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摘要:
针对战斗机大机动飞行输入饱和问题，提出了一种自适应神经网络动态面控制方法。采用径向基(RBF)神经网络逼近飞机系统的不确定性，利用双曲正切函数处理系统的输入饱和问题，根据饱和受限后的实际控制输入与期望控制输入之差定义新误差变量，结合该误差变量设计大机动飞行控制律，并构造鲁棒项抵消神经网络逼近误差、外部干扰和建模误差的影响，利用动态面控制技术避免对虚拟控制器的复杂求导并减小计算量。根据Lyapunov稳定性定理证明了闭环控制系统所有信号有界，且通过选择合适的设计参数能够使姿态角跟踪误差收敛到原点的任意小邻域内。通过仿真结果的分析，验证了所提方法具有较好的鲁棒性和稳定性。
Abstract:An adaptive neural network dynamic surface control method is proposed to resolve the input saturation problem of aircraft high-
g maneuver flight. The Radial Basis Function (RBF) neural networks are utilized to approximate the unknown uncertain parts of aircraft model. The hyperbolic tangent function is used to handle the system input saturation problem. A new error is defined by the difference between saturated actual control input and desired control input, and a high-g maneuver flight control law is designed by combining this error, and the robust term is constructed to offset the influence of approximation error of neural network, external interference and modeling errors. The dynamic surface control technique is used to avoid the complex derivative operation of the virtual controller and reduce computation amount. It is proved from Lyapunov stability theorem that all the signals in the closed-loop control system are bounded, and the attitude angle tracking error can converge to an arbitrarily small neighborhood around zero by choosing the appropriate design parameters. Simulation results demonstrate the good robustness and stability of the proposed method.-
Key words:
- flight control /
- high-g maneuver /
- input saturation /
- neural networks /
- robustness
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