基于PID神经网络的滑翔增程制导炮弹解耦控制系统设计

孙东阳; 易文俊

基于PID神经网络的滑翔增程制导炮弹解耦控制系统设计

引用本文：孙东阳，易文俊.基于PID神经网络的滑翔增程制导炮弹解耦控制系统设计[J].计算技术与自动化,2011,(2):21-25

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作者	单位
孙东阳，易文俊	（瞬态物理国家重点实验室，江苏南京210094）

中文摘要:针对低速旋转的滑翔增程制导炮弹存在的俯仰和偏航通道控制耦合性问题，阐述利用基于PID神经网络进行双通道解耦控制设计。首先描述滑翔增程炮弹动力学模型，基于对该模型的分析基础上提出PID神经网络的结构和计算方法，并采用增加动量项的权值修正和自适应可变学习率对其进行改进。在此基础上，通过粒子群优化算法对网络的初始权值进行优化。通过matlab仿真并对其结果进行分析可以得到，该设计方法较好地满足了系统的解耦控制要求，且能迅速接近控制目标，同时可以很好地克服弹体处于不同马赫时气动参数变化对控制系统造成的影响，能实现较为理想的控制效果。

中文关键词:PID神经网络解耦控制粒子群优化动量项自适应学习速率

Design of Decoupling Control System for Glide Extended Range Guided Munition Based on PID Neural Network

Abstract:For the existing control coupling between pitch and yaw channels in low speed rotation of the glide extended range guided munition, this paper describes the dual channel decoupling control design using PID neural network. To this end, the paper first describes the dynamic model of glide extended range guided munition, based on the analysis of the model a structure and calculation method of PID neural network,is proposed , and it is improved by modified weight which uses increasing momentum term and adaptive variable learning rate. On this basis, through particle swarm optimization algorithm the initial weights of the network is optimized. By simulation through Matlab and analysis of the results ,it can be obtained that the method designed satisfies the requirements of decoupling control system, and can rapidly approaching the control target, at the same time it can well overcome the impact of the control system caused by the changes of missile aerodynamic parameters in different Maches,so a better control results can be achieved through this design.

keywords:PID neural network decoupling control particle swarm optimization momentum adaptive learning rate

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