Reentry trajectory rapid optimization for hypersonic vehicle satisfying waypoint and no-fly zone con

来源 :Journal of Systems Engineering and Electronics | 被引量 : 0次 | 上传用户:summerweixi
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To rapidly generate a reentry trajectory for hypersonic vehicle satisfying waypoint and no-fly zone constraints, a novel optimization method, which combines the improved particle swarm optimization(PSO) algorithm with the improved Gauss pseudospectral method(GPM), is proposed. The improved PSO algorithm is used to generate a good initial value in a short time,and the mission of the improved GPM is to find the final solution with a high precision. In the improved PSO algorithm, by controlling the entropy of the swarm in each dimension, the typical PSO algorithm’s weakness of being easy to fall into a local optimum can be overcome. In the improved GPM, two kinds of breaks are introduced to divide the trajectory into multiple segments, and the distribution of the Legendre-Gauss(LG) nodes can be altered, so that all the constraints can be satisfied strictly. Thereby the advantages of both the intelligent optimization algorithm and the direct method are combined. Simulation results demonstrate that the proposed method is insensitive to initial values, and it has more rapid convergence and higher precision than traditional ones. The improved PSO (PSO) algorithm with improved Gauss pseudospectral method (GPM) is proposed. The improved PSO algorithm is used to generate a good initial value in a short time, and the mission of the improved GPM is to find the final solution with a high precision. In the improved PSO algorithm, by controlling the entropy of the swarm in each dimension, the typical PSO algorithm’s weakness of being easy to fall into a local optimum can be overcome. In the improved GPM, two kinds of breaks are introduced to divide the trajectory into multiple segments, and the distribution of the Legendre-Gauss (LG) nodes can be altered, so that all the constraints can be exactly 严. the advantages of both the intelligent optimization algorithm and the direct method are combined. Simulation results demonstr ate that the proposed method is insensitive to initial values, and it has more rapid convergence and higher precision than traditional ones.
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