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To address the effects of nonlinearities and uncertainties in the speed regulation of permanent magnet synchronous motors (PMSMs), an adaptive PI nonlinear control strategy is introduced. First, a nonlinear system model is developed using the PMSM mathematical model, and an adaptive PI nonlinear control approach is designed. Numerical simulations are conducted to demonstrate that this control method effectively tracks the system's desired values. Then, through a group of comparative simulation experiments, the comparison effect of the designed adaptive PI nonlinear control method and the traditional PI control method is analyzed and compared. Additionally, four PMSM collaborative control system models, including the speed tracking and speed synchronization control structures, are constructed. Finally, a simulation model for a cooperative PMSM control system is developed to evaluate the system's speed tracking capability and the synchronization between multiple motors. The results show that in the designed motor cluster cooperative control system, the PMSM motor cluster using adaptive PI nonlinear control method can achieve cooperative control in speed tracking and speed synchronization, and can maintain stable operation against nonlinear problems and unknown disturbances.
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http://dx.doi.org/10.1016/j.isatra.2025.05.047 | DOI Listing |
ISA Trans
August 2025
Department of Vehicle Engineering and Jiangsu Engineering Research Center of Vehicle Distributed Drive and Intelligent Wire Control Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Department of Vehicle Engineering and Jiangsu Engineering Research Center of Vehi
The steer-by-wire (SbW) system, as the core component of vehicle steering, needs to track the front wheel angle accurately. To mitigate the angle tracking accuracy degradation caused by D-Q axes coupling, time-varying motor electrical parameters, and load disturbance, a fractional-order adaptive fuzzy decentralized tracking control (FAFDTC) strategy is proposed in this paper. First, considering time-varying motor parameters, D-Q axes coupling, and fractional-order characteristics of components, a fractional-order SbW interconnected system is constructed to enhance its ability to characterize nonlinearities, time-varying dynamics, and system coupling.
View Article and Find Full Text PDFPLoS One
September 2025
FAMERP- Faculty of Medicine of São José do Rio Preto, Brazil.
Background: Interprofessional Education (IPE) is widely recognized as essential for fostering collaborative healthcare practices and improving patient outcomes. Despite its acknowledged importance, there remains a notable scarcity of longitudinal research assessing medical students' readiness for IPE across distinct educational stages, particularly within diverse global contexts like Brazil.
Aim: This study sought to address this gap by longitudinally mapping and analyzing the evolution of medical students' readiness for interprofessional learning throughout their academic training at a Brazilian university.
Chaos
September 2025
Indian Institute of Science Education and Research, Tirupati, Andhra Pradesh 517507, India.
Adaptation in complex systems implies a natural ability to change. In networks, adaptation may include a change in structural connectivity, which can lead to a change in collective behavior. When dihedral symmetry is present, i.
View Article and Find Full Text PDFTrauma Violence Abuse
September 2025
Ghent University, Ghent, Belgium.
This study presents a scoping review and crime script analysis of the modus operandi of online romance scammers. Online romance scams are a form of fraud in which perpetrators fabricate online romantic relationships with victims, aiming to emotionally manipulate and, ultimately, financially exploit them. The review aims to synthesize existing research on how scammers operate and to develop a comprehensive crime script that can guide prevention and policy efforts.
View Article and Find Full Text PDFMultivariate Behav Res
September 2025
Department of Statistics, TU Dortmund University, Dortmund, Germany.
Predicting ordinal responses such as school grades or rating scale data is a common task in the social and life sciences. Currently, two major streams of methodology exist for ordinal prediction: traditional statistical models such as the proportional odds model and machine learning (ML) methods such as random forest (RF) adapted to ordinal prediction. While methods from the latter stream have displayed high predictive performance, particularly for data characterized by non-linear effects, most of these methods do not support hierarchical data.
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