摘要
针对车载武器行进间射击的稳定性问题,提出一种基于RBF神经网络和模糊切换增益调节的滑模控制策略。建立车载武器伺服系统的数学模型,设计基于新型趋近率的滑模控制器,使系统快速收敛到平衡状态;在滑模控制的基础上融合模糊控制,采用模糊规则对控制器中的切换增益进行实时调节,利用切换增益消除系统受到的扰动,削弱系统抖振;利用RBF神经网络对系统的时变项进行自适应估计,以提高控制精度。仿真结果表明:设计的控制器对扰动不敏感,能有效地提高系统的位置精度、削弱抖振,并使系统具有较强的鲁棒性。
A sliding mode control strategy based on RBF neural network and fuzzy switching gain regulation is proposed to solve the stability problem of vehicle-mounted weapon firing while moving.The mathematical model of the vehicle-mounted weapon servo system is established,and the sliding mode controller based on the new reaching law is designed to make the system converge to the equilibrium state quickly.The fuzzy control is integrated on the basis of the sliding mode control,and the fuzzy rules are used to adjust the switching gain of the controller in real time.The switching gain is used to eliminate the disturbance of the system and weaken the system chattering;RBF neural network is used to adaptively estimate the time-varying items of the system to improve the control precision.The simulation results show that the designed controller is insensitive to the disturbance,and can effectively improve the position accuracy of the system,weaken the chattering,and make the system have strong robustness.
作者
李有为
侯远龙
季强
刘璐
闫智聪
Li Youwei;Hou Yuanlong;Ji Qiang;Liu Lu;Yan Zhicong(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;School of Cyber Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《兵工自动化》
北大核心
2025年第3期4-8,共5页
Ordnance Industry Automation
关键词
伺服系统
滑模控制
模糊控制
RBF神经网络
抖振抑制
servo system
sliding mode control
fuzzy control
RBF neural network
chattering suppression
