基于改进微粒群算法的无人机姿态控制参数智能整定

摘要

提出了基于改进微粒群算法的无人机姿态控制器参数智能整定方法.标准微粒群算法在搜索后期由于群体缺乏多样性而容易出现收敛停滞现象,为此提出了一种改进的微粒群算法.标准微粒群算法中的微粒速度是根据惯性运动、群体历史最优位置和自身历史最优位置来调节的.改进微粒群算法中的微粒除了保持惯性运动外,仅向当前群体中任意更优个体的状态学习,而且惯性权重系数是随机数.改进方案减少了算法不确定参数,简化了微粒学习机制,且增强了群体多样性.本文构建了无人机姿态控制系统,将改进微粒群算法用于四个控制参数的寻优整定.仿真结果表明,改进微粒群算法比一般微粒群算法具有更强的全局搜索能力,故获得更优的无人机姿态控制参数.%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.