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根据对飞机刹车过程动力学分析与建模,本文提出了一种基于无味卡尔曼滤波(UKF)的模糊神经网络控制律。本控制律结合了无味卡尔曼滤波对机体速度的良好估计效果和模糊神经网络控制器对不同系统参数的适应能力,能够很好完成对最佳滑移率的追踪任务。Matlab仿真试验结果显示,基于无味卡尔曼滤波的模糊神经网络控制器可以准确的估计飞机滑跑时的速度,改善飞机防滑刹车系统性能,提高刹车效率。
According to the dynamics analysis and modeling of aircraft braking process, this paper presents a fuzzy neural network control law based on Unscented Kalman Filter (UKF). The control law combines the good estimation of the body velocity by the unscented Kalman filter and the adaptability of the fuzzy neural network controller to different system parameters, and can well track the best slip rate. Matlab simulation results show that the FBG-based fuzzy neural network controller can accurately estimate the speed of the aircraft when it slips, improve the performance of the aircraft anti-skid braking system and improve the braking efficiency.