LAUSR

Trajectory optimization problem for hypersonic vehicles has received wide attention as its high speed and large flight range. The strong nonlinear characteristic of the ascent phase aerodynamics makes the trajectory optimization problem difficult to be solved by the optimal control theory. In this paper, an improved chicken swarm optimization (ICSO) algorithm is proposed to optimize the hypersonic vehicle ascent trajectory. To overcome the obstacle of premature convergence, three improvement strategies are put forward. To be specific, the updating laws of roosters are modified by the average position of roosters, and the difference of the optimal solution between two adjacent iterations is used to calculate the mutated particle instead of the gradient. Meanwhile, the uniform mutation operator is used to get rid of the local minimum. The convergence analysis of the proposed ICSO is provided subsequently. To handle constraints, an improved adaptive penalty method is put forward. The comparison results show that the proposed ICSO outperforms CSO and PSO on benchmark functions of CEC2014. Finally, the trajectory optimization results for a generic hypersonic vehicle, in compare with the open-source optimization software PSOPT, are put forward to demonstrate the feasibility and effectiveness of the proposed method. The results of 50 independent runs show that the improved adaptive penalty function method is effective in constraints handling.