基于L1神经网络自适应算法的飞行器姿态控制

摘要

According to the non-linearity, underactuated and high coupling of the quadrotor attitude control system, a attitude controller is designed by a novel Li neural network adaptive control method based on a sigmoid activation function. The structure is with a low pass filter in the feedback loop, identified the uncertain nonlinearity online by neural network. Both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. For the guaranteed performance of both input and output signals of the uncertain system, the Lj gain of a cascaded system, comprised of the low-pass filter and the closed-loop desired reference model, is required to be less than the inverse of the Lipschitz constant of the unknown nonlinearities in system. At last, simulation results proves the efficiency of method.%针对四旋翼飞行器系统非线性、欠驱动、强耦合的特性,提出了基于Sigmoid作用函数的L1神经网络自适应算法姿态控制器.反馈回路中设有低通滤波器,利用神经网络对系统非线性因素在线辨识.只要由低通滤波器和期望闭环系统组成的级联系统L1增益小于非线性因素的李普西兹常数的倒数,就能确保通过提高L1自适应增益,使系统的输入输出信号瞬态响应和稳态跟踪性能与期望的线性时不变系统响应保持一致.仿真验证了算法有效性.