基于改进微粒群算法的飞机纵向飞行轨迹优化  

Aircraft Trajectory Optimization in Vertical Flight Profile Based on Improved Particle Swarm Optimization Algorithm

作　　者：朱思斌[1] 李瑰贤[1] 韩俊伟[1] 

机构地区：[1]哈尔滨工业大学机电工程学院,黑龙江哈尔滨150001

出　　处：《江南大学学报（自然科学版）》2012年第2期163-168,共6页Joural of Jiangnan University (Natural Science Edition)　

基　　金：教育部新世纪优秀人才支持计划项目(NCET-04-0325)

摘　　要：为提高飞机纵向飞行轨迹优化的精度和收敛速度,提出了用改进的微粒群算法对飞机纵向飞行轨迹进行优化的新方法。基于质点动力学和能量状态方程,建立了飞机质点运动数学模型;利用庞特里亚金最小值原理,给出了飞机纵向飞行过程优化的目标方程;引入自适应惯性因子,采用罚函数法对轨迹寻优问题进行无约束化处理,基于改进的微粒群算法对纵向飞行轨迹进行了优化,并给出了算法优化流程。使用改进的微粒群算法,得到了Boeing 737-800飞机纵向飞行最优轨迹。优化结果与试验结果的比较表明,该算法可使纵向飞行轨迹快速收敛于最优解,算法具有收敛速度快、精度高的优点。In order to enhance accuracy and convergence speed for aircraft vertical flight trajectory,an improved particle swarm optimization（PSO） algorithms for flight trajectory optimization is proposed.The point-mass motion mathematical model is built based on point-mass dynamics and energy states.Objective functions for trajectory optimization in vertical flight profile are acquired through Pontryagin minimum principle.Adaptive inertia weight is introduced,the equality constraints is processed using the penalty function method.Trajectory in vertical flight profile is optimized based the improved PSO.The PSO algorithm flow with trajectory optimal in vertical flight profile is finally given.Making use of improved PSO,trajectory optimization of Boeing 737-800 aircraft in vertical flight profile is carried out.Comparison results between optimization results and experiment data show the proposed algorithm converging to optimal solution rapidly.It has merits of fast convergence speed and high precision.

关 键 词：改进微粒群算法 飞行轨迹优化 最小值原理 局部最优解 飞行模拟器 收敛速度 计算精度 罚函数法 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置] V247.19[自动化与计算机技术—控制科学与工程]