Altitude consensus based 3D flocking control for fixed-wing unmanned aerial vehicle swarm trajectory tracking

Abstract

This paper studies the 3D flocking control problem for unmanned aerial vehicle swarm when tracking a desired trajectory. In order to allow the unmanned aerial vehicle swarm to form the stable flocking geometry on a same horizontal plane, the altitude consensus algorithm is applied to the unmanned aerial vehicle altitude control channel, using the trajectory altitude as the external input signals. The flocking control algorithm is only performed in the horizontal channel to control the horizontal position of unmanned aerial vehicles. The distributed tracking algorithm, which controls the local averages of position and velocity of each unmanned aerial vehicle, is implemented to achieve the better tracking performance. The improved artificial potential field method is introduced to achieve the smooth trajectory when avoiding obstacles. The practical dynamic and constraints of unmanned aerial vehicles are also taken into account. Numerical simulations are performed to test the performance of the proposed control algorithm.

Formation control of a second-order UAVs system under switching topology with obstacle/collision avoidance

“…And suppose that the interaction topology is switching when the information links are unreliable or failed at time t. Also The communication graph and corresponding Laplacian matrix are denoted by G σ (t) and L σ (t) , respectively. In this paper, based on [1], assuming that all UAVs flying at the same altitude, and a second-order UAVs system in the 2-D plane is addressed. The system dynamic can be denoted as (1).…”

“…The key point of the consensus problem is to design an algorithm/protocol that can guarantee all agents achieve consensus via local information exchange. A larger number of results have been obtained and widely utilized in trajectory tracking [1,2], formation control [3,4], and sensor networks [5,6].…”

Flocking Control of Fixed-Wing UAVs With Cooperative Obstacle Avoidance Capability

“…To apply the agent swarm algorithm to the UAV swarm, we must establish the relationship between the UAV motion model and the agent motion model, referring to previous work by Zhang and Duan [18]. Regarding the UAV swarm system movement in three-dimensional space, the position, velocity, and control vectors are given by…”

“…We must also set parameters and states for the UAV swarm, as shown in Table 2. Some of the data in Table 1 and Table 2 are from [5], [18], [26], and [27], and some are from repetitive simulation experiments.…”

“…Kownacki and Ołdziej [17] presented a novel approach to swarm control of small fixed-wing UAVs through cohesion and repulsion behaviors combined with leadership. Zhang and Duan [18] presented a 3D flocking control algorithm for tracking a desired trajectory with obstacle avoidance capability using the improved artificial potential field method. Li et al [19] studied the multi-agent system coordination obstacle avoidance algorithm using a variable structure method, wherein only some agents have dynamic information on obstacles and each agent has a local interaction.…”