坦克炮控系统的复合自抗扰控制研究  

Research on Composite Active Disturbance Rejection Control of Tank Gun Servo System

作　　者：吕家兵 侯远龙[1] 高强[1] 金鹏程 LV Jia-bing;HOU Yuan-long;GAO Qiang;JIN Peng-cheng(School of Mechanical Engineer,Nanjing University of Science and Technology,Nanjing 210094 China)

机构地区：[1]南京理工大学机械工程学院,江苏南京210094

出　　处：《自动化技术与应用》2020年第4期1-7,共7页Techniques of Automation and Applications

摘　　要：坦克炮行进在颠簸的路面中身管的稳定是保证射击精度前提,同时坦克炮控系统自身存在的诸多非线性因素。基于自抗扰控制对被控系统参数变化和外部扰动不敏感,可自抗扰控制参数众多,整定复杂,而且参数选择直接影响到坦克炮控系统的性能。因此,设计了神经网络对非线性扩张状态观测器(NESO)和非线性控制率(NLSEF)的参数在线整定,同时对神经网络的重要参数采用改进的粒子群算法在线寻优。仿真结果表明,该方法可以提高系统的抗干扰能力和鲁棒性。The stability of the tank in the bumpy road is the premise of ensuring the accuracy of the shooting, and the tank gun control system has many nonlinear factors. Based on the active disturbance rejection control, it is not sensitive to the parameter changes and external disturbances of the controlled system. The self-disturbance control parameters are numerous, the setting is complicated, and the parameter selection directly affects the performance of tank gun control system. Therefore,a neural network is designed to tune the parameters of the nonlinear extended state observer(NESO) and the nonlinear control rate(NLSEF). At the same time, the important parameters of the neural network are optimized on-line by the optimized particle swarm optimization algorithm. The simulation results show that the method can improve the anti-jamming ability and robustness of the system.

关 键 词：坦克炮控系统 神经网络 自抗扰控制 粒子群算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TJ38[自动化与计算机技术—控制科学与工程]