Evidence map›Paper›PMID 42280907›Full record

ArticleSensors (Basel, Switzerland)2026

L-SHADE-Optimized Active Disturbance Rejection for Sensorless PMSM Drives Under Complex Uncertainties.

Xiaoqing Chen, Tao Yang, Bowen Zhang, Ling Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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

4 authors.

Xiaoqing ChenSchool of Electrical Engineering, Chongqing Industry Polytechnic University, Chongqing 401120, China.ORCID 0009-0005-2893-6569
Tao YangSchool of Electrical Engineering, Chongqing Industry Polytechnic University, Chongqing 401120, China.ORCID 0000-0003-1860-2045
Bowen ZhangChongqing Rail Transit Design and Research Institute Co., Ltd., Chongqing 401122, China.
Ling ZhangSchool of Electrical Engineering, Chongqing Industry Polytechnic University, Chongqing 401120, China.

Funding

Chongqing Municipality Education Commission KJQN202403210
6 · The paper itself

Abstract

Sensorless permanent magnet synchronous motor (PMSM) drives rely on accurate rotor electrical angle and speed estimation, vulnerable to noisy currents, quantization, and sensor biases. Fixed-bandwidth phase-locked loops (PLLs) entail an intrinsic trade-off between fast transient tracking and high-frequency noise rejection. This paper proposes an adaptive PLL based on linear active disturbance rejection control (LADRC), where a virtual coordinate formulation treats electrical-angle mismatch as a lumped disturbance estimated online by a linear extended state observer (LESO). The observer bandwidth dynamically adapts to the LESO innovation. To optimize performance, adaptive-law parameters are tuned offline via success-history adaptive differential evolution with linear population size reduction (L-SHADE). Comparative simulations against a proportional-integral PLL indicate substantially improved robustness to measurement noise, analog-to-digital quantization, and current-sensor DC offset. Specifically, the speed root-mean-square error decreases from 68.9r/min to 20.7r/min under 0.15A additive noise, and from 1.55r/min to 0.48r/min under 12-bit quantization at 200r/min. These enhancements reduce reliance on high-precision sensing hardware, offering a practical solution for low-cost, highly reliable motor control in complex industrial environments.

Indexed as

adaptive observerL-SHADEpermanent magnet synchronous motorrobust sensorless controlvariable-bandwidth LADRC-PLL

Identifiers

PMID42280907
PMCPMC13259218

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.