摘要
针对飞机模型中存在气动参数不确定性以及外界干扰等影响因素,设计了一种超机动飞行的非线性鲁棒自适应控制系统。控制系统设计过程中,模型不确定性和外界干扰由RBF神经网络在线补偿,控制律及神经网络权值自适应律由反步法得到。解决了系统中控制增益矩阵未知,同时存在外界干扰情况下的鲁棒飞行控制系统设计,并证明了闭环系统所有信号有界,系统跟踪误差和神经网络权值估计误差指数收敛到有界紧集内。对所研究的飞行控制系统进行了过失速Herbst机动仿真,结果验证了该系统在过失速机动条件下具有良好的控制性能。
A robust and adaptive flight control system is designed for a nonlinear aircraft model with parameter uncertainties and unknown disturbance. During the design of control system, parameter uncertainties and disturbance are compensated for online by RBF neural networks. Control law and adaptive laws of neural networks are achieved through backstepping method. As a result, the design problem of robust flight control system is resolved under the condition of unknown control gain matrix and disturbance. It is proved that all signals in the closed-loop system are bounded. Moreover, tracking error of the system and estimation error of neural networks＇ weights are remained in the compact sets. Herbst maneuver simulation results demonstrate that the designed control system has good oerformance under conditions of stall.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2008年第4期710-714,共5页
Systems Engineering and Electronics
基金
国家自然科学基金资助课题(90405011)
