Leader-Following Consensus and Formation Control of VTOL-UAVs with Event-Triggered Communications

Guerrero-Castellanos, J.F.; Vega-Alonzo, A.; Durand, Sylvain; Marchand, Nicolas; González-Díaz, Víctor R.; Castañeda-Camacho, J.; Guerrero-Sánchez, W. F.

doi:10.3390/s19245498

Cited by 22 publications

(16 citation statements)

References 40 publications

(67 reference statements)

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“…In [58], r is considered as a disturbance because of the absence of sensors in the motors, thus, it is ignored for the design of controllers. According to [61] and [62], a fleet of UVAs can be represented as a double integrator MAS manipulating the angles of the UAV with the following references:…”

Section: Numerical Example: a Fleet Of Uavs With Communication Faultsmentioning

confidence: 99%

Robust Formation Control Based on Leader-Following Consensus in Multi-Agent Systems With Faults in the Information Exchange: Application in a Fleet of Unmanned Aerial Vehicles

et al. 2021

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In this paper, a robust leader-following consensus protocol for multi-agent systems (MASs) subject to faults in the information exchange and disturbance is presented. The problem under consideration is to guarantee the convergence of the agent trajectories to a leader agent when all the agent followers are under faults in the information exchange as smooth time-varying delays and disturbances. The main contribution in this paper is the design of a robust leader-following control through the Lyapunov approach and an optimal H ∞ criterion such that all agents follow a virtual leader agent despite faults in the information exchange and disturbances. Linear matrix inequalities (LMIs)-based conditions are obtained whose solution allows computing the robust controller gain. In order to show the effectiveness of the proposed approach, numerical examples are carried out comparing a state-of-the-art approach and the proposed strategy in a fleet of unmanned aerial vehicles (UAVs) subject to wind turbulence which are shown to achieve the formation control.INDEX TERMS Consensus, communication faults, leader-following consensus, multi-agent systems.

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Section: Numerical Example: a Fleet Of Uavs With Communication Faultsmentioning

confidence: 99%

Robust Formation Control Based on Leader-Following Consensus in Multi-Agent Systems With Faults in the Information Exchange: Application in a Fleet of Unmanned Aerial Vehicles

et al. 2021

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“…Feasible formation control methods mainly include: leader follower, virtual structure and behavior-based method [1]. Based on the event triggering control strategy of internal and external loop control, the consistency and formation of VTOL UAV cluster are solved [2]. Through the design of the intermediate attitude composed of the desired attitude and position information, an attitude based coupled trajectory tracking scheme is completed to realize the trajectory tracking and formation tracking of the VTOL UAV [3].…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Sun

Huang

et al. 2020

IEEE Access

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In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.INDEX TERMS Cluster control, virtual core algorithm, cluster reconstruction, track planning, improved APF

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“…The UAV, as the research object of RL algorithms, has attracted the attention of many researchers [5,6,20]. Multi-UAV formation can accomplish many tasks that cannot be completed by a single UAV [21,22]. This paper proposes the Multiagent Joint Proximal Policy Optimization (MAJPPO) algorithm, which uses the moving window average of the state-value functions of different agents to get the centralized state-value function to solve the problem of multi-UAV cooperative control.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance

Zhao

Chu

Miao

et al. 2020

Sensors

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Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.

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Leader-Following Consensus and Formation Control of VTOL-UAVs with Event-Triggered Communications

Cited by 22 publications

References 40 publications

Robust Formation Control Based on Leader-Following Consensus in Multi-Agent Systems With Faults in the Information Exchange: Application in a Fleet of Unmanned Aerial Vehicles

Robust Formation Control Based on Leader-Following Consensus in Multi-Agent Systems With Faults in the Information Exchange: Application in a Fleet of Unmanned Aerial Vehicles

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance

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