Numerical estimation and experimental verification of optimal parameter identification based on modern optimization of a three phase induction motor

The parameters of electric machines play a substantial role in the control system which, in turn, has a great impact on machine performance. In this paper, a proposed optimal estimation method for the electrical parameters of induction motors is presented. The proposed method uses the particle swarm optimization (PSO) technique. Further, it also considers the influence of temperature on the stator resistance. A complete experimental setup was constructed to validate the proposed method. The estimated electrical parameters of a 3.8-hp induction motor are compared with the measured values. A heat run test was performed to compare the effect of temperature on the stator resistance based on the proposed estimation method and the experimental measurements at the same conditions. It is shown that acceptable accuracy between the simulated results and the experimental measurements has been achieved

Extending the multiphysics modelling of electric machines in a digital twin concept

The digital twin is a trending technology that has been applied to different fields. In this work, the use of the digital twin of electric machines in a life cycle perspective is investigated. It presents a literature review of the main concepts and applications of the digital twin in the field. In addition, it discusses the methodological approach to obtain the digital twin of a static electric machine by using its multiphysics model in a reduced order model, to improve the maintenance scheme and estimate the lifetime of its insulation system based on the machine’s temperature profile.info:eu-repo/semantics/publishedVersio

Effects of single-session transcranial direct current stimulation on reactive response inhibition

Transcranial direct current stimulation (tDCS) is widely used to explore the role of various cortical regions for reactive response inhibition. In recent years, tDCS studies reported polarity-, time- and stimulation-site dependent effects on response inhibition. Given the large parameter space in which study designs, tDCS procedures and task procedures can differ, it is crucial to systematically explore the existing tDCS literature to increase the current understanding of potential modulatory effects and limitations of different approaches. We performed a systematic review on the modulatory effects of tDCS on response inhibition as measured by the Stop-Signal Task. The final dataset shows a large variation in methodology and heterogeneous effects of tDCS on performance. The most consistent result across studies is a performance enhancement due to anodal tDCS over the right prefrontal cortex. Partially sub-optimal choices in study design, methodology and lacking consistency in reporting procedures may impede valid conclusions and obscured the effects of tDCS on response inhibition in some previous studies. Finally, we outline future directions and areas to improve research

Industrial applications of the Kalman filter:a review

International audienc

Dynamic Performance Analysis of a Five-Phase PMSM Drive Using Model Reference Adaptive System and Enhanced Sliding Mode Observer

This paper aims to evaluate the dynamic performance of a five-phase PMSM drive using two different observers: sliding mode (SMO) and model reference adaptive system (MRAS). The design of the vector control for the drive is firstly introduced in details to visualize the proper selection of speed and current controllers’ gains, then the construction of the two observers are presented. The stability check for the two observers are also presented and analyzed, and finally the evaluation results are presented to visualize the features of each sensorless technique and identify the advantages and shortages as well. The obtained results reveal that the de-signed SMO exhibits better performance and enhanced robustness compared with the MRAS under different operating conditions. This fact is approved through the obtained results considering a mismatch in the values of stator resistance and stator inductance as well. Large deviation in the values of estimated speed and rotor position are observed under MRAS, and this is also accompanied with high speed and torque oscillations

Comparison between Compensated and Uncompensated PD with Cascade Controller Design of PMDC Motor: Real Experiments

In this paper, we compare two different kinds of position controllers, namely PD (both compensated and uncompensated PD) and Cascade controller (both compensated and uncompensated Cascade).  SIMULINK software is used to implement lumped parameters estimation with UKF. The Proportional-Derivative (PD) controller design by root locus and Cascade controller design have been chosen for our feedback system, and coulomb friction as disturbance is taken into account in the estimation model. The aim of this paper is to find out which controller is better (both compensated and uncompensated PD controller vs. both compensated and uncompensated Cascade controller). Therefore, the objective is to show how useful the parameter estimation for controller design with compensation. Through comparing two position controller designs, the experiment results show that both Compensated Proportional Derivative (PD) controller and Cascade controller Design have much better performance than the Uncompensated ones

Phase matters when there is power : Phasic modulation of corticospinal excitability occurs at high amplitude sensorimotor mu-oscillations

