摘要
火箭破障武器装甲车在道路起伏的环境中车体易产生振动,在发射火箭炮破障弹时也会产生较大的振动,这些容易导致再次调炮破障时方向角度出现偏差影响破障精度,同时,其他扰动和不确定因素的存在也使得破障武器系统成为非线性时变系统。利用模糊控制良好的鲁棒性、适应性以及神经网络的自适应、自学习的能力,提出一种基于模糊RBF神经网络PID控制方法,同时采用K-means层次聚类对模糊神经网络的结构参数值进行初始化,采用LM算法对模糊神经网络进行训练。仿真结果表明,该方法可以有效提高系统的抗干扰能力、破障精度以及加快调炮的快速性。
In view of the fact that the armoured vehicle carrying obstacle-breaking rocket weapon may vibrate on a rough road,while a large vibration will also be generated when launching obstacle-breaking shells,which may easily cause the deviation of the direction angle of the next obstacle-breaking and affect the obstacle-breaking accuracy.With consideration of other disturbances and uncertainties,the obstacle-breaking weapon system can be taken as a nonlinear time-varying system.Based on the robustness and adaptability of fuzzy control and the adaptive and self-learning ability of neural network,a PID control algorithm based on fuzzy RBF neural network is proposed.At the same time,the structure parameter values of fuzzy neural network are initialized by K-means hierarchical clustering,and the fuzzy neural network is trained by using the LM algorithm.The simulation results show that the method can effectively improve the anti-interference ability,the accuracy of the obstacle-breaking and the speed of the gun adjustment.
作者
陶征勇
童仲志
侯远龙
时尚
胡近朱
TAO Zhengyong;TONG Zhongzhi;HOU Yuanlong;SHI Shang;HU Jinzhu(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《电光与控制》
CSCD
北大核心
2020年第9期99-104,共6页
Electronics Optics & Control
