Motor Parameters Research Articles

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors.

This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots, and above the stator windings. In this case, the sensor was able to measure a resultant magnetic flux density, having both rotor and stator magnetic flux contributions. The present methodology does not require motor parameters for torque prediction. The proposed approach successfully estimated load torque using three optimizers across almost the entire motor load operational range, spanning from 1.5% to 93.9% of the rated load. Four model configurations achieved a mean absolute percentage error (MAPE) less than or equal to 3.7%. Specifically, two models for a 40 × 50 pixel image achieved MAPE of 3.7% and 3%, one model for a 40 × 25 pixel image achieved a MAPE of 3.5%, and one model for a 50 × 80 pixel image achieved a MAPE of 3.3%. This research has been experimentally validated with a 7.5 kW squirrel cage induction machine.

AAV-Mediated CAG-Targeting Selectively Reduces Polyglutamine-Expanded Protein and Attenuates Disease Phenotypes in a Spinocerebellar Ataxia Mouse Model.

Polyglutamine (polyQ)-encoding CAG repeat expansions represent a common disease-causing mutation responsible for several dominant spinocerebellar ataxias (SCAs). PolyQ-expanded SCA proteins are toxic for cerebellar neurons, with Purkinje cells (PCs) being the most vulnerable. RNA interference (RNAi) reagents targeting transcripts with expanded CAG reduce the level of various mutant SCA proteins in an allele-selective manner in vitro and represent promising universal tools for treating multiple CAG/polyQ SCAs. However, it remains unclear whether the therapeutic targeting of CAG expansion can be achieved in vivo and if it can ameliorate cerebellar functions. Here, using a mouse model of SCA7 expressing a mutant Atxn7 allele with 140 CAGs, we examined the efficacy of short hairpin RNAs (shRNAs) targeting CAG repeats expressed from PHP.eB adeno-associated virus vectors (AAVs), which were introduced into the brain via intravascular injection. We demonstrated that shRNAs carrying various mismatches with the CAG target sequence reduced the level of polyQ-expanded ATXN7 in the cerebellum, albeit with varying degrees of allele selectivity and safety profile. An shRNA named A4 potently reduced the level of polyQ-expanded ATXN7, with no effect on normal ATXN7 levels and no adverse side effects. Furthermore, A4 shRNA treatment improved a range of motor and behavioral parameters 23 weeks after AAV injection and attenuated the disease burden of PCs by preventing the downregulation of several PC-type-specific genes. Our results show the feasibility of the selective targeting of CAG expansion in the cerebellum using a blood-brain barrier-permeable vector to attenuate the disease phenotype in an SCA mouse model. Our study represents a significant advancement in developing CAG-targeting strategies as a potential therapy for SCA7 and possibly other CAG/polyQ SCAs.

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications.

Simplified V/f Control Algorithm for Reduction of Current Fluctuations in Variable-Speed Operation of Induction Motors

This paper introduces a straightforward control strategy aimed at the reduction of current fluctuations within the low-frequency domain of open-loop V/f control in induction motor drives. Traditional control techniques necessitate the addition of a current compensator based on motor parameters and the use of digital filters such as band-pass or high-pass filters. These methods, however, rely on precise motor parameters and involve complex filter design and implementation. The proposed control is capable of suppressing current fluctuations without controlling the slip of the induction motor. The proposed control strategy generates the forced rotation angle and command input voltage using the V/f block and outputs the d-axis voltage using a proportional integral controller to keep the d-axis current constant at zero. The difference between the command input voltage and the d-axis voltage is applied as the q-axis voltage and then applied through SVPWM. In order to verify the effectiveness of the proposed control, the proposed control is implemented and analyzed using power simulation based on the results of the analysis of the causes of current fluctuations in the induction motor. Finally, the effect of suppressing current fluctuations of the induction motor is verified through experimental results. In the 10~19 Hz range, where the conventional V/f control method resulted in current fluctuation rates exceeding 10% and peaking at 113.3% at 13 Hz, the proposed method suppressed the fluctuation rate to below 8.6% across all frequencies. This paper validates the effectiveness of the proposed control strategy through these results.

