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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 intelligent parameter setting method for UAV attitude controller based on improved particle swarm optimization is proposed. Standard Particle Swarm Optimization (PSO) tends to converge and stagnate due to the lack of diversity in the late search stage. An improved Particle Swarm Optimization (PSO) algorithm is proposed. The particle velocity in the standard particle swarm algorithm is adjusted based on the motion of inertia, the optimal position of the group history and the optimal position of its own history. Particle swarm optimization algorithm in addition to maintaining inertial movement, only to the current population of any better individual state learning, and the inertia weight coefficient is a random number. The improved scheme reduces the uncertain parameters of the algorithm, simplifies the particle learning mechanism and enhances the population diversity. In this paper, a UAV attitude control system is constructed. The improved particle swarm optimization algorithm is used to optimize the four control parameters. The simulation results show that the improved particle swarm optimization algorithm has better global search ability than the general particle swarm optimization algorithm, so the better UAV attitude control parameters are obtained.