基于改进粒子群算法的无人机爬升轨迹优化

Climb Trajectory Optimization of UAV Based on Improved Particle Swarm Optimization

研究无人机控制优化爬升性能问题,由于单独提高速度或节省燃油问题,均存在互相影响。为了使无人机能够快速、省油地爬升到预定高度,综合考虑了油耗和时间这两个因素。在分析了无人机爬升段数学模型的基础上,提出将油耗和时间的综合运营成本作为优化指标,并提出了一种改进粒子群算法的无人机爬升轨迹优化方法。将无人机轨迹优化问题转化为有约束的参数优化问题,并用改进粒子群算法进行参数优化,从而得到综合指标最优的爬升轨迹。对某无人机实例进行爬升轨迹优化,仿真结果比传统方法更节省了运营成本,证明了改进方法的优越性。

In order to achieve the desired height economically and quickly,this paper considered the two factors of fuel cost and time.First,the paper studied the mathematical model of UAV climb trajectory optimization,and then made the total cost of fuel and time as the performance index.Second,this paper proposed a method of Unmanned Aerial Vehicle（UAV） climb trajectory optimization based on particle swarm optimization（PSO） and turned the problem of UAV trajectory optimization into the problem of constrained parameter optimization,and found the optimal parameters using PSO.In the end,validation was made with a UAV,and the result turned to be saving more operational costs,which proves the method is better.