摘要
研究一种鲁棒轨迹线性化控制方法并将其应用于无人机(unmanned aerial vehicle,UAV)航迹跟踪控制设计。通过理论分析指明传统轨迹线性化控制方法对系统中的不确定性存在鲁棒性不足的问题,采用改进隐层自适应神经网络对不确定性进行补偿,并利用Lyapunov理论证明了跟踪误差的有界性,最后将该方法应用到无人机三维航迹跟踪控制中。仿真结果表明,当参数摄动在20%时,该控制方法仍能使UAV很好地跟踪理想航迹,从而验证了该方法的有效性。
A robust trajectory linearization control method and its application to unmanned aerial vehicle（UAV） path following are proposed.Considering that the conventional trajectory linearization control method lacks enough robustness to the system uncertainties,an improved single hidden layer adaptive neural network is developed to compensate the system uncertainties,then the boundedness of the tracking error is proved by Lyapunov theory.Finally,the proposed method is applied to the UAV three-dimension path following problem.Simulation results show that the designed controller can still make the UAV track the desired path well even if there exist 20% uncertainties in the parameters,thus the effectiveness of the method is validated.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2012年第4期767-772,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(61074007)
空军工程大学研究生创新基金资助课题
关键词
无人机
航迹跟踪
鲁棒轨迹线性化控制
自适应神经网络
unmanned aerial vehicle（UAV）
path following
robust trajectory linearization control（RTLC）
adaptive neural network（ANN）
