无人机模糊小波神经网络轨迹线性化控制

Design of a fuzzy wavelet neural network based trajectory linearization controller for UAV

针对系统存在不确定和有界干扰的情况,提出了一种基于模糊小波神经网络的轨迹线性化控制方法。利用模糊小波神经网络对非线性函数的逼近能力,减小不确定干扰对系统的影响,并与轨迹线性化方法结合设计了无人机飞控系统控制器。采用Lyapunov稳定性理论,证明了在所设计的控制器下,闭环系统所有信号一致最终有界。最后对系统存在不确定的情况下进行了仿真,并与没有加模糊小波神经网络的轨迹线性化控制器进行了对比,仿真结果证明了所提方法的有效性和鲁棒性。

Considering the uncertainty and bounded disturbance existed in nonlinear systems, a Trajectory Linearization Control （TLC） method based on Fuzzy Wavelet Neural Network（FWNN） is proposed. The FWNN has the capability to approach the nonlinear function, thus it can estimate the uncertain disturbance and reduce the influence of it to the nonlinear system. By combining FWNN with TLC, a flight control system is designed for Uninhabited Aerial Vehicle （UAV）. Based on Lyapunov theory, it is proved that all signals of the closed-loop systems are limited at last under the designed controller. Simulations were carried out for a uncertain system, and the result was compared with that of the TLC controller without using FWNN. It showed that the FWNNTLC controller proposed in this paper is effective and robust,and its performance is better.