Adaptive Neural Output Feedback Control for Nonstrict-Feedback Nonlinear Systems with Quantized Input

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• 英文论文题名：Adaptive Neural Output Feedback Control for Nonstrict-Feedback Nonlinear Systems with Quantized Input
• 论文作者：Yan Dong ; Zhaoxu Yu ; Fangfei Li
• 英文论文作者：Yan Dong ; Zhaoxu Yu ; Fangfei Li ; Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education ; East China University of Science and Technology ; Department of Mathematics ; East China University of Science and Technology
• 年：2017
• 作者机构：Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education,East China University of Science and Technology;Department of Mathematics,East China University of Science and Technology;
• 英文论文关键词：Nonstrict-feedback systems ; quantized input signal ; output feedback ; adaptive control ; unknown control direction
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（A）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
• 语种：英文
• 分类号：TP273.2
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：6
• 文件大小：204k
• 原文格式：D
• 会议级别：全国

摘要

This paper focuses on the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown control coefficients and quantized input. The difficulty from the unknown control direction is solved by using the linear state transformation and the Nussbaum gain function(NGF) approach. Based on the combination of input-driven observer, backstepping technique, neural network(NN) parametrization and variable separation method, a novel adaptive output feedback quantized control scheme involving only one adaptive parameter is developed for such systems. The designed quantized controller ensures that all signals of closed-loop systems are semi-globally uniformly ultimately bounded(SGUUB), and the tracking error converges to an adjustable neighborhood of the origin. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed control design.
This paper focuses on the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown control coefficients and quantized input. The difficulty from the unknown control direction is solved by using the linear state transformation and the Nussbaum gain function(NGF) approach. Based on the combination of input-driven observer, backstepping technique, neural network(NN) parametrization and variable separation method, a novel adaptive output feedback quantized control scheme involving only one adaptive parameter is developed for such systems. The designed quantized controller ensures that all signals of closed-loop systems are semi-globally uniformly ultimately bounded(SGUUB), and the tracking error converges to an adjustable neighborhood of the origin. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed control design.

引文

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