Multi-Layered Optimal Navigation System For Quadrotors UAV

Research output: Contribution to journalArticle

Authors

External departments

  • University of Blida

Details

Original languageEnglish
JournalAircraft Engineering and Aerospace Technology
Early online date21 Oct 2019
DOIs
Publication statusE-pub ahead of print - 21 Oct 2019
Publication type

Research output: Contribution to journalArticle

Abstract

Purpose
This 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/approach
The 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).

Findings
All 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 implications
The proposed controllers are easily implementable on-board and are computationally efficient.

Originality/value
The 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.