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This paper investigates the emerging field of grid-connected wind-powered pumping kite generator system (PKGS), focusing on the challenges associated with the generator/motor speed control. Conventional use of proportional–integral (PI) controllers faces difficulties in meeting requirements for dynamic response, tracking performance, stability, and disturbance rejection encountered in this technology, notably the periodical variation in the rotational speed reference in maximum power point tracking in generation phases and the dynamic response for the step reference in transient ones. To overcome these limitations, a model-free controller (MFC) approach is introduced, also known as intelligent PID controllers. Unlike traditional methods, MFC does not rely on a control model of the system and adapts to uncertainties and disturbances through online estimation based on the system's input–output behavior. To further improve the control performances, a tracking differentiator based on a nonlinear hyperbolic tangent function is integrated in the MFC. The effectiveness of the proposed strategy is proved through simulations in MATLAB/Simulink. The results highlight the superior performances of the proposed MFC approach in terms of speed control accuracy, response time, and robustness. [ABSTRACT FROM AUTHOR]

Traditional vector control strategies cannot eliminate the disturbances existing in permanent magnet synchronous motor (PMSM) systems while maintaining their dynamic performances. To address this issue, this paper proposes an adaptive continuous twisting (ACT) control method to further improve the control accuracy and anti-interference ability of the PMSM system. Firstly, a continuous twisting controller is designed to generate a continuous signal, which is utilised in the speed loop. It can guarantee that the tracking error of the speed regulation system finite-time converges to zero. This not only enhances the robustness of PMSM system but also effectively weakens the control chattering. Secondly, on this basis, an ACT controller is constructed to handle the disturbances with unknown bounds in PMSM systems. Finally, simulation and experimental results show that the proposed control method can improve the performances of PMSM system. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Switched Reluctance Motor (SRM) has a very high potential for adjustable speed drive operation due to their cost-effectiveness, high efficiency, robustness, simplicity, etc. Now a days SRMs are widely used in automotive industries as traction motors in electric vehicles and hybrid electric vehicles, air-conditioning compressors, and for other auxiliary services. In this article, a novel super twisting sliding mode controller (STSMC) is proposed to improve the performance of an SRM for reducing the ripple in speed and torque. Initially, a novel Modified Electric Eel Foraging Optimization (MEEFO) technique is developed by incorporating a quasi-oppositional phase and its performance is compared with the conventional Electric Eel Foraging Optimization (EEFO) technique with four popular benchmark functions. Then, both MEEFO and EEFO techniques are implemented to optimally design PI, SMC and STSMC controllers to effectively control the speed of an SRM. The study is carried in three different scenarios such as during starting, during a torque change and during a speed change. Finally, performance of the SRM in real time is studied with OPAL-RT 4510 simulator. It is observed that MEEFO based STSMC exhibits significant improvements in effectively controlling speed of the SRM, as compared to its other proposed counterparts. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness. To address this, the mechanism behind these non-ideal characteristics is analyzed based on the principles of space vector pulse width modulation (SVPWM) and its circuit structure. Tests are then conducted to examine the actual driver characteristics and verify the analysis. Building on this, a real-time compensation method is proposed, physically matched to the driver. Using the volt–second equivalence principle, an input–output voltage model of the driver is derived, with model parameters estimated from test data. The driving error is then compensated with a voltage method based on the model. The results of simulations and experiments show that the proposed method effectively mitigates the influence of the driver's non-ideal characteristics, improving the driving and speed control accuracies by 88.07% (reducing the voltage error from 0.7345 V to 0.0879 V for a drastic command voltage with a sinusoidal amplitude of 10 V and a frequency of 50 Hz) and 53.08% (reducing the speed error from 0.0130°/s to 0.0061°/s for a lower command speed with a sinusoidal amplitude of 20° and a frequency of 0.1 Hz), respectively, in terms of the root mean square errors. This method is cost-effective, practical, and significantly enhances the control performance of PMSMs. [ABSTRACT FROM AUTHOR]

In industrial automation systems, motors are crucial in driving mechanisms for speed or positioning control tasks. The widespread adoption of AC motors has been driven by their advantages such as low cost, solid structure, and easy maintenance. AC motors have gradually supplanted traditional DC motors with brushes and commutators and have become the predominant choice for motor and drive applications in industrial settings. In this paper, we introduce a hybrid control scheme that combines a sliding mode controller (SMC) and time delay estimation to enhance the robustness of speed control for permanent magnet synchronous motors (PMSM). Following the field-oriented principle, a flux SMC is initially designed to meet stator flux control requirements promptly. Subsequently, a speed controller is introduced, employing SMC with time delay estimation to address challenges such as torque and flux ripples, ultimately enhancing the overall robustness of the control system. Simulation results validate the effectiveness of the proposed control scheme under conditions of load disturbance and parameter uncertainties. Future work will involve implementing the proposed approach on digital signal processors to validate its performance and practicality in real-world applications. [ABSTRACT FROM AUTHOR]

