南京航空航天大学学报（英文版）2009，Vol.26Issue(1)：52-57,6.
基于改进微粒群算法的无人机姿态控制参数智能整定
IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR INTELLIGENTLY SETTING UAV ATTITUDE CONTROLLER PARAMETERS
摘要
Abstract
An improved particle swarm optimization (PSO) algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle (UAV). Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global searching ability than the common PSO algorithms, and obtains better UAV attitude control parameters.关键词
无人机/姿态控制/微粒群算法Key words
unmanned aerial vehicle/attitude control/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
浦黄忠,甄子洋,王道波,胡勇..基于改进微粒群算法的无人机姿态控制参数智能整定[J].南京航空航天大学学报（英文版）,2009,26(1):52-57,6.基金项目
江苏省普通高校研究生科研创新计划(CX08B-091Z)资助项目 （CX08B-091Z）
南京航空航天大学博士学位论文创新与创优基金(BCXJ08-06)资助项目.Supported by the Graduate Student Research Innovation Program of Jiangsu Province (CX08B-091Z) （BCXJ08-06）
the Innovation and Excellence Foundation of Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ08-06). （BCXJ08-06）
