Evidence map›Paper›PMID 40942800›Full record

ArticleSensors (Basel, Switzerland)2025

Piezoelectric Hysteresis Modeling Under a Variable Frequency Based on a Committee Machine Approach.

Francesco Aggogeri, Nicola Pellegrini

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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. Article
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

2 authors.

Francesco AggogeriDepartment of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.ORCID 0000-0001-9414-3763
Nicola PellegriniDepartment of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.ORCID 0000-0002-6108-3941

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Piezoelectric actuators, widely used in micro-positioning and active control systems, show important hysteresis characteristics. In particular, the hysteresis contribution is a complex phenomenon that is difficult to model when the input amplitude and frequency are time-dependent. Existing dynamic physical models poorly describe the hysteresis influence of industrial mechatronic devices. This paper proposes a novel hybrid data-driven model based on the Bouc-Wen and backlash hysteresis formulations to appraise and compensate for the nonlinear effects. Firstly, the performance of the piezoelectric actuator was simulated and then tested in a complete representative domain, and then using the committee machine approach. Experimental campaigns were conducted to develop an algorithm that incorporated Bouc-Wen and backlash hysteresis parameters derived via genetic algorithm (GA) and particle swarm optimization (PSO) approaches for identification. These parameters were combined in a committee machine using a set of frequency clusters. The results obtained demonstrated an error reduction of 23.54% for the committee machine approach compared with the complete approach. The root mean square error (RMSE) was 0.42 µm, and the maximum absolute error (MAE) appraisal was close to 0.86 µm in the 150-250 Hz domain via the Bouc-Wen sub-model tuned with the genetic algorithm (GA).

Indexed as

backlashBouc–Wencommittee machinegenetic algorithmhysteresisparticle swarm optimization

Identifiers

PMID40942800
PMCPMC12431137

What OpenQuestion holds

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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.