基于RBF神经网络补偿的航空发动机H_(∞)自适应控制研究  

Research on H_(∞)adaptive control of aero-engine based on RBF neural network compensation

作　　者：薛红阳 蔡开龙 李黄琪 濮志刚 XUE Hongyang;CAI Kailong;LI Huangqi;PU Zhigang(College of Aircraft Engineering,Nanchang Hangkong University,Nanchang 330063,China)

机构地区：[1]南昌航空大学飞行器工程学院,南昌330063

出　　处：《航空工程进展》2023年第1期128-134,共7页Advances in Aeronautical Science and Engineering

基　　金：江西省双千计划(jxsq2018106057)。

摘　　要：航空发动机控制系统是飞行器的重要机构,航空发动机存在的控制增益衰减和未建模动态等不确定性问题影响了其控制性能,为此设计将H_(∞)自适应控制和补偿控制相结合的控制器。首先,基于混合灵敏度理论设计H_(∞)自适应控制器;然后,基于Lyapunov严格稳定理论设计RBF神经网络补偿控制器对不确定性进行拟合补偿,并通过与误差相关的线性函数调整拟合速度;最后,以归一化后的航空发动机模型为被控对象进行多变量仿真试验。结果表明:本文设计的自适应控制器能够有效补偿不确定性,相比H_(∞)控制器,超调量和调节时间都有所降低。Aero-engine control system is an important mechanism of aircraft,the uncertainty of control gain attenuation and unmodeled dynamics of aero-engine can affect its control performance,therefore,a controller combining H_(∞)adaptive control and compensation control is designed.Firstly,the H_(∞)adaptive controller is designed based on the mixed sensitivity theory.And then,based on Lyapunov strict stability theory,the radial basis function(RBF)neural network compensation controller is designed to compensate the uncertainty,and the fitting speed is adjusted by the linear function related to the error.Finally,the normalized aero-engine model is taken as the controlled object to carry out the multi-variable simulation test.The results show that the adaptive controller designed in this paper can effectively compensate the uncertainty and reduce the overshoot and adjusting time compared with the H_(∞)controller.

关 键 词：航空发动机 多变量控制 不确定性 混合灵敏度 RBF神经网络补偿 

分 类 号：V233.7[航空宇航科学与技术—航空宇航推进理论与工程]