基于神经扰动观测器的陀螺神经滑模控制

Luoyu Zhang, Zhiwei Wen, Xinsong Zhang, Yunxiang Guo, Cheng Lu
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引用次数: 1

摘要

本文提出了一种基于神经网络扰动观测器的神经网络滑模控制方法,用于外部扰动下MEMS陀螺仪的控制。在设计过程中,分别在滑模控制器设计和干扰设计中引入了两组RBF神经网络,以解除各模块对陀螺仪系统参数的依赖。同时,神经干扰观测器的集成有助于缓解传统滑模控制力中的抖振现象。仿真研究验证了所提控制方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Sliding Mode Control using Neural Disturbance Observer for Gyroscopes
In this paper, a neural network sliding mode control based on neural network disturbance observer is proposed for the control of MEMS gyroscopes under external disturbances. In the design process, two sets of RBF neural networks are incorporated in the sliding mode controller design as well as the disturbance design separately to release the dependence of each module on gyroscope system parameters. Meanwhile, the integration of the neural disturbance observer can help to alleviate chattering phenomenon in conventional sliding mode control forces. Simulation studies are implemented to verify the effectiveness of the proposed control scheme.
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