超机动飞行的鲁棒自适应神经网络动态面控制

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率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。对所研究的飞行控制系统进行了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。

Robust Adaptive Control of Neural Dynamic Surface for Supermaneuverable Flight

An approach to neural network-based robust and adaptive dynamic surface control is proposed for supermaneuverable flight with violent changes of aerodynamics 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.