Multi-layered optimal navigation system for quadrotor UAV

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.

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

“…Based on Lyapunov stability analysis, the position and velocity control laws can be derived as indicated in [ 26 ] as follows: where is the position error, is the velocity error, and , are positive constant coefficients.…”

“…Ref. [ 25 ] suggests a second-order consensus algorithm to follow a specified external reference in their work, whereas [ 26 ] frames the formation control problem as a position control problem to be solved. In contrast, spacecraft formation vehicles use a mechanism for robust attitude control, as described in [ 23 ].…”

Improved radial basis function artificial neural network and exact-time extended state observer based non-singular rapid terminal sliding-mode control of quadrotor system

“…rescue operations, monitoring, agriculture, photography and live-streaming, etc. (Choutri et al, 2020). It has the phenomenal quality of vertical take-off and landing, agility, low cost, smaller size and simple architecture (Shi et al, 2018a(Shi et al, , 2018bWang et al, 2020).…”

Improved particle filter-based estimation of a quadrotor subjected to uncertainties

“…Based on this flexibility, UAVs can be used in risky tasks without any direct human intervention (Beharie et al , 2015; Unal, 2021). Quadrotors are four-rotor-based UAVs that are widely used by the communities due to their advantages such as hover, low-speed flight and vertical takeoff and landing (Choutri et al , 2020). However, quadrotors are prone to coupling between the states, dynamic nonlinearities, uncertainties and unstable open-loop characteristics (Camci et al , 2018).…”