摘要
针对两栖武器除了受到路基环境下车体易振动的因素影响外,还会受到海洋环境下载体本身摇摆等因素的影响,导致两栖武器发射装置的发射角度出现偏差的情况,利用自构建小波神经网络的自适应和自学习的能力,提出一种基于自构建小波神经网络的内模控制方法来进行两栖武器随动系统研究。由小波基函数的激励强度和衰减程度来决定增加神经元节点或者修剪、删除神经元节点,达到优化隐含层结构的目的,然后采用LM算法来提高学习速率。通过自构建小波神经网络对内模控制系统的正、逆模型进行辨识,来改进控制技术。最后的实验仿真结果表明,该方法可以有效提高系统的抗干扰能力、发射精度以及调节的快速性。
In addition to the influence of the vibration of ground vehicles in subgrade environment,the amphibious weapons will also be affected by the swinging of its carrier in marine environment,which may result in the deviation of the launch angle of the amphibious weapon launcher.By using the self-adaptation and self-learning ability of wavelet neural network,an internal model control method based on self-built wavelet neural network is proposed for the amphibious weapon servo system.The excitation strength and attenuation degree of the wavelet basis function are used to add neuron nodes,or to trim or delete neuron nodes to optimize the hidden layer structure,and then the LM algorithm is used to improve the learning rate.The self-built wavelet neural network is used to identify the forward and inverse models of the internal model control system to improve the control technology.The final results show that the method can effectively improve the system’s anti-jamming capability,launching accuracy and adjustment speed.
作者
吴凯莉
侯远龙
高强
柯于锋
何禹琨
WU Kaili;HOU Yuanlong;GAO Qiang;KE Yufeng;HE Yukun(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
2021年第1期41-46,共6页
Electronics Optics & Control
关键词
两栖武器
随动系统
自构建
小波神经网络
内模控制
LM算法
amphibious weapon
servo system
self-construction
wavelet neural network
internal model control
LM algorithm