A Novel Current Measurement Offset Error Compensation Method Based on the Adaptive Extended State Observer for IPMSM Drives

Offset error from the measured phase current mostly causes a performance afflicted for interior permanent-magnet synchronous motor (IPMSM) drives, and innovative solutions are eagerly sought. Considering this, this paper proposes an adaptive extended state observer (AESO) to compensate the current measurement offset error (CMOE) without additional hardware. In the proposed scheme, the AESO is designed in a stationary coordinate system to estimate CMOE in DC form. Notably, the proposed AESO possesses a second-order low-pass filtering characteristics. With this, the proposed method shows a high robustness against the disturbances from the AC components caused by motor parameter changes, etc. Furthermore, the adaptive gains enable the proposed scheme to maintain a satisfactory performance under the motor frequency changes. The feasibility of the proposed scheme is thoroughly verified by extensive experimental tests based on the test bench of a 3-kW IPMSM drives.

The association between childhood trauma and motor structure in obsessive-compulsive rituals: An ethological and evolutionary approach.

Ethological models have highlighted a specific motor structure of compulsions in obsessive-compulsive disorder (OCD), based on the rigid repetitions of acts, and with the adaptive significance of facing unpredictable conditions. Such an evolutionary mechanism might explain the robust association between childhood traumatic experiences (CTEs) and OCD. However, a relationship between CTEs and the motor structure of compulsions has not been investigated yet. The first objective of the study was to confirm a specific motor structure of OCD compulsions with respect to control behaviors; the second objective was to assess a possible association between the motor structure of compulsions and CTEs severity. Thirty-two OCD outpatients (13 female, Mage = 44.50 years, SE = 19.71) and 27 healthy controls (10 female, Mage = 37.62 years, SE = 16.20), matched for sex and age, provided a videotape of their compulsions and corresponding ordinary acts, respectively. Behavior was scored with the software "Observer." Participants were administered the Yale-Brown Obsessive Compulsive Scale and the Childhood Trauma Questionnaire. A dependent t test was used to compare the motor structure of behavior between the groups; Pearson's correlations to investigate associations between motor parameters and CTEs. Compulsions showed a specific motor structure due to the repetition of functional and nonfunctional acts. CTEs severity was especially associated with the repetition of functional acts, independently from OCD severity. Our findings, in confirming a peculiar motor structure for OCD compulsions, hint for the first time at a link between CTEs and compulsive repetition of functional acts, which would represent a plastic developmental response to CTEs unpredictability. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

FCS-MPC for Three-Level NPC Inverter-Fed SPMSM Drives Without Information of Motor Parameters and DC Capacitor

The conventional finite control set-model predictive control (FCS-MPC) suffers from poor robustness against model mismatches. This paper presents an improved FCS-MPC with a reconstructed mathematical model to predict current variations without using a lookup table (LUT) or motor parameters. The model coefficients and current variations related to different voltage vectors can be updated during each control period. As a result, the prediction error is significantly reduced at low switching frequency when compared with the prior LUT-based model-free predictive current control (MFPCC). Additionally, the tracking accuracy of the proposed method at high speeds is improved due to the elimination of approximation error. Furthermore, a simple scheme is developed to suppress neutral-point-potential drift without knowledge of the dc-bus capacitor. Simulation and experimental tests, along with comparisons with prior arts carried out on a three-level inverter-fed surface-mounted permanent magnet synchronous motor drive, confirm the superiority of the proposed method.

