This paper proposes a system framework for solving the problem of multi-UAV cooperative task assignment and track planning for ground moving targets. For the combinatorial optimization model, it is solved by a new particle swarm optimization algorithm based on guidance mechanism. In order to plan an effective track for the target more rapidly, a new ant colony optimization algorithm based on adaptive parameter adjustment and bidirectional search is proposed. Furthermore, in the case of target movement, a method of the predicted meeting point is proposed to solve the problem that the moving point cannot be used as the target point of the track planning algorithm. In addition, the track planning problem in the UAV tracking mode is also considered. An online re-planning method is proposed for time-sensitive uncertainties. Finally,the simulation results show that compared with other algorithms, the proposed method can not only effectively plan a reasonable track, but also solve the uncertainty problem, and obtain the optimal task allocation plan, which improves the multi-UAV cooperative combat capability.
We propose a new approach to discuss the consensus problem of multi-agent systems with time-varying delayed control inputs, switching topologies, and stochastic cyber-attacks under hybrid-triggered mechanism. A Bernoulli variable is used to describe the hybrid-triggered scheme, which is introduced to alleviate the burden of the network. The mathematical model of the closed-loop control system is established by taking the influences of time-varying delayed control inputs, switching topologies, and stochastic cyber-attacks into account under the hybrid-triggered scheme. A theorem as the main result is given to make the system consistent based on the theory of Lyapunov stability and linear matrix inequality. Markov jumps with uncertain rates of transitions are applied to describe the switch of topologies. Finally, a simulation example demonstrates the feasibility of the theory in this paper.
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