A continuous scanning laser Doppler vibrometry (CSLDV) obtained sweeping a single laser beam along a periodic scan pattern allows measuring surface vibrations at many points simultaneously by demultiplexing the CSLDV signal. This known method fundamentally differs from conventional scanning laser vibrometry techniques in which the laser beam is kept at a fixed point during each measurement and then moved to a new position prior to the next measurement. This article demonstrates the use of a CSLDV for measuring in a non-contact fashion the velocity of low-frequency surface waves (f < 100 Hz) propagating over soft materials, namely here gel surfaces—mimicking human body soft tissues—and skeletal muscles, to develop an affordable and noninvasive elastography modality. The CSLDV vibration measurements obtained with a single laser beam, linearly scanned over the test surface at 200 Hz over lengths up to 6 cm, were validated using an array of three fixed laser Doppler vibrometers distributed along the same scan line. Furthermore, this CSLDV setup was used to measure the increase in surface wave velocity over the biceps brachii muscle which was directly correlated to the actual stiffening of the biceps occurring while a subject was performing voluntary contractions at an increasing level.

This paper presents a novel methodology for minimizing power losses in brushless DC (BLDC) motors through the implementation of adaptive flux control techniques. Conventional motor control strategies, such as direct torque control (DTC), typically employ fixed flux values, which often result in suboptimal performance, particularly under dynamic load and speed variations. To mitigate this inherent limitation, two adaptive flux control methods are introduced: incremental conductance (IncCond) and fuzzy logic. These proposed strategies facilitate real-time dynamic adjustment of the stator flux, thereby optimizing motor performance and significantly enhancing system efficiency. Experimental validation confirms the efficacy of these adaptive techniques, demonstrating substantial improvements in power loss reduction and overall efficiency when compared to traditional fixed flux control strategies. Notably, the fuzzy logic control strategy achieves the highest efficiency, registering a system efficiency of $66.59$%, which surpasses both the incremental conductance method and conventional fixed flux control. These findings underscore the considerable potential of adaptive flux control in applications where energy efficiency is paramount, including electric vehicles and renewable energy-driven systems.

The induction motor (IM) drives are prone to various uncertainties, disturbances, and non-linear dynamics. A high-performance control system is essential in the outer loop to guarantee the accurate convergence of speed and torque to the required value. Super-twisting sliding mode control (ST-SMC) and fractional-order calculus have been widely used to enhance the sliding mode control (SMC) performance for IM drives. This paper combines the ST-SMC and fractional-order calculus attributes to propose a novel super-twisting fractional-order sliding mode control (ST-FOSMC) for the outer loop speed control of the model predictive torque control (MPTC)-based IM drive system. The MPTC of the IM drive requires some additional sensors for speed control. This paper also presents a novel machine learning-based Gaussian Process Regression (GPR) framework to estimate the speed of IM. The GPR model is trained using the voltage and current dataset obtained from the simulation of a three-phase MPTC based IM drive system. The performance of the GPR-based ST-FOSMC MPTC drive system is evaluated using various test cases, namely (a) electric fault incorporation, (b) parameter perturbation, and (c) load torque variations in Matlab/Simulink environment. The stability of ST-FOSMC is validated using a fractional-order Lyapunov function. The proposed control and estimation strategy provides effective and improved performance with minimal error compared to the conventional proportional integral (PI) and SMC strategies.

Fault diagnosis and localization are critical for maintaining the integrity of motor drive systems and optimizing maintenance interventions. This paper introduces a novel methodology for the rapid diagnosis and localization of semiconductor open-switch faults in power converters that drive synchronous reluctance motor drives. By exploring innovative diagnostic techniques, the effectiveness of a signal-based approach for accurately identifying faults is emphasized. A new algorithm is proposed to detect open-switch faults using a power-based diagnostic approach. The method achieves both accurate fast diagnosis and fault localization. The performance of the technique is evaluated through simulations and experimental data, demonstrating its effectiveness in enhancing the reliability of motor drive systems.

Due to their advantages in ensuring low harmonic distortion and high power factors, single-phase Pulse-Width Modulated (PWM) rectifiers are widely employed in several industrial applications. Generally, the conventional control loop of a single-phase PWM rectifier uses both voltage and current sensors. Hence, in case of sensor fault, the performance and the availability of the converter can be seriously compromised. Therefore, diagnosis approaches and fault-tolerant control (FTC) strategies are mandatory to monitor these systems. Accordingly, this paper introduces a novel multiple-sensor FTC scheme for a single-phase PWM rectifier. The proposed fault diagnosis approach relies on joining several Generalized Proportional Integral (GPI) and Model Reference Adaptive System (MRAS) observers with a residual generation technique to detect and isolate sensor faults in a simple and reliable manner. While conventional sensor FTC methods dedicated to PWM rectifiers can only deal with single faults, the suggested approach guarantees a very good effectiveness level of sensor fault detection, isolation (FDI) and FTC of multiple-sensor fault occurrence scenarios. Consequently, the single-phase PWM rectifier can work with only the survivable single sensor with the guarantee of very good performance as in healthy operation mode. The effectiveness of the proposed sensor FDI approach and its control reconfiguration performance are demonstrated through both extensive simulation and experimental results.

This paper introduces a novel non-linear control strategy to enhance the voltage regulation performance of Medium Voltage DC (MVDC) converters operating under significant external disturbances and parameter uncertainties. The proposed controller synergistically improves the performance of MVDC microgrid by proposing a Barrier Adaptive Super Twisting Sliding Mode Control (BA-STSMC) employing a novel non-singular finite time proportional integral derivative (PID)-type terminal surface. The proposed work collectively provides a rapid transient response and finite-time error convergence, robust disturbance rejection and eliminate steady-state errors using continuous control signal that effectively mitigating chattering. This composite design inherently avoids the singularity issues common in conventional terminal SMC. The barrier adaptivity mechanism dynamically adjusts the control effort by constraining state trajectories within a predefined invariant domain, thereby guaranteeing that control signals remain within safe operational limits while enhancing the finite-time convergence properties. The stability and finite-time reachability of the closed-loop system are rigorously established via Lyapunov analysis. Comprehensive simulation and experimental validations demonstrate the proposed controller's superior ability to restore Distributed Generator (DG) current and voltage during severe events including abrupt load variations and communication failures outperforming conventional PI, standard SMC, and adaptive STSMC methods in terms of robustness, convergence speed, and control signal quality.