摘要
燃气流量调节技术是固体火箭冲压发动机的关键技术之一,对提高固冲发动机的可靠性、机动性、精确性等有着很大的意义。而燃气流量调节系统是一个形式上较为复杂具有参数变化特性的非线性系统,此外由于系统存在着模型误差和参数误差等扰动,所以传统的PID无法在发动机的全工作域内均取得精确度高、响应速度快的控制效果。利用自适应RBF神经网络结合反馈线性化对燃气发生器进行压强闭环控制。并且利用变结构控制项,增强系统的抗扰动能力。仿真结果表明,所研究的控制算法能够克服燃气发生器变参性、非线性的影响,能够实现其对目标压强的良好的跟踪性能,并且大幅提升了响应时间,对提高燃气发生器的性能有重要的意义。
Gas flow regulation technology is one of the key technologies of solid rocket ramjets which is of great significance for improving the reliability,maneuverability,and accuracy of solid rocket ramjet engines.The gas flow control system is a non-linear system with complex parameter changes in form.In addition,due to the system's dis-turbances such as model errors and parameter errors,the traditional PID cannot achieve high-precision and fast-re-sponse control effects in the entire working range of the engine.Therefore,this paper used an adaptive RBF neural network combined with feedback linearization to control the pressure of the gas generator in a closed loop.And varia-ble structure control items were used to enhance the anti-disturbance ability of the system.The simulation results show that the control algorithm studied can overcome the influence of variable parameter and nonlinear of gas genera-tor,achieve good tracking performance of target pressure,and greatly improve the response time,which is of great significance to improve the performance of gas generator.
作者
齐胜举
陈雄
薛海峰
魏岩淞
QI Sheng-ju;CHEN Xiong;XUE Hai-feng;WEI Yan-song(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing Jiangsu 201194,China)
出处
《计算机仿真》
北大核心
2023年第5期112-116,171,共6页
Computer Simulation
基金
总装备部预先研究项目(40404040301)
国家自然科学基金(51606098)
江苏省自然科学基金(BK20140772)。
关键词
燃气发生器
流量调节
自适应神经网络
反馈线性化
Gas generator
Flow regulation
Adaptive neural network
Feedback linearization
