基于RBFSMC车载武器行进间稳定控制  

Moving Stability Control of Vehicular Weapon Based on RBFSMC

作　　者：李佳帅 高强[1] 邓桐彬 李勃 季强 符伟鹏 Li Jiashuai;Gao Qiang;Deng Tongbin;Li Bo;Ji Qiang;Fu Weipeng(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;Land Combat Command Information System Research Department,No.28 Research Institute of China Electronics Technology Group Corporation,Nanjing 210000,China)

机构地区：[1]南京理工大学机械工程学院,南京210094 [2]中国电子科技集团公司第二十八研究所陆上作战指挥信息系统研究部,南京210000

出　　处：《兵工自动化》2025年第4期1-5,25,共6页Ordnance Industry Automation

摘　　要：针对某车载机关炮行进间射击会受到一系列非线性因素的影响,设计一种基于RBF神经网络的滑模控制策略。基于滑模控制强鲁棒性的特点,通过一种实时扰动观测器精确观测扰动,利用RBF神经网络在非线性函数逼近方面的独特优势来逼近系统的不确定项,设计自适应律来保证系统的渐进稳定性;通过RBF神经网络动态调节切换增益,进一步抑制产生的抖振问题,抑制参数变化和外界扰动等非线性因素的影响。仿真结果表明:与常规的滑模控制相比,该控制策略可有效提高车载机关炮系统的稳定控制精度。A sliding mode control strategy based on RBF neural network was designed in order to solve the problem that the on-road firing of a vehicle-mounted machine gun would be affected by a series of nonlinear factors.Based on the strong robustness of sliding mode control,a real-time disturbance observer is used to accurately observe the disturbance,and the unique advantage of RBF neural network in nonlinear function approximation is used to approximate the uncertainties of the system,and an adaptive law is designed to ensure the asymptotic stability of the system.The switching gain is dynamically adjusted by RBF neural network to further suppress the chattering problem and the influence of nonlinear factors such as parameter change and external disturbance.The simulation results show that compared with the conventional sliding mode control,the proposed control strategy can effectively improve the stability control precision of the vehicle-mounted gun system.

关 键 词：RBF神经网络 稳定控制 滑模控制 车载武器 扰动观测器 

分 类 号：TJ35[兵器科学与技术—火炮、自动武器与弹药工程]