四旋翼飞行器自适应PIDNN控制研究  

On Adaptive PIDNN Control of Quadrotor Aircraft

作　　者：尚明杰 浦黄忠[1] 郭剑东[1] SHANG Ming-jie PU Huang-zhong GUO Jian-dong(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

机构地区：[1]南京航空航天大学自动化学院,南京210016

出　　处：《电光与控制》2017年第8期20-23,37,共5页Electronics Optics & Control

基　　金：国家自然科学基金(61304223);教育部高等学校博士学科点专项科研基金(20123218120015);中央高校基本科研业务费专项资金(NZ2015206)

摘　　要：针对传统四旋翼PID控制器参数整定困难和控制效果较难达到最优的问题,综合了传统PID控制器工程意义明确、参数整定简单以及神经网络的非线性映射和自学习的优点,构造了四旋翼飞行器神经网络PID(PIDNN)控制器。利用神经网络的非线性映射特点和自学习能力优化了传统PID控制器的控制效果,借助PID控制器的结构,解决了神经网络层数、节点数和连接权重初值选取困难的问题。同时利用自适应调整比例神经元加权系数,增加了系统的响应速度。最后,通过非线性全数值仿真验证了算法的合理性和有效性。In traditional quadrotor PID controller, parameter tuning is difficult and it is also difficult to achieve optimum control effect. To solve the problems, we constructed a quadrotor PID Neural Network （PIDNN） controller, which integrated the advantages of the traditional PID controller of clear engineering meaning and simple parameter tuning, with the advantages of Neural Network （NN） of nonlinear mapping and self-learning capability. The nonlinear mapping and self-learning capabilities of NN were used to optimize the control effect of traditional PID controller. By constructing the PID controller, the initial values of number of neural network layers, modes and connection weights were determined. At the same time, we designed a kind of adaptive flight control algorithm of PIDNN, using adaptive adjustment of proportional neuron weighting coefficient to increase the response speed of the system. The rationality and validity of the algorithm were verified by using a nonlinear full numerical simulation.

关 键 词：四旋翼 控制器 神经网络 PIDNN 自适应 

分 类 号：V279[航空宇航科学与技术—飞行器设计]