基于改进被囊群算法的四旋翼姿态控制PID整定

PID Parameter Tuning of Quadrotor Attitude Control Based on Mended Tunicate Swarm Algorithm

四旋翼无人机模型具有非线性、强耦合、欠驱动的特性,其姿态控制的PID参数整定非常困难。针对上述问题,将被囊群算法引入姿态控制器,进行参数优化。并在原有算法的基础上引入混沌初始化、逐维反向学习策略和自适应权重因子,以此来提高算法的全局探索能力和局部开发能力,从而有效地对PID参数进行整定。最后,通过在MATLAB/Simulink中搭建模型并进行虚拟仿真。经测试验证,运用该算法优化的PID控制器有更好的控制精度和效率。

The Quadrotor UAV model has the characteristics of non-linear, strong coupling and underactuated, and it is very difficult to tuning the PID parameters of its attitude control. To solve these problems, The Tunicate Swarm Algorithm is introduced into the attitude controller to optimize the parameters. On the basis of the original algorithm, chaotic initialization, the opposition-based learning strategy and adaptive weight factor are introduced to improve the global exploration ability and local development ability of the algorithm, so as to effectively tune the PID parameters. Finally, the model was built and virtually simulated in MATLAB/Simulink. The test results show that the PID controller optimized by this algorithm has better control accuracy and efficiency.