摘要
针对某舰载火箭炮射击精度较低的问题,提出了一种基于BP神经网络PID的控制方法。根据舰载火箭炮随动系统的组成和原理,建立了其数学模型,将BP神经网络与PID控制相结合,利用神经网络的自适应和自学习特点对传统的PID控制器进行优化,从而实现自适应控制提高系统的性能。仿真结果表明:所提方法能够使系统具有更好的跟踪参数模型输出,且可以使系统具有更强的抗干扰能力。
In order to better control the servo system of carrier rocket,the BP neural network PID control method is proposed in this paper.According to the composition and principle of the shipborne rocket launcher servo system,its mathematical model is established.By combining BP neural network with PID control,the traditional PID controller can be modified using the adaptive and learning characteristics of neural network,so as to achieve adaptive control and improve the performance of the system.The simulation results show that the proposed method can make the system output better track the parameter model output and make the system have stronger anti-interference ability.
作者
朱新向
侯润民
高强
桂林
ZHU Xinxiang;HOU Runmin;GAO Qiang;GUI Lin(College of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;China Jiangshan Heavy Industry Research Institute Military Products Company,Nanjing 210094,China)
出处
《自动化与仪器仪表》
2023年第7期60-62,68,共4页
Automation & Instrumentation
