Search Results (1,044)

As global industry enters the digital era, automation is becoming increasingly pervasive. Due to their superior efficiency and reliability, Permanent Magnet Synchronous Motors (PMSMs) are playing an increasingly prominent role in industrial applications. Sliding Mode Control (SMC) has emerged as a modern control strategy that is widely employed not only in PMSM drive systems, but also across broader power and industrial control domains. This technique effectively mitigates key challenges associated with PMSMs, such as nonlinear behavior and susceptibility to external disturbances, thereby enhancing the precision of speed and torque regulation. This paper provides a thorough review and evaluation of recent advancements in SMC as applied to PMSM control. It outlines the fundamentals of SMC, explores various SMC-based strategies, and introduces integrated approaches that combine SMC with optimization algorithms. Furthermore, it compares these methods, identifying their respective strengths and limitations. This paper concludes by discussing current trends and potential future developments in the application of SMC for PMSM systems. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

This paper develops multiple control strategies for a precision long-travel stage, which comprises motor and piezoelectric transducer (PZT) stages. First, the PZT stage is equipped with control switching and model estimation mechanisms to achieve nm-level precision within 100 μm distances. The control switching mechanism selects the optimal control sequences by predicting system responses, while the model estimation algorithm updates the system model to improve the prediction accuracy. Second, the motor stage is equipped with gain-scheduling and feedforward control mechanisms to achieve a maximum displacement of 100 mm with a resolution of 0.1 μm. The gain scheduling control modifies the control gain in accordance with tracking errors, while the feedforward control can mitigate phase lags. We integrate the stages to achieve nm-level precision over long travels and conduct simulations and experiments to show the advantages of the control mechanisms. Finally, we apply the long-travel precision stage to fabricate micro-lenses using two-photon polymerization and evaluate the fabricated micro-lenses’ optical characteristics to illustrate the merits of the control strategies. Full article

The accurate estimation of parameters in dynamical systems stands for an open key research issue in modeling, control, and fault diagnosis. The presence of noise in input and output signals poses a serious challenge for accurate real-time dynamical system parameter estimation. This paper proposes a new robust algebraic parameter estimation methodology for integer-order dynamical systems that explicitly incorporates the signal filtering dynamics within the estimator structure and enhances noise attenuation through fractional differentiation in frequency domain. The introduced estimation methodology is valid for Liouville-type fractional derivatives and can be applied to estimate online the parameters of differentially flat, oscillating or vibrating systems of multiple degrees of freedom. The parametric estimation can be thus implemented for a wide class of oscillating or vibrating, nth-order dynamical systems under noise influence in measurement and control signals. Positive values are considered for the inertia, stiffness, and viscous damping parameters of vibrating systems. Parameter identification can be also used for development of actuators and control technology. In this sense, validation of the algebraic parameter estimation is performed to identify parameters of a differentially flat, permanent-magnet direct-current motor actuator. Parameter estimation for both open-loop and closed-loop control scenarios using experimental data is examined. Experimental results demonstrate that the new parameter estimation methodology combining signal filtering dynamics and fractional calculus outperforms other conventional methods under presence of significant noise in measurements. Full article

Background: Transcranial Magnetic Stimulation (TMS) has been employed in athletes from various sports to enhance performance; however, no data have focused on its effects in mixed martial arts (MMA). This study investigated the effects of intermittent theta-burst stimulation (iTBS), an alternative modality of TMS, on motor performance and plasma oxidative-stress biomarkers of ten male mixed martial arts (MMA) athletes. Methods: A randomized controlled trial was conducted with ten professional MMA athletes (aged 18–35 years). Participants were assigned to the experimental (iTBS) or placebo groups. Baseline and post-intervention performance were assessed using the Multiple Frequency Speed of Kick Test (MFSKT) and a Progressive Speed Kick Test (PSKT). Plasma biomarkers of oxidative stress, including thiols, total antioxidant capacity, 8-isoprostane, and carbonylated proteins, were measured before and after the performance tests in both groups. The iTBS was applied to the left primary motor cortex at an 80 motor threshold for the experimental group and at sub-threshold levels for the placebo group. A two-way ANOVA for paired groups, followed by Bonferroni post-hoc tests, were used to analyze the repeated measures, with the significance level set at p < 0.05. Results: The findings revealed significant improvements on the MFSKT [25.4 (±1.2) kicks vs. 20.8 (±1.4) kicks] and the PSKT [27.6 (±1.5) vs. 22.4 (±1.7) kicks] in the iTBS group vs. placebo, respectively. No significant differences were observed between the groups in terms of the serum redox balance biomarkers pre- and post-test, suggesting a limited impact on redox homeostasis despite performance enhancement. The placebo group showed no notable changes in either test or biomarker levels. Conclusions: These results highlight the improved physical performance in MMA athletes without altering redox biomarkers in the blood—emphasizing its applicability for neuromodulation in sports. Full article

