四旋翼飞行器动态粒子群优化算法的PID控制技术

PID Control Technology for Quadrotor Aircraft Based on Dynamic Particle Swarm Optimization Algorithm

针对四旋翼飞行器的标准粒子群优化算法PID控制器容易陷入局部最优解、过早收敛的问题,提出了一种动态粒子群优化算法的PID控制技术。该算法主要由两部分组成:①根据迭代过程中粒子群粒子与全局最优粒子间的欧氏距离大小动态改变惯性权重,并设置系数控制其对惯性权重的影响程度;②引入杂交进化,在指定迭代次数内,若粒子群全局最优值连续未变,则对指定数量的粒子进行杂交,增加粒子多样性,避免陷入局部最优。通过Matlab/Simulink搭建四旋翼飞行器模型并仿真。结果表明,该优化算法能有效地避免陷入局部最优和过早收敛,使四旋翼飞行器得到更平稳、精确的控制,减少超调,提升计算效率。

The PID controller of the standard particle swarm optimization algorithm for quadrotor is easy to fall into the local optimal solution and premature convergence.A dynamic particle swarm optimization algorithm PID control technique is proposed.The algorithm consists of two parts:firstly,the inertia weight is dynamically changed according to the Euclidean distance between the particle swarm particle and the global optimal particle in the iterative process,and the coefficient is adjusted to control its influence on the inertia weight.Secondly,the algorithm introduces the hybrid variation.If within the specified number of iterations the global optimal value of the particle group has not changed,the hybridization of the specified particle group is performed.The four-rotor aircraft model is built and simulated by MATLAB/Simulink.The results show that the optimization algorithm can effectively avoid local optimal and premature convergence,and make the quadrotor more smooth and precise control and reduce overshoot.