Abstract
In this paper, a biologically-inspired distributed intelligent control methodology is proposed to overcome the challenges, i.e., networked imperfections and uncertainty from the environment and system, in networked multi-Unmanned Aircraft Systems (UAS) flocking. The proposed method is adopted based on the emotional learning phenomenon in the mammalian limbic system, considering the limited computational ability in the practical onboard controller. The learning capability and low computational complexity of the proposed technique make it a propitious tool for implementing in real-time networked multi-UAS flocking considering the network imperfection and uncertainty from environment and system. Computer-aid numerical results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm for distributed intelligent flocking control of networked multi-UAS.
Highlights
IntroductionDistributed coordination of networked multi-Unmanned Aircraft Systems (UAS) has been studied by diverse research communities in recent years [1,2,3,4]
A total of 50 unmanned aircraft and 150 unmanned ground vehicles (UGVs) where employed, with initial velocities equal to zero, and positions randomly distributed in a squared area
The challenges of network-induced delay in flocking of networked multi-unmanned aircraft systems have been studied in this paper
Summary
Distributed coordination of networked multi-Unmanned Aircraft Systems (UAS) has been studied by diverse research communities in recent years [1,2,3,4]. Several research groups have been contributed for improving the flocking behavior of networked multi-UAS in recent years [9,10,11,12]. In many applications involving networked multi-UAS, require communicating in order to successfully accomplish their assigned tasks. It is of paramount importance to address the challenges of network-induced delay and taking the influence of network-induced delays into account in designing control algorithms for multi-unmanned aircraft systems. The uncertainty from the complex environment and system dynamics is another critical challenge and cannot be ignored in advanced applicable control development. It is important to consider the uncertainties from the environment and system in designing control algorithms
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