Abstract
In this paper we proposed the circle trajectory assembly algorithm to control the multi-UAVs circular assembly formation. CTFAP solution provides rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance. Proposed Distributed model prediction control framework improves the optimization ability and reduces the computation consumption with the better convergence ability of the UAV formation. Firstly, a circular trajectory following algorithm with an adaptive parameter is proposed to complete the rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance during formation forming. Then, in the stage of formation reconfiguration, with distributed model prediction control framework (DMPC), the proposed method gets the prediction information of DMPC to optimize the population of classical differential evolution (DE) algorithm and improve the iterative optimization ability of DE algorithm. Experiments show that the proposed differential evolution algorithm greatly improves the efficiency of solving the formation reconfiguration problem under the DMPC framework and overcomes the disadvantages of random population of classical DE. For the proposed rapid forming method, assembly range is reduced by 41% compared with direct linearly formation assembly, and the formation forming time is reduced by approximately 21%. Compared with other optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and DE, the proposed differential evolution algorithm reduces instruction response time of single-drones by 16%–30%.
Highlights
Multi-UAV formation flying includes both the assembly, maintenance and reconstruction of formation flights, as well as the planning and organization of flying tasks [1]
Ru [12] et al designed a Distributed Model Predictive Control (DMPC) method based on Nash bargaining to solve the reconfiguration function of the multi-UAV formation
Formula (3-4) shows that under the framework of DMPC, the optimization in each time domain is only related to the status Xi(k) and the control set UiN (k) of the either UAV, the state x(k − 1), the forecast information of all UAVs at last moment received by the network
Summary
Multi-UAV formation flying includes both the assembly, maintenance and reconstruction of formation flights, as well as the planning and organization of flying tasks [1]. Ru [12] et al designed a Distributed Model Predictive Control (DMPC) method based on Nash bargaining to solve the reconfiguration function of the multi-UAV formation. It does not take the communication distance constraint of UAVs into the formation mathematical model. Zhang [13] et al adopted an improved differential evolution algorithm to solve the optimal control variables in the multi-UAV global reconfiguration process, and considered various constraints based on the realworld scenarios. This algorithm has a large amount of computing burden to solve the control variables. The method can reduce the dispersion interval of the multi-UAV formation, guarantee the communication performance between the UAVs, and provide a good initial formation for the formation reconfiguration
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