摘要
提出一种改进的动态面鲁棒自适应飞行控制律设计方法;动态面飞行控制律消除了反推设计中由于对虚拟控制反复求导而导致的复杂性问题;利用RBF神经网络在线逼近飞机气动参数变化引起的非线性和不确定性,并以所有神经网络权值范数平方的最大值为更新参数来设计控制器,仅一个参数需要更新;基于Lyapunov稳定性定理证明了闭环系统的所有信号半全局一致最终有界;飞机俯仰运动飞行的数值仿真表明:在考虑气动参数摄动的情况下,轨迹角跟踪仍很好地实现,且兼具控制器结构简单、计算量小和鲁棒性强的特点。
Flight control law based on improved dynamic surface robust adaptive control approach is proposed. The complex problem in traditional backstepping design, which is caused by repeated differentiations of virtual control, is eliminated by dynamic surface control meth od. On line parameter update laws that make use of neural networks are used to approximate the aerodynamic parameters nonlinear and un ccrtainties. The Minimax norm of all NN weight vetor is defined as updated parameter, only one parameter is needed to be estimated on line. Based on Lyapunov theorem, it is proved that all signals in the closeloop syatem are guaranteed to be semiglobally uniformly ulti mately bounded. Simulation results for aircraft pitch movement demonstrate that considering aerodynamic parameters disturbances the control law still can accomplish angle track very well, and guarantee a simpler controller structure, small numeration and good robustness.
出处
《计算机测量与控制》
北大核心
2013年第6期1552-1555,共4页
Computer Measurement &Control
基金
国家自然科学基金资助(60904038)
关键词
飞行控制
动态面控制
神经网络
自适应控制
Flight control
Dynamic surface control
Neural network
Adaptive control
