近空间飞行器泛函连接网络自适应预测控制

针对存在强烈不确定和干扰的近空间高超声速飞行器（NHV），提出了一种新的非线性自适应控制方法。控制律由最优广义预测控制（OGPC）算法和泛函连接网络（FLN）直接自适应律组成。OGPC是一种连续时间的非线性预测控制算法。FLN则通过在线学习来抵消飞行中的未知不确定和干扰的影响。学习过程不需要任何离线训练过程。文中提供了NHV的闭环系统稳定性分析，经过证明系统误差和权值学习误差一致最终有界。对于姿态跟踪系统，仿真结果显示了控制器的良好性能。

ADAPTIVE PREDICTIVE CONTROL OF NEAR-SPACE VEHICLE USING FUNCTIONAL LINK NETWORK

A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle (NHV) in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller (OGPC) and the functional link network (FLN) direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately bounded. Simulation results show the satisfactory performance of the controller for the attitude tracking.