Prior studies have suggested that oscillatory activity in cortical networks can modulate stimulus-evoked responses through time-varying fluctuations in neural excitation-inhibition dynamics. Studies combining transcranial magnetic stimulation (TMS) with electromyography (EMG) and electroencephalography (EEG) can provide direct measurements to examine how instantaneous fluctuations in cortical oscillations contribute to variability in TMS-induced corticospinal responses. However, the results of these studies have been conflicting, as some reports showed consistent phase effects of sensorimotor mu-rhythms with increased excitability at the negative mu peaks, while others failed to replicate these findings or reported unspecific mu-phase effects across subjects. Given the lack of consistent results, we systematically examined the modulatory effects of instantaneous and pre-stimulus sensorimotor mu-rhythms on corticospinal responses with offline EEG-based motor evoked potential (MEP) classification analyses across five identical visits. Instantaneous sensorimotor mu-phase or pre-stimulus mu-power alone did not significantly modulate MEP responses. Instantaneous mu-power analyses showed weak effects with larger MEPs during high-power trials at the overall group level analyses, but this trend was not reproducible across visits. However, TMS delivered at the negative peak of high magnitude mu-oscillations generated the largest MEPs across all visits, with significant differences compared to other peak-phase combinations. High power effects on MEPs were only observed at the trough phase of ongoing mu oscillations originating from the stimulated region, indicating site and phase specificity, respectively. More importantly, such phase-dependent power effects on corticospinal excitability were reproducible across multiple visits. We provide further evidence that fluctuations in corticospinal excitability indexed by MEP amplitudes are partially driven by dynamic interactions between the magnitude and the phase of ongoing sensorimotor mu oscillations at the time of TMS, and suggest promising insights for (re)designing neuromodulatory TMS protocols targeted to specific cortical oscillatory states

Reliability of resting-state EEG modulation by continuous and intermittent theta burst stimulation of the primary motor cortex:a sham-controlled study

Theta burst stimulation (TBS) is a form of repetitive transcranial magnetic stimulation designed to induce changes of cortical excitability that outlast the period of TBS application. In this study, we explored the effects of continuous TBS (cTBS) and intermittent TBS (iTBS) versus sham TBS stimulation, applied to the left primary motor cortex, on modulation of resting state electroencephalography (rsEEG) power. We first conducted hypothesis-driven region-of-interest (ROI) analyses examining changes in alpha (8-12 Hz) and beta (13-21 Hz) bands over the left and right motor cortex. Additionally, we performed data-driven whole-brain analyses across a wide range of frequencies (1-50 Hz) and all electrodes. Finally, we assessed the reliability of TBS effects across two sessions approximately 1 month apart. None of the protocols produced significant group-level effects in the ROI. Whole-brain analysis revealed that cTBS significantly enhanced relative power between 19 and 43 Hz over multiple sites in both hemispheres. However, these results were not reliable across visits. There were no significant differences between EEG modulation by active and sham TBS protocols. Between-visit reliability of TBS-induced neuromodulatory effects was generally low-to-moderate. We discuss confounding factors and potential approaches for improving the reliability of TBS-induced rsEEG modulation.</p

Deep learning-based behavioral profiling of rodent stroke recovery

BACKGROUND Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reproducible and often unsuitable for unraveling the complex behavior after injury. RESULTS Here, we provide a comprehensive 3D gait analysis of mice after focal cerebral ischemia based on the new deep learning-based software (DeepLabCut, DLC) that only requires basic behavioral equipment. We demonstrate a high precision 3D tracking of 10 body parts (including all relevant joints and reference landmarks) in several mouse strains. Building on this rigor motion tracking, a comprehensive post-analysis (with >100 parameters) unveils biologically relevant differences in locomotor profiles after a stroke over a time course of 3 weeks. We further refine the widely used ladder rung test using deep learning and compare its performance to human annotators. The generated DLC-assisted tests were then benchmarked to five widely used conventional behavioral set-ups (neurological scoring, rotarod, ladder rung walk, cylinder test, and single-pellet grasping) regarding sensitivity, accuracy, time use, and costs. CONCLUSIONS We conclude that deep learning-based motion tracking with comprehensive post-analysis provides accurate and sensitive data to describe the complex recovery of rodents following a stroke. The experimental set-up and analysis can also benefit a range of other neurological injuries that affect locomotion