对“基于全驱动系统方法的综合制导与控制鲁棒二阶反演设计”的修正

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2025-05-21 DOI:10.1002/rnc.8038

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引用次数: 0

摘要

王伟，陈世生，范军，张宏，“基于全驱动系统方法的鲁棒二阶反步综合制导控制设计”，国际鲁棒与非线性控制学报35 (2025)：2933-2951, https://doi.org/10.1002/rnc.7815.Due关于我们在原投稿中未反映的经费信息的更新，我们文章发表版本中的经费信息不准确(国家重点研发计划项目北京市事业单位高水平创新团队建设与支持项目，资助/奖励号：BPHR20220123；国家自然科学基金项目，资助/奖励号：52272358,62,103,052；国家重点研发计划项目（资助/奖励号：2020YFC1511705）。正确的资助信息是“国家自然科学基金，资助/奖励号：52272358,62103052；北京市事业单位高水平创新团队建设与支持项目，批准/奖励号：BPHR20220123。我们为这个错误道歉。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Correction to “Robust Second-Order Backstepping Design of Integrated Guidance and Control Based on a Fully Actuated System Approach”

W. Wang, S. Chen, J. Fan, and H. Zhang, “ Robust Second-Order Backstepping Design of Integrated Guidance and Control Based on a Fully Actuated System Approach,” International Journal of Robust and Nonlinear Control 35 (2025): 2933–2951, https://doi.org/10.1002/rnc.7815.

Due to an update in our funding information which was not reflected in the original submission, the funding information in the published version of our article was inaccurate (National Key Research and Development Program Project of Construction and Support for high-level Innovative Teams of Beijing Municipal Institutions, Grant/Award Number: BPHR20220123; National Natural Science Foundation of China, Grant/Award Number: 52272358, 62,103,052; National Key Research and Development Program, Grant/Award Number: 2020YFC1511705).

The correct funding information is “National Natural Science Foundation of China, Grant/Award Number: 52272358, 62103052; The Project of Construction and Support for high-level Innovative Teams of Beijing Municipal Institutions, Grant/Award Number: BPHR20220123.”

We apologize for this error.

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来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.