores Smem Se Masificanunclassified

Aportaciones al control de vehículos aéreos no tripulados en México

Rodriguez-Cortes

2022

Rev. iberoam. autom. inform. ind.

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“…Each drone is subjected to external disturbances in terms of wind gusts. From [30], [31] and [32], one can obtain the dynamical model of the i th drone in the standard state-space representation as…”

Section: Problem Formulationmentioning

confidence: 99%

Adaptive Active Disturbance Rejection Control for Rendezvous of a Swarm of Drones

et al. 2022

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This paper addresses the time-varying rendezvous problem of a swarm of drones with the leader-follower consensus hierarchy. Each drone in the swarm is under the influence of external disturbances in the form of wind gusts. To control the swarm of perturbed drones, this paper proposes a fully distributed adaptive leader-following time-varying disturbance rejection pinning control for the rendezvous of drones using the framework of the extended state observer that depends on the state estimates of the neighboring agents rather than the actual states. The communication topology within the swarm is modelled using algebraic graph theory. Unlike most of the literature in which the leader agent act as a virtual reference generator, an active leader agent with non-zero control input is considered. The states of the leader drone are only available to a subset of the graph. The communication topology between leader-follower is directed while that between follower-follower is undirected. Based on the relative output information between the neighboring drones, the extended state observer estimates the local states and disturbances of each drone and the adaptive control actively compensates for the external disturbances simultaneously. Finally, it is shown analytically as well as in simulations that the time-varying rendezvous can be achieved by the proposed algorithm effectively.

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“…The algorithms are derived from artificial neural networks [22,23], human-based logic [24,25] and fuzzy logic [26,27]. The model-based nonlinear flight control algorithms include sliding mode control [28][29][30][31][32], backstepping control [33,34], visual-based techniques [35,36] and Lyapunov-based methods [2,37,38], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Autonomous control of multiple quadrotors for collision‐free navigation

Raj

Raghuwaiya

Havea

et al. 2023

IET Control Theory & Appl

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This paper addresses the development of a set of novel autonomous control laws for the navigation of multiple mini or micro quadrotors in a workspace populated with cylindrical obstacles. To the authors' knowledge, this is the first time that such a set of control inputs for the autonomous control of multiple quadrotors and avoidance of cylindrical obstacles is being derived from a single Lyapunov function. The avoidance of the cylindrical obstacles is achieved via a Minimum Distance Technique that allows a quadrotor to avoid the closest point on the curved surface of a cylinder at every unit time. In addition, the novel controllers ensure a near-to-horizontal orientation of a quadrotor at every unit time and the hovering motion exhibited by the quadrotor in a neighborhood of its target. The hovering capability has been solved completely in this paper by solving the underactuatedness of the quadrotor which allows for the design of the maximum translational velocities needed for vertical take-off and landing, and hovering. This is important in applications where payloads are sensitive to acrobatic orientations. Computer simulations that mimic real-life scenarios using cylindrical towers as obstacles in a city-like environment illustrates the effectiveness of the controllers.

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Observer-Based Time-Varying Backstepping Control for a Quadrotor Multi-Agent System

Cited by 23 publications

References 16 publications

Aportaciones al control de vehículos aéreos no tripulados en México

Aportaciones al control de vehículos aéreos no tripulados en México

Adaptive Active Disturbance Rejection Control for Rendezvous of a Swarm of Drones

Autonomous control of multiple quadrotors for collision‐free navigation

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