一类径向基神经网络干扰观测器轨迹线性化控制

利用径向基神经网络(RBFNN)的逼近能力,研究了基于径向基神经网络干扰观测器(RDO)的鲁棒自适应轨迹线性化控制(TLC)策略,以解决空天飞行器复杂飞行条件下系统不确定及干扰的控制问题。分析了系统存在不确定性时轨迹线性化控制方法性能降低甚至失效的原因,设计了自适应调节律,并采用Lyapunov方法严格证明了在该自适应调节律作用下闭环系统所有误差信号最终有界。仿真结果表明,较当前TLC方法的控制性能,新方案在空天飞行器系统上具有更优异的控制性能和鲁棒性。

Robust adaptive trajectory linearization control for aerospace vehicle

A robust adaptive TLC approach is developed to improve the performance of the current TLC by combining with a radial basic function neural network disturbance observer(RDO).The study shows that the current TLC method may exhibit poor performance when uncertainties exist and turn large.And a robust adaptive law is applied to overcome the reconstruction error.By Lyapunov's direct method,a rigorous poof demonstrates that the provided adaptive laws can guarantee ultimate boundedness of all the signals in the integrated closed system.Finally,the proposed method is applied to the control of aerospace vehicle(ASV) in order to illustrate its effectiveness and applicability.