This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of the proportional-integral (PI) controller tuning is avoided. The response of the suggested technique is verified by simulation results and compared with that of the conventional FCS-MPC.

In this study, a model has been developed to analyze AlGaN/GaN high-electron-transistor (HEMT) and metal-oxide semiconductor high-electron-transistor (MOSHEMT) based biosensors. The model focuses on detecting biomolecules such as ChOx, protein, streptavidin and uricase by modulating the dielectric constant. The sensitivity parameters used for biomolecule detection include drain current, transconductance, and drain off sensitivity. The dielectric constant is adjusted based on the specific biomolecule being sensed by the biosensor. The variation in dielectric leads to changes in drain current, with an increase or decrease depending on the positive charge of the biomolecules. The HEMT device exhibits greater variations in drain current, transconductance, and drain off sensitivity compared to the MOSHEMT device when the biomolecule is present in the cavity region. The simulation results are validated by comparing them with Atlas-TCAD (atlas-technology computer aided design) and experimental data, showing excellent agreement.

Induction motor (IM) is considered one of the most important machines in industrial applications, which requires precise and effective control of its behavior in order to improve its performance. In this paper, three control strategies based on the development of inverse artificial neural networks (IANNs) were proposed in order to control the current (Ias), electromagnetic torque (Ce), and speed (Wr) of an asynchronous machine IM. These inverse artificial neural networks have been learned from conventional control system (PI controller and vector control) data using MATLAB software. Comparison between the responses of both the classical controller and the IANNs showed the ability and effectiveness of the latter in precisely controlling the three properties of the asynchronous motor, and it also achieved better dynamic motor behavior, speed without overtaking, and good load disturbance rejection, which proves the high performance of these developed IANNs.

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency, compact structure, and excellent dynamic performance. However, achieving accurate speed control with high robustness under load disturbances and parameter uncertainties remains a significant challenge. Conventional proportional–integral (PI) controllers often suffer from overshoot, slow dynamic response, and sensitivity to nonlinear operating conditions. To address these limitations, this paper proposes an intelligent control strategy that combines third-order sliding mode control (TOSMC) with the Golden Jackal Optimization (GJO) algorithm for optimal PMSM speed regulation. The proposed TOSMC-GJO approach aims to enhance the operational performance, robustness, and reliability of PMSM drives. The control structure consists of an optimized outer-loop speed controller and an inner-loop predictive current controller to improve current quality and eliminate the need for conventional PI tuning. The controller parameters are optimized using a fitness function designed to minimize tracking error, overshoot, settling time, torque ripples, and total harmonic distortion (THD). Simulation results under variable speed and load torque conditions demonstrate that the proposed TOSMC-GJO controller achieves superior performance compared with PI control and TOSMC optimized using Grey Wolf Optimization (GWO). The proposed strategy eliminates speed overshoot and reduces the response time to 0.0052 s, compared with 0.0056 s for TOSMC-GWO and 0.011 s for PI control. In addition, the THD of stator currents is reduced to 6.12%, improving current quality and reducing harmonic distortion. The proposed controller also provides smoother torque response, better disturbance rejection capability, and improved waveform symmetry. These results confirm that integrating high-order nonlinear control with metaheuristic optimization significantly improves the dynamic performance, operational reliability, and robustness of PMSM drive systems under demanding operating conditions.