Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

Abstract

Quadrotor Unmanned Aerial Vehicles (UAVs) are generally underactuated systems and when load is attached for transportation purposes, the system complexity increases. Therefore, the need to appropriately control such systems becomes paramount as they usually navigate in cluttered environments. In this work, we conceptualize the problem of cooperative tracking control for a Multi-agent UAV load system (MUAVLs) whereby each UAV is divided into global position and local attitude subsystems. To ensure that formation is maintained in a desired path, Neural Network Graph-theoretic Distributed Adaptive Control (NNGDAC) is used for the position subsystem with a modified virtual force artificial potential field for obstacle avoidance. Another Adaptive Feedback Linearization (AFBL) controller is also designed for the attitude subsystem which is verified by simulation results.

Modeling, Guidance, and Robust Cooperative Control of Two Quadrotors Carrying a “Y”-Shaped-Cable-Suspended Payload

“…In [10], the desired trajectory for the payload is seen as a virtual leader, while the two quadrotors act as followers to track the virtual leader's trajectory in a formation. In contrast, by sharing a global yaw angle reference, neural network graph-theoretic distributed adaptive control is proposed in [11] to ensure that formation is maintained in a desired path in a leader-follower manner. Unlike in [11], where the quadrotors share global references, Ref.…”

“…In contrast, by sharing a global yaw angle reference, neural network graph-theoretic distributed adaptive control is proposed in [11] to ensure that formation is maintained in a desired path in a leader-follower manner. Unlike in [11], where the quadrotors share global references, Ref. [12] assumes that only the leader quadrotor has access to the global reference, while the other quadrotor employs a PID-like controller to follow the leader at a constant distance.…”

Adaptive Backstepping Multi-Agent Control of Multiple UAVs Conveying Load: A Graph Rigidity Approach

“…MUAVLs modeling are vastly available in different formulations based on Udwadia Kalaba [19], Lagrange model [26], Kane's [27], and Newton-Euler [31], [44]. In this section, we employ the Newton-Euler model adopted in [7] because it allows for analysis of each UAV separately.…”

“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ by connectedness (Graph Laplacian) [7], [8], [9], [10], [11]. Thus, in this work, we employ the benefits of graph rigidity theory to establish autonomous flight for the MUAVLs translational subsystem.…”

Dynamics and control of dual-quadrotors slung a slender beam with attitude-pendulum coupling effects

“…Mounting numbers of literatures reported multiple (dual or more) quadrotors transporting a load (Aliyu and Ferik, 2022; Dhiman et al, 2020; Li et al, 2021; Mohammadi et al, 2020; Yu et al, 2022b). However, the suspension cables were assumed to be attached at the mass centers of the quadrotors in above-mentioned literatures.…”