Model-Free Predictive Current Control of SPMSM Based on Enhanced Extended State Observer

Conventional model-free predictive current control (MFPCC) using an extended state observer (ESO) based on ultra-local model significantly decreased the sensitivity to partial motor parameters. However, the performance and stability of ESO will be degraded in the face of amplified high-frequency (HF) perturbation caused by inductance parameter mismatch. To solve the above problem, an enhanced ESO-based MFPCC method is proposed in this article. On the basis of constructing the discrete equation of ESO, an accurate and fast calculation of inductance parameter is proposed, which is real-time convergence to the actual value by deadbeat control principle, and fed back to the lumped disturbance and current observation calculation. It effectively eliminates the effects of HF perturbation on ESO. Simultaneously, the inductance and compensated lumped disturbance obtained from ESO are provided to the MFPCC command voltage calculation module, further improving the parameter robustness of MFPCC. The stability of the proposed ESO is ensured by the design of observer gain. Experiments are carried out on 1kW surfaced permanent magnet synchronous machines (SPMSM) test bench. Compared with the conventional ESO-based MFPCC method, the proposed method is superior in dynamic response, steady-state performance, and parameter robustness.

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

This paper aims to design an accurate angular position control for DC motors using a proportional integral derivative (PID) controller. The estimated DC motor parameters have been calculated using the parameter estimator toolbox in MATLAB, Arduino Mega 2560 and speed sensor to build an accurate model in MATLAB. The optimized PID coefficients are found for the DC motor model using the firefly algorithm (FA), which aims to make the actual angle match the desired value without overshooting and oscillations. The PIC16F877A microcontroller was used to implement the code based on optimized PID coefficients found in MATLAB/Simulink to generate the suitable pulse width modulation (PWM) output. In this work, step input was tested to analyze the characteristics of the system response in terms of rise time, settling time and overshoot. It was found that the controller output response curve which is produced from FA-based-PID reached the desired position without overshoot and any oscillations. The findings established that closed-loop control of any system using a system identification toolbox and optimized PID technique can be applied in real applications using low-cost controllers and sensors such as PIC16F877A microcontroller and analog rotary position sensor, respectively.

Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke.

Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity. Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand. The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age. The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.

Robust flux angle control of induction motors to improve efficiency at light loads

AbstractVector control of induction motors has provided us with both good performance and acceptable efficiency at rated loads. Using this method at light loads reduces the efficiency due to the adjustment of motor flux at the rated value. Electric motors are normally used even at lower loads than the rated ones and often selected oversized. Enhancing the efficiency of induction motors at light loads will significantly reduce electric energy consumption. A novel method called Flux Angle Control (FAC) of the induction motor is presented to improve efficiency in the steady state at light loads. To increase the accuracy of the induction motor model we consider iron losses. This method controls the angle between the stator flux (stator current) and the d‐axis (rotor flux direction). The primary aim of the flux angle control method is to reduce the flux level and increase the flux angle at light loads. The optimal flux angle is sensitive to some induction motor parameters. The proposed method offers several advantages compared to similar methods, including high efficiency, minimal sensitivity of the optimal flux angle to changes in motor parameters, a reduced number of calculations, and easy implementation. The validity of this method is examined through experiments.

Review Soft Switching Methods for Parameter Monitoring of AC Machine Drives

Industrial applications continue to primarily use induction motors (AC Motor), and dependable performance depends on efficient monitoring and control of these motors' properties. There are several ways to accomplish this, and the focus of this project is on controlling and monitoring a single-phase induction engine. Important parameters of the induction motor, such as temperature, current, and voltage, are continually monitored by means of a module that is fitted with sensors and transducers. After being gathered, the data is sent to an information processing unit, which shows its parameters on a display. Through a LCD display gateway, the system integrates both automatic and human control techniques to start or stop the AC Motor, reducing the possibility of system failures. It is projected that this system's deployment will improve operational effectiveness by guaranteeing ongoing observation, averting problems, and encouraging preventative maintenance. Top of Form Keywords— Arduino Controller, LCD Display, Parameter Monitoring, AC motor, Parameter Controlling etc.

A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes.

