基于PIDNN的六旋翼无人机飞行控制算法研究

摘要

Aiming at problem that parameters of six-rotor unmanned helicopter proportion integration differentiation(PID) controller are difficult to be optimized,a PID neural network (PIDNN)method which has characteristics of nonlinear mapping and self-learning,is developed and employed.It can adjust attitude control parameters dynamically and promote the self-adaptability of controller system.To prove the effectiveness of the method,the mathematical modeling for six-rotor is established by Simulink module in Matlab,PIDNN controller based on back propagation(BP) algorithm is obtained by S-function.Simulation results are compared with control effects of PID,verifying that control effect of PIDNN is more valid in reducing attitude adjustment time and decreasing overshoots.%针对六旋翼无人机比例-积分-微分(PID)控制器参数优化困难的问题,采用了PID神经网络(PIDNN)控制方法,利用其非线性映射和自学习的特性,实现了姿态控制参数的动态调整,增加了系统的自适应性.为验证方法的有效性,通过Matlab的Simulink模块构建了六旋翼无人机数学模型;利用S函数实现了基于反向传播(BP)算法的PIDNN控制器;将仿真结果与传统PID控制效果进行对比,结果表明:在缩短姿态调整时间与减少超调量方面,PIDNN方法控制效果优于PID方法.