四旋翼飞行器自适应PIDNN控制研究

尚明杰; 浦黄忠; 郭剑东

doi:doi:10.3969/j.issn.1671-637x.2017.08.005

电光与控制, 2017, 24 (8): 20, 网络出版: 2017-09-21

四旋翼飞行器自适应PIDNN控制研究

On Adaptive PIDNN Control of Quadrotor Aircraft

论文大纲

尚明杰浦黄忠郭剑东

作者单位

南京航空航天大学自动化学院, 南京 210016

四旋翼控制器神经网络自适应 quadrotor controller neural network PIDNN PIDNN adaptive

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。

Abstract

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.

参考文献

[1] MIAA A A, WANG D B.Modeling and backstepping based nonlinear control strategy for a 6 DOF quadrotor helicopter［J］.Chinese Journal of Aeronautics, 2008, 21(3):261-268.

[2] VOOS H.Nonlinear and neural network-based control of a small four-rotoraerial robot［C］//International Conference on Advanced Intelligent Mechatronics, 2007:1-6.

[3] NICOL C, MACNAB C J B, RAMIREZ-SERRANO A.Robust neural network control of a quadrotor helicopter［C］//IEEE Canadian Conference on Electrical and Computer Engineering, Canada, 2008:1233-1238.

[4] DIERKS T, JAGANNATHAN S.Neural network output feed-back control of a quadrotor UAV［C］//The 47th IEEE Conference on Decision and Control, 2008:3633-3639.

[5] RASHID M I, AKHTAR S.Adaptive control of a quadrotor with unknown model parameters［C］//Proceedings of 9th International Bhurban Conference on Applied Sciences and Technology, 2012:8-14.

[6] MOREL Y, LEONESSA A.Direct adaptive tracking control of quadrotor aerial vehicles［C］//ASME International Mechanical Engineering Congress and Exposition, 2006:155-161.

[7] 陈彦民, 何勇灵, 孔令博, 等.四旋翼飞行器分散PID神经元网络控制［J］.中国惯性技术学报,2014, 22(2):185-190.

[8] 舒怀林.PID神经元网络及其控制系统［M］.北京:国防工业出版社,2006.

[9] 单海燕.四旋翼无人直升机飞行控制技术研究［D］.南京:南京航空航天大学, 2008.

[10] 苏立军, 朱红娟, 李小民, 等.四旋翼PIDNN控制器设计［J］.电光与控制, 2015, 22(2):65-68.

[11] 舒怀林.PID神经元网络对强耦合带时延多变量系统的解耦控制［J］.控制理论与应用, 1998, 15(6): 920-924.

[12] ZHANG L C, ZHANG C L, HORNG J H, et al.Study on multivariable system based on PID neural network control［J］.Advanced Materials Research, 2012(591/592/593):1490-1495.

尚明杰, 浦黄忠, 郭剑东. 四旋翼飞行器自适应PIDNN控制研究[J]. 电光与控制, 2017, 24(8): 20. SHANG Ming-jie, PU Huang-zhong, GUO Jian-dong. On Adaptive PIDNN Control of Quadrotor Aircraft[J]. Electronics Optics & Control, 2017, 24(8): 20.

四旋翼飞行器自适应PIDNN控制研究

关于本站 Cookie 的使用提示

全站搜索

四旋翼飞行器自适应PIDNN控制研究

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索