Abstract
This paper proposes an algorithm of leader-following consensus control of multiple fixed-wing unmanned aerial vehicles’(UAVs) attitudes with time delays and unknown external disturbances. Firstly, a distributed controller based on undirected graph for leader-following consensus control of multi-UAVs’ attitudes is proposed. Then, the effects of time delays are compensated with the help of Lyapunov-Krasovskii function, Cauchy and Young’s inequality. Secondly, RBF neural network is introduced to estimate and reduce the negative effects of external disturbances. Finally, the effectiveness of the proposed distributed controller is demonstrated by Lyapunov theory and simulations.
Highlights
In recent decades, multiple unmanned aerial vehicles system control has become an attractive and active topic because of its wide applications in various fields
The fundamental issue of the multi-agent system is the consensus problem [6]–[8], generally speaking, the purpose of consensus control of a multi-agent system is that all agents that will be synchronized to reach a common state by a control protocol based on the neighbour agents’ information [9], [10], which can be divided into two classes that are leaderless consensus and leader-following consensus [11]–[13]
The neural network (NN) technique can be a powerful tool of approximating arbitrary nonlinear functions, in particular, radial basis function (RBF) neural network is widely used for its simple network structure and effectiveness in estimating nonlinear functions
Summary
Multiple unmanned aerial vehicles (multiUAVs) system control has become an attractive and active topic because of its wide applications in various fields. Searching and rescuing targets in a large area [1], [2], drawing a map for a large field [3] and completing aircrafts’ aerial refuelling mission by multi-UAVs’ team cooperation [4], [5]. It could be difficult to construct the consensus controller with time delays and unknown nonlinear disturbances and functions. In [12]–[15], the scheme of NN-based distributed control has been designed to compensate the nonlinear unknown parts or disturbances, but none of them consider the uncertainties of time delays. Large time delays can damage stability of multi-UAVs’ system and increase the difficulty for neural network to learn the unknown functions, in [16], [17], by applying Lyapunov–Krasovskii function, Cauchy and Young’s inequality, the time delays in the multi-agent system were compensated
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