To date, clinical expert opinion is the gold standard diagnostic technique for Parkinson's disease (PD), and continuous monitoring is a promising candidate marker. This study assesses the feasibility and performance of a new wearable tool for supporting the diagnosis of Parkinsonian motor syndromes. The proposed method is based on the use of a wrist-worn measuring system, the execution of a passive, continuous recording session, and a computation of two digital biomarkers (i.e., motor activity and rest tremor index). Based on the execution of some motor tests, a second step is provided for the confirmation of the results of passive recording. In this study, fifty-nine early PD patients and forty-one healthy controls were recruited. The results of this study show that: (a) motor activity was higher in controls than in PD with slight tremors at rest and did not significantly differ between controls and PD with mild-to-moderate tremor rest; (b) the tremor index was smaller in controls than in PD with mild-to-moderate tremor rest and did not significantly differ between controls and PD patients with slight tremor rest; (c) the combination of the said two motor parameters improved the performances in differentiating controls from PD. These preliminary findings demonstrate that the combination of said two digital biomarkers allowed us to differentiate controls from early PD.

Sleep deprivation induces late deleterious effects in a pharmacological model of Parkinsonism.

Parkinson's disease is a degenerative, chronic and progressive disease, characterized by motor dysfunctions. Patients also exhibit non-motor symptoms, such as affective and sleep disorders. Sleep disorders can potentiate clinical and neuropathological features and lead to worse prognosis. The goal of this study was to evaluate the effects of sleep deprivation (SD) in mice submitted to a progressive pharmacological model of Parkinsonism (chronic administration with a low dose of reserpine). Male Swiss mice received 20 injections of reserpine (0.1mg/kg) or vehicle, on alternate days. SD was applied before or during reserpine treatment and was performed by gentle handling for 6h per day for 10 consecutive days. Animals were submitted to motor and non-motor behavioral assessments and neurochemical evaluations. Locomotion was increased by SD and decreased by reserpine treatment. SD during treatment delayed the onset of catalepsy, but SD prior to treatment potentiated reserpine-induced catalepsy. Thus, although SD induced an apparent beneficial effect on motor parameters, a delayed deleterious effect on alterations induced by reserpine was found. In the object recognition test, both SD and reserpine treatment produced cognitive deficits. In addition, the association between SD and reserpine induced anhedonic-like behavior. Finally, an increase in oxidative stress was found in hippocampus of mice subjected to SD, and tyrosine hydroxylase immunoreactivity was reduced in substantia nigra of reserpine-treated animals. Results point to a possible late effect of SD, aggravating the deficits in mice submitted to the reserpine progressive model of PD.

MODEL CHARACTERISTICS OF PHYSICAL FITNESS OF PROFESSIONAL SOLDIERS OF TWO AGE GROUPS

The purpose of the work is to analyze the integration and interaction of various components of the structure of the physical condition of professional servicemen of the military unit of the West Pomeranian Voivodeship. 50 professional soldiers aged 26-30 and 50 professional soldiers aged 36-40 took part in the study. The scope of the study included the registration of 12 parameters of physical (FD) and motor (MD) development. Mathematical analysis of the obtained research results was carried out with the help of statistics and graphics programs Statistics 13.5 and Excel-16. The results show that the age of the two groups of servicemen does not affect the value of the FD indicators. However, in the group of servicemen over 30 years old, the indicators of FD are integrated more strongly. Among the parameters of MD, the biggest changes occurred in the parameters of the 3000 m run and body inclinations in the age group of servicemen under 30 years of age. Studies have shown that most indicators of FD and MD are interrelated and depend on each other. Among the FD indicators, the following indicators have the largest number of strong relationships: BMI, water content in the body (%), ratio of body weight to body surface area (kg/m2) and body surface area (S) in m2. Among the MD indicators in the group of military personnel under the age of 30, the largest number of strong interrelationships is manifested in the parameters of running 3000 m (s), body tilts 2', pull-ups on a high bar. However, in the group of servicemen over 30 years of age, the strongest relationships are observed for the parameters of pull-ups and 2' trunk bends. As a result of factor, regression and canonical analysis, the main factors and information indicators of the structure of the physical condition of military personnel were determined. Models of their interaction were developed and the specifics of the mutual influence of the group of FD indicators and the group of MD indicators were investigated. The MD scores of professional soldiers have a greater effect on the FD scores than the FD scores on the MD scores.

