针对某火箭炮交流伺服系统存在惯性力矩、摩擦、变负载及不同工况下内外扰动等复杂非线性,传统的PID控制方法难以得到良好性能指标的问题, 在分析火箭炮交流伺服系统组成的基础上, 建立其系统数学模型, 并设计一种基于神经网络自抗扰控制(ADRC)的分数阶PID(FOPID)控制器; 为减少该控制器参数计算量以及提高其动态性能特性, 引入RBF神经网络控制算法, 对FOPID控制器的积分阶次和微分阶次实时在线自整定。数值仿真实验结果表明: 该控制策略能够有效抑制位置扰动, 具有修复到位快、响应速度快、无超调等优点。

In the AC servo system of a rocket launcher, it is difficult for the traditional PID control method to get good performance due to the complex nonlinearity of the moment of inertia, friction, variable loads and internal and external disturbances under different working conditions.To solve the problem, we established the mathematical model of the AC servo system of rocket launcher according to its composition, and designed a Fractional Order PID (FOPID) controller based on neural network Active Disturbance Rejection Control (ADRC).In order to reduce the parameters'calculation cost of the fractional order controller and improve its dynamic performance, a control algorithm of Radical Basis Function (RBF) neural network is adopted for on-line self-tuning of the two parameters of FOPID, integral order and differential order.The simulation test results show that the control strategy can effectively suppress the position disturbance with fast response speed and no overshoot.