摘要
针对超机动飞行过程中气动参数变化剧烈、控制精度高的特点,提出了一种基于神经网络的鲁棒自适应动态面控制方法.模型不确定性和外界干扰由RBF神经网络在线补偿,控制律由动态面控制方法得到,降低了反推控制器的复杂性,改进的神经网络权值调整自适应律改善了系统的过渡过程品质.利用Lyapunov稳定性定理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.对所设计的飞行控制系统进行了Herst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.
An approach to neural network-based robust and adaptive dynamic surface control is proposed for supermaneuverable flight with violent changes of aero-dynamics parameters. To achieve high precision, a radical basis function neural network （RBFNN） is used to compensate for parameter uncertainties and unknown disturbance. Control laws are obtained from dynamic surface control which reduces the complexity of backstepping controller. In addition, adaptive tuning rules of neural network weights are improved to achieve good performance of transient processes. Stability analysis using the Lyapunov stability theorem shows that all closed-loop signals are bounded. Output tracking error and approximate error of neural network weights exponentially converge to small compacts. Finally, results of the Herbst maneuver simulation show that the designed control systems have good performance under stall conditions.
出处
《应用科学学报》
CAS
CSCD
北大核心
2007年第6期632-638,共7页
Journal of Applied Sciences
基金
国家自然科学基金资助项目(90405011)
关键词
飞行控制
动态面控制
RBF神经网络
超机动
flight control
dynamic surface control
RBF neural network （RBFNN）
supermaneuver
