基于高增益观测器的航迹角自适应反步控制

针对飞行控制系统中状态向量不完全可测量的问题,设计了一种高增益神经网络自适应观测器.在常规高增益观测器的基础上引入径向基(RBF, Radial Basis Function)网络自适应项,对建模误差和外界干扰进行在线估计.将高增益观测器与基于动态面的反步控制相结合,提出了一种神经网络自适应反步控制方法.引入一阶滤波器,避免了传统反步控制中的"计算膨胀"问题.基于Lyapunov稳定性理论,给出自适应输出反馈控制器和RBF网络权值向量的自适应律,并证明了闭环系统是半全局一致有界.从航迹角控制系统仿真结果可以看出,航迹角能够较好地跟踪指令信号,不受建模误差和外界干扰的影响,所设计的观测器具有良好的收敛性,控制系统具有较高的鲁棒性.

Adaptive backstepping control for flight path angle based on high gain observer

An adaptive neural network high gain observer was designed to solve the problem that the states of the flight control system were not absolutely measurable. The adaptive items based on the radial basis function (RBF) network were introduced into the general high gain observer on purpose of the on-line estimation of the modeling errors and the external disturbances. With the combination of the high gain observer and the backstepping control based on the dynamic surface, an adaptive neural network backstepping control approach was proposed. The traditional problem of explosion of calculation in the backstepping control was avoided by the introduction of the first-order filter. The adaptive output feedback controller and the adaptive laws of the RBF network weight vectors were obtained on the basis of the Lyapunov stability theory. It is proved that the closed-loop system is semi-globally and uniformly bounded. The simulation results of the flight path angle control system show that the flight path angle can track the command signal without the effect of the modeling errors and the external disturbances, the convergence of the observer is concluded and the robustness of the control system is verified.