Multi-Layered Optimal Navigation System For Quadrotors UAV

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Multi-Layered Optimal Navigation System For Quadrotors UAV. / Choutri, Kheireddine; Lagha, Mohand; Dala, Laurent.

In: Aircraft Engineering and Aerospace Technology, Vol. 92, No. 2, 21.10.2019, p. 145-155.

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Choutri, Kheireddine ; Lagha, Mohand ; Dala, Laurent. / Multi-Layered Optimal Navigation System For Quadrotors UAV. In: Aircraft Engineering and Aerospace Technology. 2019 ; Vol. 92, No. 2. pp. 145-155.

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@article{910e64bd24cd4d8d8dae7f5283176b55,
title = "Multi-Layered Optimal Navigation System For Quadrotors UAV",
abstract = "PurposeThis paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV).Design/methodology/approachThe proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS).FindingsAll the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system.Practical implicationsThe proposed controllers are easily implementable on-board and are computationally efficient.Originality/valueThe originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state{\textquoteright}s cost functions jointly.",
keywords = "Quadrotors, UAV, Optimization, Control, Trajectory generation and Differential Flatness",
author = "Kheireddine Choutri and Mohand Lagha and Laurent Dala",
year = "2019",
month = oct,
day = "21",
doi = "10.1108/aeat-12-2018-0313",
language = "English",
volume = "92",
pages = "145--155",
journal = "Aircraft Engineering and Aerospace Technology",
issn = "1748-8842",
publisher = "Emerald",
number = "2",

}

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TY - JOUR

T1 - Multi-Layered Optimal Navigation System For Quadrotors UAV

AU - Choutri, Kheireddine

AU - Lagha, Mohand

AU - Dala, Laurent

PY - 2019/10/21

Y1 - 2019/10/21

N2 - PurposeThis paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV).Design/methodology/approachThe proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS).FindingsAll the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system.Practical implicationsThe proposed controllers are easily implementable on-board and are computationally efficient.Originality/valueThe originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly.

AB - PurposeThis paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV).Design/methodology/approachThe proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS).FindingsAll the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system.Practical implicationsThe proposed controllers are easily implementable on-board and are computationally efficient.Originality/valueThe originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly.

KW - Quadrotors

KW - UAV

KW - Optimization

KW - Control

KW - Trajectory generation and Differential Flatness

UR - http://www.scopus.com/inward/record.url?scp=85074412708&partnerID=8YFLogxK

U2 - 10.1108/aeat-12-2018-0313

DO - 10.1108/aeat-12-2018-0313

M3 - Article

VL - 92

SP - 145

EP - 155

JO - Aircraft Engineering and Aerospace Technology

JF - Aircraft Engineering and Aerospace Technology

SN - 1748-8842

IS - 2

ER -