Objectives: Sub-second motor timing is critical for skilled performance in domains such as sport, music, and safety-critical multitasking; however, its robustness under cognitive load remains unresolved. Dual-task paradigms offer a method to test whether attentional demands selectively disrupt temporal precision. This study intended to investigate the effects of cognitive load on rhythmic finger tapping at a sub-second interval. Methods: A sample of 103 college students (19–25 years) performed a synchronization–continuation tapping task at 500 ms intervals under single- and dual-task conditions across five trials. The dual-task condition included a distracting letter-span task imposing working memory load. Inter-response intervals (IRIs), their variability (IRI SD), and accuracy (AI) were analyzed using linear mixed-effects models. Results: Tapping intervals were consistently shorter than the 500 ms target by approximately 70 ms in both conditions, showing anticipatory mechanisms that remained stable under cognitive load. Mean accuracy did not vary between single- and dual-task conditions. By contrast, temporal variability was significantly higher in the dual-task condition, reflecting diminished trial-to-trial consistency. These effects continued throughout trials and were supported by model estimates, which indicated robust between-subject variability but selective disruption of consistency rather than mean performance. Conclusions: Dual-tasking selectively hinders temporal stability in sub-second motor timing while ensuring that the reproduction and accuracy of the mean interval remain unchanged. This pattern supports dual-process accounts of timing, suggesting distinct roles for predictive control and attentional allocation. The results have applied relevance for situations requiring precise rhythmic performance under cognitive load, including sports, ensemble music, and safety-critical tasks. Full article

The development of electromechanical linear actuators (EMLAs) aims at compactness, energy efficiency, and high reliability. Conventional design methods often rely on costly prototypes and individual considerations of mechanics, electromagnetics, and control dynamics. This leads to long development cycles, inadequate treatment of nonlinear effects, and suboptimal performance. To address these challenges, our paper introduces a novel hybrid design methodology, integrating Analytical Modeling, Finite Element Analysis (FEA), Genetic Algorithms (GAs), and targeted experiments. Analytical Modeling provides rapid sizing, FEA combined with a GA refines geometry, and targeted experiments quantify nonlinear effects (friction, wear, thermal variability, and dynamic resonances). Unlike conventional methods, the integration is performed within iterative loops, using empirical data to refine simulation assumptions. As a result, development time is reduced by 30% and nonlinear effects are precisely addressed. The method is demonstrated on an automotive-grade EMLA. Its design is based on a claw-pole Permanent Magnet Stepper Motor, a trapezoidal lead screw, and an open-loop control with Hall effect end-position detection. After applying the method, the EMLA delivers more than 40 N of push force and achieves 600,000 actuations under the required conditions, making it suitable for various applications. Full article

Background: Hemiplegic cerebral palsy (HCP) is a motor dysfunction disorder resulting from perinatal developmental brain injury, predominantly impairing upper limb function in children. Nonetheless, there has been insufficient research on the brain activation patterns and inter-brain coordination mechanisms of HCP children when performing motor control tasks, especially in contrast to children with typical development(CD). Objective: This cross-sectional study employed functional near-infrared spectroscopy (fNIRS) to systematically compare the cerebral blood flow dynamics and brain network characteristics of HCP children and CD children while performing upper-limb mirror training tasks. Methods: The study ultimately included 14 HCP children and 28 CD children. fNIRS technology was utilized to record changes in oxygenated hemoglobin (HbO) signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) of the subjects while they performed mirror training tasks. Generalized linear model (GLM) analysis was used to compare differences in activation intensity between HCP children and CD children in the prefrontal cortex and motor cortex. Finally, conditional Granger causality (GC) analysis was applied to construct a directed brain network model, enabling directional analysis of causal interactions between different brain regions. Results: Brain activation: HCP children showed weaker LPFC activation than CD children in the NMR task (t = −2.032, p = 0.049); enhanced LMC activation in the NML task (t = 2.202, p = 0.033); and reduced RMC activation in the MR task (t = −2.234, p = 0.031). Intragroup comparisons revealed significant differences in LMC activation between the NMR and NML tasks (M = −1.128 ± 2.764, t = −1.527, p = 0.025) and increased separation in RMC activation between the MR and ML tasks (M = −1.674 ± 2.584, t = −2.425, p = 0.031). Cortical effective connectivity: HCP group RPFC → RMC connectivity was weaker than that in CD children in the NMR/NML tasks (NMR: t = −2.491, p = 0.018; NML: t = −2.386, p = 0.023); RMC → LMC connectivity was weakened in the NMR task (t = −2.395, p = 0.022). Conclusions: This study reveals that children with HCP exhibit distinct abnormal characteristics in both cortical activation patterns and effective brain network connectivity during upper limb mirror training tasks, compared to children with CD. These characteristic alterations may reflect the neural mechanisms underlying motor control deficits in HCP children, involving deficits in prefrontal regulatory function and compensatory reorganization of the motor cortex. The identified fNIRS indicators provide new insights into understanding brain dysfunction in HCP and may offer objective evidence for research into personalized, precision-based neurorehabilitation intervention strategies. Full article

