Evidence map›Paper›PMID 39064436›Full record

ArticleMicromachines2024

Adaptive Internal Model Backstepping Control for a Class of Second-Order Electromagnetic Micromirror with Output Performance Constraints and Anomaly Control.

Huasen Gan, Yi Qin, Jinfeng Zhang, Cixing Lv, Zhonghua Chen, Yaohua Hu

Abstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Huasen GanSchool of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523000, China.
Yi QinSchool of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523000, China.ORCID 0000-0002-8517-1137
Jinfeng ZhangCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, China.
Cixing LvSchool of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523000, China.ORCID 0000-0002-7973-0940
Zhonghua ChenDongguan LongTTech Company Ltd., Dongguan 523000, China.
Yaohua HuSchool of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523000, China.

Funding

Guangdong Provincial Department of Education Innovation Strong School Program 2022ZDZX1031R&D projects in key areas of Guangdong Province 2022B0303010001
6 · The paper itself

Abstract

This paper investigates the asymptotic tracking problem for a class of second-order electromagnetic micromirror model with output performance constraints and anomaly control, which is subject to model parameter uncertainties and external disturbances. Specifically, this paper formulates the trajectory tracking control problem of an electromagnetic micromirror as a closed-loop control trajectory tracking problem based on the general solution framework of output regulation. Moreover, the extended internal model is introduced to reformulate the closed-loop control problem into a state stabilization problem of the augmented system. Based on the augmented system, an internal model backstepping controller is proposed by integrating the barrier Lyapunov Functions (BLF) and the Nussbaum gain function with the backstepping structure.This controller not only satisfies the output performance constraints of the micromirror, but also maintains the control performance in anomalous control situations. The final performance simulation demonstrates the efficacy of the proposed controller.

Indexed as

anomaly controlelectromagnetic micromirroroutput performance constraintoutput regulation

Identifiers

PMID39064436
PMCPMC11279285

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.