基于粒子群优化算法的飞行成本最优轨迹优化

Best Flight Cost Trajectory Optimization Based on Particle Swarm Optimization Algorithm

以某大型民用飞机为例,应用了以飞行成本最优为目标的性能指标,同时运用最优控制理论,推导出飞机纵向飞行过程(爬升-巡航-下降)的优化方程,使用粒子群算法进行全局寻优,并通过Matlab进行了仿真,实现了以最优成本为目标的轨迹优化。仿真结果表明:用粒子群算法进行全局寻优,寻优效率较高,考虑时间成本后的轨迹,与节油轨迹相比,空速有所增加,耗油量也有所增加,但飞行时间大大缩短,为航空公司降低运营成本提供帮助。

Taking a certain large aircraft as example,the performance index was applied to which was based on the best flight cost.At the same time,the theory of optimal control is used to derive the optimization equation of aircraft＇s longitudinal flight process（climb-cruise-descent）.The global optimization has been achieved with a particle swarm optimization algorithm,and realizes the goal of an optimal cost trajectory by matlab.The simulation results indicate that the particle swarm optimization algorithm＇s ability in the global optimization is efficient.We can see form the time-cost trajectory,which is compared with the fuel-optimal trajectory,that the airspeed and fuel consumption is increased,but the flight-time is shortened largely,which can help the airline company reduce the operator cost.