Background/Objectives: Amyotrophic Lateral Sclerosis (ALS) is a progressive and fatal neurodegenerative disease characterized by motor neuron loss, muscle weakness, and respiratory dysfunction, often culminating in ventilatory failure. Evidence suggests that High-Definition Transcranial Direct Current Stimulation (HD-tDCS) may modulate motor cortical excitability and potentially influence motor and respiratory function in ALS. This study aims to evaluate the effects of home-based HD-tDCS applied over the primary diaphragmatic motor cortex on respiratory parameters and disease progression in individuals with ALS. Methods: This is a multicenter, randomized, controlled clinical trial. Eligible participants (aged 18–80, both sexes, diagnosed with ALS) will be randomized into an active HD-tDCS group (gTDCS) or a sham group (gSham). The intervention consists of 30 min daily HD-tDCS sessions (3 mA) applied for two weeks (5 days/week), using a 4 × 1 ring configuration targeting the diaphragmatic motor cortex. Sham stimulation includes an identical setup but only delivers ramp currents (30 s) with a minimal ongoing current (0.1 mA). Results: Pre-, intra-, and post-intervention evaluations will include measures of cortical excitability, cerebral and tissue perfusion, surface electromyography, respiratory and pulmonary function, fatigue, sleep quality, pain, motor performance, dyspnea, quality of life, and adverse effects. All procedures will be conducted at participants’ homes with appropriate safety monitoring. Conclusions: This study will investigate the effects of HD-tDCS on respiratory and motor function in ALS and explore the feasibility of a home-based neuromodulation intervention. The outcomes may provide insight into non-pharmacological strategies for respiratory management in ALS. Full article

Improving energy efficiency in buildings is critical for supporting sustainable growth in the construction sector. In this context, the implementation of passive solar solutions in the building envelope plays an important role. Trombe wall is a passive solar system that presents great potential for passive solar heating purposes. However, its performance can be enhanced when the Internet of Things is applied. This study employs a multi-domain smart system based on Matter-enabled IoT technology for maximizing Trombe wall functionality using appropriate 3D-printed ventilation grids. The system includes ESP32-C6 microcontrollers with temperature sensors and ventilation grids controlled by actuated servo motors. The system is automated with a Raspberry Pi 5 running Home Assistant OS with Matter Server. The integration of the Matter protocol provides end-to-end interoperability and secure communication, avoiding traditional systems based on MQTT. This work demonstrates the technical feasibility of implementing smart ventilation control for Trombe walls using a Matter-enabled infrastructure. The system proves to be capable of executing real-time vent management based on predefined temperature thresholds. This setup lays the foundation for scalable and interoperable thermal automation in passive solar systems, paving the way for future optimizations and addicional implementations, namely in order to improve indoor thermal comfort in smart and more efficient buildings. Full article

We propose a speed-adapted treadmill that can be incorporated into a rehabilitation trainer that applies neurodevelopmental treatment (NDT) for patients with stroke. NDT practice is effective for post-stroke patients, but its requirement for therapists’ participation can limit the patients’ rehabilitation during the golden period of recovery. Previous studies have proposed a trainer that can automatically reiterate therapists’ interventions. However, that trainer employed a constant-speed treadmill, which required the users to frequently adjust their walking speeds during rehabilitation. This paper develops a speed-adapted treadmill that can regulate the treadmill motor to maintain the subject’s position during the training process. First, we derive models of the treadmill and cable motors through experiments. Then, we design robust controls for the two systems and simplify them as proportional-integral-derivative controllers for hardware implementation. Finally, we integrate the system and invite healthy and stroke subjects to participate in clinical experiments. Among ten stroke subjects, all subjects’ walking speeds and nine subjects’ stride lengths were improved, while eight subjects showed improvement in the swing-phase asymmetry and pelvic rotation after receiving the NDT rehabilitation employing the speed-adapted treadmill. Our findings indicate that the NDT trainer effectively enhances users’ gait characteristics, including swing-phase symmetry, pelvic rotation, walking speed, and stride length. Full article

