受干扰非线性高超音速飞行器模型的自适应学习型优化跟踪控制系统设计与分析

IF 4.4 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Kai An, ZhenGuo Wang, Wei Huang
{"title":"受干扰非线性高超音速飞行器模型的自适应学习型优化跟踪控制系统设计与分析","authors":"Kai An, ZhenGuo Wang, Wei Huang","doi":"10.1007/s11431-023-2616-3","DOIUrl":null,"url":null,"abstract":"<p>We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone vehicles. The longitudinal nonlinear model is first established and transformed into the control-oriented error equations, and the control scheme is organized by a steady-compensation combination. To overcome and eliminate the impact of model uncertainties and external disturbances, an adaptive radial basis function neural network (RBFNN) is designed by a <i>q</i>-gradient approach. Taking the height-velocity error system with estimated uncertainties into account, the adaptive learning-based optimal tracking control (ALOTC) scheme is proposed by combining the critic-only adaptive dynamic programming (ADP) framework and parameter optimization of system settling time. Furthermore, a novel weight update law is proposed to satisfy the online iteration requirements, and the algorithm convergence and closed-loop stability are discussed by the Lyapunov theory. Finally, four simulation cases are provided to prove the effectiveness, accuracy, and robustness of the proposed scheme for the hypersonic longitudinal control system.</p>","PeriodicalId":21612,"journal":{"name":"Science China Technological Sciences","volume":"73 1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model\",\"authors\":\"Kai An, ZhenGuo Wang, Wei Huang\",\"doi\":\"10.1007/s11431-023-2616-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone vehicles. The longitudinal nonlinear model is first established and transformed into the control-oriented error equations, and the control scheme is organized by a steady-compensation combination. To overcome and eliminate the impact of model uncertainties and external disturbances, an adaptive radial basis function neural network (RBFNN) is designed by a <i>q</i>-gradient approach. Taking the height-velocity error system with estimated uncertainties into account, the adaptive learning-based optimal tracking control (ALOTC) scheme is proposed by combining the critic-only adaptive dynamic programming (ADP) framework and parameter optimization of system settling time. Furthermore, a novel weight update law is proposed to satisfy the online iteration requirements, and the algorithm convergence and closed-loop stability are discussed by the Lyapunov theory. Finally, four simulation cases are provided to prove the effectiveness, accuracy, and robustness of the proposed scheme for the hypersonic longitudinal control system.</p>\",\"PeriodicalId\":21612,\"journal\":{\"name\":\"Science China Technological Sciences\",\"volume\":\"73 1\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science China Technological Sciences\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11431-023-2616-3\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science China Technological Sciences","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11431-023-2616-3","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

考虑到高超音速翼锥飞行器的模型不确定性和外部干扰,我们提出了一种基于自适应学习的高度-速度控制模型优化控制方案。首先建立纵向非线性模型并将其转化为面向控制的误差方程,然后通过稳定补偿组合来组织控制方案。为了克服和消除模型不确定性和外部干扰的影响,采用 q 梯度方法设计了自适应径向基函数神经网络(RBFNN)。考虑到带有估计不确定性的高度-速度误差系统,结合批判式自适应动态编程(ADP)框架和系统稳定时间的参数优化,提出了基于自适应学习的最优跟踪控制(ALOTC)方案。此外,还提出了满足在线迭代要求的新型权值更新法则,并通过李雅普诺夫理论讨论了算法的收敛性和闭环稳定性。最后,通过四个仿真案例证明了所提方案在高超音速纵向控制系统中的有效性、准确性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model

We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone vehicles. The longitudinal nonlinear model is first established and transformed into the control-oriented error equations, and the control scheme is organized by a steady-compensation combination. To overcome and eliminate the impact of model uncertainties and external disturbances, an adaptive radial basis function neural network (RBFNN) is designed by a q-gradient approach. Taking the height-velocity error system with estimated uncertainties into account, the adaptive learning-based optimal tracking control (ALOTC) scheme is proposed by combining the critic-only adaptive dynamic programming (ADP) framework and parameter optimization of system settling time. Furthermore, a novel weight update law is proposed to satisfy the online iteration requirements, and the algorithm convergence and closed-loop stability are discussed by the Lyapunov theory. Finally, four simulation cases are provided to prove the effectiveness, accuracy, and robustness of the proposed scheme for the hypersonic longitudinal control system.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Science China Technological Sciences
Science China Technological Sciences ENGINEERING, MULTIDISCIPLINARY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
8.40
自引率
10.90%
发文量
4380
审稿时长
3.3 months
期刊介绍: Science China Technological Sciences, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research. Science China Technological Sciences is published in both print and electronic forms. It is indexed by Science Citation Index. Categories of articles: Reviews summarize representative results and achievements in a particular topic or an area, comment on the current state of research, and advise on the research directions. The author’s own opinion and related discussion is requested. Research papers report on important original results in all areas of technological sciences. Brief reports present short reports in a timely manner of the latest important results.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信