The Significance of Chronic Fatigue in the Post-Covid Consultation and its Consequences for Outpatient Rehabilitation in the Context of Statutory Accident Insurance

The post-COVID consultation (PCC) is offered as part of a comprehensive range of treatment services provided by the statutory accident insurance for post-COVID patients to determine individual recommendations for further care. The aim of the study was to record the main symptoms and the associated restrictions on social and occupational participation in order to derive consequences for outpatient rehabilitation. In addition to a medical examination and a psychological consil, numerous assessments were carried out to evaluate the biopsychosocial state of health. 373 female (82.2%) and 81 male patients aged between 40 and 60 years from the professions of health and care services, education and pedagogy participated in the PCC since April 2021. Nearly all patients (98.2%) reported fatigue as a cardinal symptom of their post-COVID complaints, in combination with subjectively experienced limitations in brain functioning in over 73% of cases. The duration of the symptomatology persisted for an average of 14-15 months in both female and male insured persons. Thus, over 85% of the total sample can be classified as cases of chronic fatigue (Fatigue Scale). The severity of fatigue also proportionally affects quality of life (SF-36), feelings of anxiety and depression (HADS), psychological resilience (RS-13), and motor parameters such as maximum grip strength and endurance capacity. 54.3% of the patients also received a suspected mental diagnosis and 38.1% a recommendation for further neuropsychological diagnostics. For further treatment of the leading symptom of chronic fatigue, a multimodal and interdisciplinary outpatient rehabilitation is recommended, which should be oriented towards the treatment of the diagnosis of chronic fatigue syndrome (ME/CFS) and thus in particular towards a psychoeducational and rather than a curative therapeutic approach, and should consider aftercare strategies. Confirmed mental disorders and neuropsychological deficits are to be treated in addition.

Model-free predictive current control based on improved sliding mode disturbance observer

The motor parameters of permanent magnet synchronous motor (PMSM) can be mismatched in operation due to temperature variations and magnetic saturation. The parameter mismatch leads to a significant decrease in the robustness of traditional deadbeat predictive current control (DPCC), which leads to issues such as current static error and motor vibration noise. In this paper, a model-free predictive current control (MFPCC) method based on an improved sliding mode disturbance observer (SMDO) was proposed. The method estimated the total disturbance of the system using the improved SMDO, which could effectively suppress sliding mode chattering and accelerate the convergence speed of current errors. Subsequently, the predicted control voltage was calculated using a hyper-local model and compensated for the time delay. The experimental results demonstrated that the improved MFPCC-SMDO method generated lower motor noise compared to the traditional DPCC method when the controller’s inductance parameters are mismatched.

Optimization design of brushless DC motor based on improved JAYA algorithm

Brushless direct current motor is widely used in industrial production because of its simple structure, wide speed range and low noise. To improve the operation efficiency of brushless DC motor and reduce the production and application costs, the optimization of brushless DC motor is analyzed by introducing the JAYA algorithm. This method determines the optimal parameters of a brushless DC motor using the theory of electromagnetic structure parameter selection and efficiency calculation. The population diversity of the JAYA algorithm is improved through an empirical learning strategy, and an adaptive strategy is introduced to balance the development ability and search performance of the algorithm. This ensures population diversity and improves convergence speed. The experiment showcases that the improved JAYA algorithm has a lower rank average in unimodal function operations, demonstrating stronger local development ability and better stability. It exhibits strong search ability in many local optima of multimodal functions. Moreover, the motor's average efficiency after optimization is 94.48%. The algorithm reaches the global optimum after approximately 40 iterations and offers faster convergence speed and higher accuracy. The adaptive JAYA algorithm is stable at around 93% when the number of iterations reaches 90, with a maximum efficiency of 95.3%. It is 5–12 percentage points higher than the other three comparison algorithms. The optimal solution of the motor parameters in the adaptive JAYA algorithm is closest to the theoretical parameter optimization value, meeting both the constraints of variables and the constraints of the model. The stator diameter, tooth magnetic induction, winding current density, air gap magnetic induction, and stator yoke magnetic induction values are 201.5 mm, 1.8 T, 2.049 A/mm2, 0.63 T, and 0.91 T, respectively. The research overcomes the problem of parameter optimization in the optimization design of brushless DC motor, improving their economic value of brushless DC motor in industrial production and application.

