Fuzzy inference system enabled neural network feedforward compensation for position leap control of DC servo motor.

Zhiwen Huang, Yuting Yan, Yidan Zhu, Jiajie Shao, Jianmin Zhu, Dianjun Fang

Abstract read

In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zhiwen HuangSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Yuting YanSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Yidan ZhuCollege of Design and Engineering, National University of Singapore, Singapore, Singapore.

Jiajie ShaoSchool of Mechanical Engineering, Tongji University, Shanghai, China.

Jianmin ZhuSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China. jmzhu_usst@163.com.

Dianjun FangSchool of Mechanical Engineering, Tongji University, Shanghai, China.

Funding

National Key Research and Development Program of China 2022YFE0114300Research Program of Shanghai Key Laboratory of Online Testing and Control Technology Z2022304013

6 · The paper itself

Abstract

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

Indexed as

Artificial neural networkDC servo motorFeedforward compensation controlFuzzy inference systemPosition leap control

Identifiers

PMID39242769

DOI10.1038/s41598-024-71647-1

PMCPMC11379902

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.