Position trajectory tracking of a quadrotor helicopter based on L1 adaptive control

Monte, Paul De; Lohmann, Boris

doi:10.23919/ecc.2013.6669410

Cited by 27 publications

(18 citation statements)

References 16 publications

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“…Ohne die Trennung der Adaption von der nominalen Regelung wäre dies nicht möglich, denn das Signal σ n würde nicht existieren und dessen Informationsgehalt müsste durch die Adaption erzeugt und von C(s) gefiltert werden. Dies entspräche einer klassischen Anwendung der L 1 -adaptiven Regelung [21,42], welche hier bewusst umgangen weden soll, um eine niedrige Bandbreite vonĈ(s) und gleichzeitig eine hohe Flugdynamik zu ermöglichen.…”

Section: Zustandsraummodellunclassified

Adaptive Trajektorienfolgeregelung für Quadrokopter basierend auf der L1-adaptiven Regelungstheorie

Monte¹,

Sebastian²

2015

At - Automatisierungstechnik

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ZusammenfassungDiese Arbeit stellt eine adaptive Backsteppingregelung zur Trajektorienfolge für die Position und das Heading eines Quadrokopters vor. Der Regelalgorithmus ist dafür entwickelt, schnelle Flugmanöver zu realisieren und alle Modellunsicherheiten sowie eine Vielzahl an Störungen zu kompensieren. Dazu greift der Entwurf einen Ansatz der L1-adaptiven Regelung für nichtlineare und zeitvariante Referenzsysteme auf, passt ihn an das Backsteppingschema an und modifiziert ihn, so dass die nominale Regelung von der Adaption entkoppelt. Es werden zwei Nachweise geführt und verglichen, welche die Beschränktheit aller Systemsignale zeigen. Simulationen und Experimente verifizieren den vorgestellten Ansatz und veranschaulichen dessen hohe Leistungsfähigkeit.

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Section: Zustandsraummodellunclassified

Adaptive Trajektorienfolgeregelung für Quadrokopter basierend auf der L1-adaptiven Regelungstheorie

Monte¹,

Sebastian²

2015

At - Automatisierungstechnik

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“…Over the last decade, there has been a number of studies on this topic with several algorithms introduced. The popular ones include A * , D * , Rapidly Exploring Random Tree (RRT) and Probabilistic Roadmap Method (PRM) [9]- [11]. The advantage of the A * and D * algorithms lie in the ability to judge or evaluate the best point-to-point path so they can provide a flyable trajectory for UAVs [9].…”

Section: Introductionmentioning

confidence: 99%

“…The popular ones include A * , D * , Rapidly Exploring Random Tree (RRT) and Probabilistic Roadmap Method (PRM) [9]- [11]. The advantage of the A * and D * algorithms lie in the ability to judge or evaluate the best point-to-point path so they can provide a flyable trajectory for UAVs [9]. The RRT and PRM are based on probabilistic reasoning, and hence, robust but they require the discretization of the operating space which affects the smoothness of the planned trajectory [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Angle-Encoded Swarm Optimization for UAV Formation Path Planning

Hoang

Phung

Dinh

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (UAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of UAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angleencoded Particle Swarm Optimization (θ-PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of-Things (IoT) for multi-directional transmission and reception of data between the UAVs, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach.

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“…The backstepping control scheme is a nonlinear control method based on the Lyapunov theorem. The backstepping control design techniques have received great attention because of its systematic and recursive design methodology for nonlinear feedback control [14][15][16][17][18][19][20][21]. Unlike the feedback linearization method with the problems such as the precise model requirement and the cancellation of useful nonlinear terms, the backstepping approach offers a choice of design tools for accommodation of nonlinearities, and can avoid unwanted cancellations.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Backstepping Controller Design with Application to Autonomous Quadrotor Unmanned Aerial Vehicle

Basri

Husain

Danapalasingam

2014

J Intell Robot Syst

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Quadrotor unmanned aerial vehicle (UAV) is an underactuated multi-input and multi-output (MIMO) system which has nonlinear dynamic behavior such as high coupling degree and unknown nonlinearities. It is a great challenge to design a quadrotor control system due to these features. In this paper, the contribution is focused on the backstepping-based robust control design of the quadrotor UAV. Firstly, the dynamic model of the aerial vehicle is mathematically formulated. Then, a robust controller is designed for the stabilization and tracking control of the vehicle. The developed robust control system comprises a backstepping and a proportional-derivative (PD) controller. Backstepping is a recursive design methodology that uses Lyapunov theorem which can guarantee the stability of the nominal model system, while PD control is used to attenuate the effects caused by system uncertainties. For the problem of determining the backstepping control parameters, particle swarm optimization (PSO) algorithm has been employed. In addition, the genetic algorithm (GA) technique is also adopted for the purpose of performance comparison with PSO scheme. Finally, the designed controller is experimentally evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development.

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Position trajectory tracking of a quadrotor helicopter based on L1 adaptive control

Cited by 27 publications

References 16 publications

Adaptive Trajektorienfolgeregelung für Quadrokopter basierend auf der L1-adaptiven Regelungstheorie

Adaptive Trajektorienfolgeregelung für Quadrokopter basierend auf der L1-adaptiven Regelungstheorie

Angle-Encoded Swarm Optimization for UAV Formation Path Planning

Enhanced Backstepping Controller Design with Application to Autonomous Quadrotor Unmanned Aerial Vehicle

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