Optimal design of a BLDC motor using African vulture optimization algorithm

BLDC motors are extensively favoured in robots, electric vehicles, and many industrial applications due to their superior torque, efficiency, and speed control features as compared to traditional motors. In this paper, collective intelligence-based optimization approaches are used to improve the cost-effectiveness, volume, and total loss of a BLDC motor. The geometric dimensions of the motor are considered the optimization parameters. The study employs several optimization algorithms, such as the Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Cuckoo Search Algorithm (COA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the African Vulture Optimization algorithm (AVOA), used for the optimal BLDC motor design. The investigation can provide data on the convergence rates and optimal design parameters of a BLDC motor through optimization approaches. The standard deviation has also been calculated for each optimization technique to assess the stability and consistency of finding the optimum solution. The paper also includes a sensitivity analysis of the AVOA regulating parameter to obtain the best solutions. In comparison to GA, PSO, COA, GWO, and WOA, the AVOA technique achieves faster convergence. Additionally, the ANOVA test is employed as a statistical test to validate the efficacy of the AVOA. According to the results, AVOA outperforms all other optimization algorithms for the design of the BLDC motor.

The Effect of Transcranial Photobiomodulation for Motor Performance Improvement in Patients with Brain Disorders

Background: Transcranial photobiomodulation (PBM) therapy has emerged as a promising alternative therapeutic option for the management of neurological and psychiatric disorders. However, the underlying mechanisms of PBM therapy and its effects on motor performance in brain disorders are not yet fully understood. The aim of this literature review is to provide a more detailed and evidence-based explanation of the rationale and intent behind the correlation between PBM therapy and its effects on motor performance in brain disorders. Methods: A literature search was performed in the databases "PubMed/Medline", "Scopus," and "Google Scholar" for all relevant English language papers. A combination of different keywords was used for the database search. Video articles, patents, review articles, book chapters, articles using other transcranial methods, non-transcranial PBM, and case reports were excluded. Results: Out of the 2174 papers, 18 addressed the effect of PBM on motor performance. Among these, four studies were on ischemic stroke models and individuals with stroke, six studies on models associated with traumatic brain injury (TBI), five studies on models associated with neurodegenerative diseases and Parkinson's disease, and four studies related to models and patients with central nervous system inflammation. All studies have shown that motor parameters improve with PBM. In two studies on healthy individuals, 65 showed improvement in motor function and 16 showed improvement in motor evoked potential. In most studies (n=10), the wavelength used was between 800 and 900 nm. Near-infrared or LED continuous light was used in most studies. However, two studies compared the effects of pulsed and continuous waves and found the superiority of pulsed over continuous waves. Conclusions: PBM therapy appears to be useful in brain injury, inducing changes at the behavioral, motor, cellular, and chemical levels. Recent studies suggest that PBM therapy may have potential benefits in improving motor performance in brain disorders, including stroke, traumatic brain injury, Parkinson's disease, and demyelination. However, further research is needed to determine the optimal parameters for PBM therapy and to investigate its effects on motor function in different brain disorders. Overall, PBM therapy appears to be a promising therapeutic option for brain injury and warrants further investigation.