Explicit Model Following Distributed Control Scheme for Formation Flying of Mini UAVs

Ali, Qasim; Montenegro, Sérgio

doi:10.1109/access.2016.2517203

Cited by 33 publications

(16 citation statements)

References 14 publications

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“…Thus, the control scheme is capable of variable set-point tracking as well. LQR PI control scheme based on model following is quite robust against different types of possible perturbations as shown in our previous study [16]. However, for the sake of brevity and clarity in plots, disturbances are not studied in this paper.…”

Section: Trajectory Tracking Performancementioning

confidence: 77%

“…Detailed motion dynamics of quadcopter are described in a number of publications, for example, [16]. For the sake of brevity, these are not repeated here.…”

Section: Quadcopter Dynamic Modelmentioning

confidence: 99%

“…LQR PI control scheme guarantees perfect tracking [16,22]. Although our derivation is based on step reference command, the resulting control system gives good time response for any arbitrary reference command signal ( ) [22], as will be demonstrated in simulation section.…”

Section: Control Strategiesmentioning

confidence: 99%

“…Portions of this work have been reported in our previous work [16]. However, for present paper, there are additional contributions like reference model that resides within the quadcopters to explicitly define the performance criteria for the quadcopters to make them behave like the model with desired dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Control for Scalable Quadcopter Formations

Ali

Montenegro

2016

International Journal of Aerospace Engineering

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An innovative framework has been developed for teamwork of two quadcopter formations, each having its specified formation geometry, assigned task, and matching control scheme. Position control for quadcopters in one of the formations has been implemented through a Linear Quadratic Regulator Proportional Integral (LQR PI) control scheme based on explicit model following scheme. Quadcopters in the other formation are controlled through LQR PI servomechanism control scheme. These two control schemes are compared in terms of their performance and control effort. Both formations are commanded by respective ground stations through virtual leaders. Quadcopters in formations are able to track desired trajectories as well as hovering at desired points for selected time duration. In case of communication loss between ground station and any of the quadcopters, the neighboring quadcopter provides the command data, received from the ground station, to the affected unit. Proposed control schemes have been validated through extensive simulations using MATLAB5/Simulink5 that provided favorable results.

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Section: Trajectory Tracking Performancementioning

confidence: 77%

“…Detailed motion dynamics of quadcopter are described in a number of publications, for example, [16]. For the sake of brevity, these are not repeated here.…”

Section: Quadcopter Dynamic Modelmentioning

confidence: 99%

Section: Control Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decentralized Control for Scalable Quadcopter Formations

Ali

Montenegro

2016

International Journal of Aerospace Engineering

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“…In the area of quadcopter literature, there is a variety of applications as aerial manipulation [1,2], quadcopter pendulum [3], navigation and localization [4,5], obstacle avoidance [6], altitude control [7], and cooperative and formation control [8,9]. Moreover, several control schemes have been proposed including adaptive control [10][11][12][13], fuzzy control [14], neural network control [15], linear parameter varying (LPV) control [16], predictive control [17,18], nonlinear control methods [19][20][21][22][23], and sliding mode control [24,25].…”

Section: Introductionmentioning

confidence: 99%

Advanced UAVs Nonlinear Control Systems and Applications

Joukhadar¹,

Alchehabi²,

Jejeh³

2020

Unmanned Robotic Systems and Applications

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Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB ® /Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results.

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Obstacle Avoidance for Quadcopters in Formation Flying Based on A* Algorithm

Roy

2023

Lecture Notes in Civil Engineering

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Explicit Model Following Distributed Control Scheme for Formation Flying of Mini UAVs

Cited by 33 publications

References 14 publications

Decentralized Control for Scalable Quadcopter Formations

Decentralized Control for Scalable Quadcopter Formations

Advanced UAVs Nonlinear Control Systems and Applications

Obstacle Avoidance for Quadcopters in Formation Flying Based on A* Algorithm

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