摘要
为解决火炮伺服系统面临的一系列非线性因素,设计一种基于回声状态网络的自抗扰控制(active disturbance rejection control,ADRC)策略。使用回声状态网络(echo state network,ESN)实现自抗扰控制重要参数的在线整定,并引入梯度下降算法与改进后的灰狼优化算法(grey wolf optimization,GWO)对回声状态网络进行训练。仿真结果表明:该新型控制方法能有效提高火炮伺服系统的动态响应性能、抗干扰性能以及随动跟踪精度,满足火炮伺服系统所要求的性能指标。
In order to solve a series of nonlinear factors faced by gun servo system,an active disturbance rejection control(ADRC)strategy based on echo state network(ESN)is designed.The echo state network is used to realize the online tuning of the important parameters of ADRC,and the gradient descent algorithm and the improved gray wolf optimization(GWO)algorithm are introduced to train the echo state network.The simulation results show that the new control method can effectively improve the dynamic response performance,anti-interference performance and tracking accuracy of the gun servo system,and meet the performance requirements of the gun servo system.
作者
吴亮
陈机林
候远龙
王攀伟
姜昭钰
Wu Liang;Chen Jilin;Hou Yuanlong;Wang Panwei;Jiang Zhaoyu(School of Mechanical Engineering,Nanjing University of Science&Technology,Nanjing 210094,China)
出处
《兵工自动化》
2021年第11期16-19,31,共5页
Ordnance Industry Automation
关键词
伺服系统
自抗扰控制
回声状态网络
梯度下降算法
灰狼优化算法
servo system
active disturbance rejection control
echo state network
gradient descent algorithm
grey wolf optimization algorithm
