Neural Network Modeling-Based Anti-Disturbance Tracking Control for Hypersonic Flight Vehicle Models

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• 英文论文题名：Neural Network Modeling-Based Anti-Disturbance Tracking Control for Hypersonic Flight Vehicle Models
• 论文作者：XU Lubing ; YI Yang ; SHAO Liren ; ZHENG Weixing
• 英文论文作者：XU Lubing ; YI Yang ; SHAO Liren ; ZHENG Weixing ; College of Information Engineering ; Yangzhou University ; School of Computing ; Engineering and Mathematics ; University of Western Sydney
• 年：2017
• 作者机构：College of Information Engineering,Yangzhou University;School of Computing,Engineering and Mathematics,University of Western Sydney;
• 英文论文关键词：anti-disturbance control ; hypersonic flight vehicle ; tracking control ; neural network identifier ; disturbance observer
• 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：TP183;V249
• 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：247k
• 原文格式：D
• 会议级别：全国

摘要

This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle(HFV) models by using neural network(NN) identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbanceobserver-based-control(DOBC) algorithm with adaptive regulation laws is thus presented to estimate the nonlinear disturbances.By integrating the estimated value of disturbances with the PI feedback control input, a composite controller based on convex optimization theory is generated to ensure the satisfactory stability and dynamical tacking convergence of HFV models. Finally,a numerical example for HFV models is included to illustrate the effectiveness of the theoretical results.
This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle(HFV) models by using neural network(NN) identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbanceobserver-based-control(DOBC) algorithm with adaptive regulation laws is thus presented to estimate the nonlinear disturbances.By integrating the estimated value of disturbances with the PI feedback control input, a composite controller based on convex optimization theory is generated to ensure the satisfactory stability and dynamical tacking convergence of HFV models. Finally,a numerical example for HFV models is included to illustrate the effectiveness of the theoretical results.

引文

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