Ascent trajectory optimization of hypersonic vehicle based on improved Particle Swarm algorithm

By introducing the adaptive inertia weight, the time factor and the structure rebuilding of Particle Swarm Optimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence speed, a PSO strategy is proposed, which consists of the dynamic population structure, opposition-based learning, crossover operator and variable step integral. Combining the improvement of PSO and the optimization strategy, the modified particle swarm optimization (MPSO) algorithm is formed. The MPSO is applied to optimize the ascent trajectory of hypersonic vehicle. The precision and efficiency of this trajectory optimization method are demonstrated by comparing the results of PSO and MPSO. The simulation results show that the performance of MPSO is significantly superior to PSO either convergence speed or convergent accuracy.