基于改进粒子群算法的再入飞行器轨迹优化

摘要

A method using Partical Swarm Optimization(PSO) is applied to reentry vehicle trajectory optimization, the method based on distance measures and adaptive penalty functions avoids the complex design for penalty functions and penalty factors, and develops the generality of the algorithm. For the minimum control energy reentry trajectory optimization for hypersonic glide vehicles, model is simplified, control variable is parameterized. Two different hypersonic vehicles are optimized by the method. The effectiveness and generality of the method are demonstrated by simulation results.%采用基于距离量度和自适应惩罚相结合的约束处理技术的改进粒子群优化算法(PSO)应用于再入飞行器轨迹优化,避免适应值函数中复杂的罚函数及罚因子的设计,提高优化算法的通用性.以高超声速飞行器最小控制量再入轨迹优化为例,并对飞行器运动模型进行简化及控制量参数化.对两种不同的高超声速飞行器模型进行优化,仿真结果验证算法的有效性及通用性.