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Fuzzy wavelet neural control with improved prescribed performance for MEMS gyroscope subject to input quantization

机译：模糊小波神经控制，具有改进的MEMS陀螺仪的输入量化规定性能

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摘要

In this paper, a fuzzy wavelet neural control scheme with improved prescribed performance is investigated for micro-electromechanical system (MEMS) gyroscope in the presence of uncertainties and input quantization. A hysteresis quantizer (HQ) is introduced in the controller design to generate input signal in a finite set, which can greatly reduce the actuator bandwidth without decreasing the control accuracy, and avoid the undesirable chattering occurring universally in other quantizers. To guarantee the output tracking with better prescribed transient behavior, a modified prescribed performance control (MPPC) consisting of asymmetric performance boundaries and an error transformation function is explored, such that arbitrarily small overshoot can be assured without retuning design parameters. Unlike the traditional neural network that suffers from explosion of learning, a fuzzy wavelet neural network (FWNN) based on minimal-learning-parameter (MLP) is designed to identify uncertainties with slight computational burden. A robust quantized control scheme is synthesized to compensate for quantization error and achieve prescribed ultimately uniformly bounded (UUB) tracking. Finally, extensive simulations are presented to verify the effectivenessIn this paper, a fuzzy wavelet neural control scheme with improved prescribed performance is investigated for micro-electro-mechanical system (MEMS) gyroscope in the presence of uncertainties and input quantization. A hysteresis quantizer (HQ) is introduced in the controller design to generate input signal in a finite set, which can greatly reduce the actuator bandwidth with-out decreasing the control accuracy, and avoid the undesirable chattering occurring universally in other quantizers. To guarantee the output tracking with better prescribed transient behavior, a modified prescribed performance control (MPPC) consisting of asymmetric performance boundaries and an error transformation function is explored, such that arbitrarily small overshoot can be assured without retuning design parameters. Unlike the traditional neural network that suffers from explosion of learning, a fuzzy wavelet neural network (FWNN) based on minimal-learning-parameter (MLP) is designed to identify uncertainties with slight computational burden. A robust quantized control scheme is synthesized to compensate for quantization error and achieve prescribed ultimately uniformly bounded (UUB) tracking. Finally, extensive simulations are presented to verify the effectiveness of proposed control scheme.(c) 2020 Elsevier B.V. All rights reserved.
机译：本文在存在不确定性和输入量化存在下，研究了具有改进的规定性能的模糊小波神经控制方案，用于微机电系统（MEMS）陀螺。在控制器设计中引入磁滞量化器（HQ）以在有限组中产生输入信号，这可以大大减小致动器带宽而不降低控制精度，并且避免在其他量化器中普遍发生不希望的抖动。为了保证具有更好规定的瞬态行为的输出跟踪，探讨了由不对称性能边界和误差变换功能组成的修改规定的性能控制（MPPC），使得可以在不重新设计参数的情况下确保任意小的过冲。与遭受学习爆炸的传统神经网络不同，基于最小学习参数（MLP）的模糊小波神经网络（FWNN）旨在识别具有轻微计算负担的不确定性。合成了稳健的量化控制方案以补偿量化误差并实现最终均匀有界（UUB）跟踪的规定。最后，大量的模拟被呈现给验证effectivenessIn本文以提高规定的性能模糊小波神经控制方案研究了微机电系统（MEMS）陀螺仪中的不确定性和输入的量化的存在。滞后量化器（HQ）在控制器设计中引入，以产生在一组有限的输入信号，从而可以大大减小致动器带宽出降低控制精度，避免在其他的量化普遍发生的不希望的抖动。为了保证具有更好规定的瞬态行为的输出跟踪，探讨了由不对称性能边界和误差变换功能组成的修改规定的性能控制（MPPC），使得可以在不重新设计参数的情况下确保任意小的过冲。与遭受学习爆炸的传统神经网络不同，基于最小学习参数（MLP）的模糊小波神经网络（FWNN）旨在识别具有轻微计算负担的不确定性。合成了稳健的量化控制方案以补偿量化误差并实现最终均匀有界（UUB）跟踪的规定。最后，提出了广泛的模拟以验证所提出的控制方案的有效性。（c）2020 Elsevier B.V.保留所有权利。

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  • 来源
    《Fuzzy sets and systems》 |2021年第15期|136-154|共19页
  • 作者

    Shao Xingling; Si Haonan; Zhang Wendong;

  • 作者单位

    North Univ China Key Lab Instrumentat Sci & Dynam Measurement Minist Educ Taiyuan 030051 Peoples R China|North Univ China Sch Instrument & Elect Natl Key Lab Electmn Measurement Technol Taiyuan 030051 Peoples R China;

    North Univ China Key Lab Instrumentat Sci & Dynam Measurement Minist Educ Taiyuan 030051 Peoples R China|North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China;

    North Univ China Key Lab Instrumentat Sci & Dynam Measurement Minist Educ Taiyuan 030051 Peoples R China|North Univ China Sch Instrument & Elect Natl Key Lab Electmn Measurement Technol Taiyuan 030051 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    MEMS gyroscope; Modified prescribed performance control; Fuzzy wavelet neural network; Hysteresis quantizer;

    机译：MEMS陀螺;修改规定的性能控制;模糊小波神经网络;滞后量化器;

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