A fully automated, buoy-based deployment sensor system is being developed to acquire high-quality water column data, and requires a controller to accurately position an array of sensors at various depths. The sensor system will be potentially deployed under rough ocean conditions. Depth is measured by a pressure sensor and adjusted through a rotating drum powered by a stepper motor. The proposed controller uses a model predictive control algorithm, a type of optimal control that predicts system response to optimize control actions used to track a desired variable-depth, setpoint profile. The profile is calculated to ensure smooth motion of the system, preventing motor malfunction. A simplified system model was created and used to simulate an open-loop test and system response. Constraints were applied to the control actions to match the practical limitations of the stepper motor. The simulated results show successful tracking of both a shallow and deep profile. At this stage of testing, the effects of ocean currents are considered by using a simple disturbance that provides the effect of ocean currents. A practical prototype that can implement the model predictive controller was tested on the physical buoy-based system with good control performance. Full article

In this study, a sensorless speed control method is proposed to enhance the speed control performance under load variations by utilizing a discrete-time flux observer in a V/f control environment. Due to their simple structure, low cost, and high reliability, induction motors are widely used in various fields, such as fans, pumps, and home appliances. Among the control methods for induction motors, V/f control operates as an open-loop system, without using speed sensors. It is mainly applied in industrial environments where fast dynamic performance is not required, due to its simple implementation and low cost. However, in cases of load variations or low-speed operation, it suffers from performance degradation and control limitations due to flux variations. To overcome these issues, this paper proposes a method that uses a discrete-time flux observer to estimate the stator flux. We calculate the rotor speed based on the estimated flux, and then improve V/f control performance by adding a compensation signal to the reference frequency, which signal is generated through a PI controller based on the difference between the estimated rotor speed and the reference speed. The proposed method is validated through MATLAB/Simulink-based simulations and experiments using a 5.5 kW induction motor M−G set, confirming that compared to conventional V/f control, the speed maintenance capability and overall robustness against load variations are enhanced. This study presents a practical solution to effectively improve the performance of existing V/f control systems without adding external sensors. Full article

This paper proposes an iterative auto-tuning algorithm for PI controllers in permanent magnet synchronous motor (PMSM) drive systems. The controller gains are initially set using motor-parameter-based formulas derived from pole–zero cancelation, providing a theoretical first-order approximation. To address discrepancies caused by practical non-idealities such as delays, nonlinearities, and unmodeled dynamics, the proposed method iteratively refines the gains based on real-time measurements of time-domain performance indices. In each iteration, rise time, peak time, and percent overshoot are evaluated against predefined target values, and gain compensation terms are calculated accordingly. These compensations are applied to update the controller gains until all performance indices fall within the desired range, at which point the tuning process terminates automatically. The effectiveness of the proposed algorithm is validated through both MATLAB/Simulink simulations and real-time hardware experiments, demonstrating significant improvements in transient response, overshoot suppression, and closed-loop stability compared to conventional tuning approaches. Full article

Control valves are widely applied in nuclear power, offshore oil/gas extraction, and chemical engineering, but suffer from issues like pressure oscillation, flow control accuracy degradation, and motor overload due to unstable fluid loads (e.g., nuclear reactions in power plants and complex marine climates). This paper proposes a dual-motor redundant compensation method to address these challenges. The core lies in a control strategy where a single main motor drives the valve under normal conditions, while a redundant motor intervenes when load torque exceeds a preset threshold—calculated via the valve core’s fluid load model. By introducing excess load torque as positive feedback to the current loop, the method coordinates torque output between the two motors. AMESim and Matlab/Simulink joint simulations compare single-motor non-compensation, single-motor compensation, and dual-motor schemes. Results show that under inlet pressure step changes, the dual-motor compensation scheme shortens the stabilization time of the valve’s controlled variable by 40%, reduces overshoot by 65%, and decreases motor torque fluctuation by 50%. This redundant design enhances fault tolerance, providing a novel approach for reliability enhancement of deep-sea oil/gas control valves. Full article