The seabed surface is an important boundary for ocean exploration and foundation for ocean engineering construction. Accurate acquisition of seabed sediment mechanical properties and environmental parameters is critical to the development of marine resources and marine engineering. In this study, by designing the Seabed Cone Penetration Test (CPT) and Sampling System, multiparameter in situ testing and low-disturbance sampling of 3,000-m deep-sea seabed sediments are performed. Accounting for the stable penetration speed of the probe rod is the basis for ensuring the accuracy of the static penetration test results. The system adopts electrohydraulic proportional position control and a fuzzy proportional integral derivative (PID) controller to precisely control the position of the piston of the hydraulic circuit, which can improve the accuracy of the cone test data and reduce the interference of the sampling tube with the original sediment during the sampling process. Moreover, electrohydraulic co-simulation of the hydraulic control system was conducted with the AMESim and Simulink software, and the position control and speed control effects of the system were verified. The entire system was tested on site in the Shenhu Sea area of the South China Sea. This test successfully obtained nine in situ parameters, including physical and chemical parameters, for sediments within a depth range of 2.66 m on the seabed surface at a depth of 1,820 m. This system accurately and efficiently reflects the property characteristics of seafloor sediments in an in situ environment and can be widely used in marine engineering geological investigations. [ABSTRACT FROM AUTHOR]

Switched Reluctance Motor (SRM) drives have gained a lot of attention in the drive domain because of their built-in benefits, which include ease of use, longevity, and compatibility with high-speed applications. Precise speed control is essential to minimize torque variations and maximize SRM performance. Traditionally, the proportional–integral controller is used for speed control. The nonlinearity and uncertain complexities of SRM have shifted the focus to sophisticated control techniques from conventional. In this work, the Model Predictive Controller (MPC) is applied for SRM speed control, and its performance is compared with that of conventional control methods. MPC improves speed control precision and disturbance rejection by taking into account input restrictions and system dynamics through optimization across a constrained prediction horizon, a strong fault tolerance capability. Furthermore, three different models are developed to control the speed of the SRM using a PI controller, a lookup table-based controller, and an adaptive neuro-fuzzy inference system controller to compare the results of MPC. Through extensive simulations, the study seeks to show how successful MPC is in terms of speed tracking accuracy, transient responsiveness, and stability when compared to standard control techniques, especially under variable operating circumstances and load disturbances. The practicality of the suggested control approach is illustrated by comparing it with other conventional control methods, and its performances are evaluated by using real-time simulator OPAL-RT 4510 under various situations. [ABSTRACT FROM AUTHOR]

Although full automation has not yet been achieved, automated vehicles are a valid research area. Not only would automated vehicles provide ultimate driver convenience, but they would maximize energy efficiency by eliminating undesired human driving behaviors and optimally controlling the powertrain. From the perspective of control related to energy saving, speed profile optimization is important for improving system efficiency and satisfying passenger demands. This study employs Dynamic Programming (DP) to solve the constrained optimal problem for travel time, distance, and speed limit by exploring all possible control options. The solutions obtained by DP demonstrate consistent control patterns combining four control modes—acceleration, cruising, coasting, and braking, with cruising or coasting being selective depending on the boundary conditions. This study introduces DP-based simulation results and attempts to provide comprehensive interpretations of the optimal policy by analyzing the essential factors that affect the control problem, including boundary conditions, road load, and powertrain characteristics. Based on these interpretations, the control concepts can be explained as the optimal policy selecting the best control option based on system efficiency and boundary conditions. The results of DP are compared with a human-like driver model to show that the optimal speed profiles can effectively reduce energy consumption. [ABSTRACT FROM AUTHOR]

This research proposes two approaches for determining the required frequency and modulation index for a pulse-width-modulation (PWM) system in a variable frequency drive (VFD) to control the speed of the threephase induction motor. The first approach which is the reverse direction algorithm (RDA), uses a set of equations to calculate the necessary frequency and voltage for maintaining a constant motor speed under varying load conditions. The second one involves training a neural network (NN) on data collected by the RDA, which can then be used to continuously adjust the motor speed in real time to adapt to changing load torque requirements. Simulation and laboratory models for the three-phase induction motor are built and the proposed RDA-NN controller is examined. Results have proved that the proposed controller is effective in providing a stable and responsive motor speed control system. [ABSTRACT FROM AUTHOR]

This article proposes a novel Lie group controller for speed sensorless control of a surface-mounted Permanent Magnet Synchronous Motor (SPMSM) fed by a solar microinverter. Solar-fed motor drive poses significant challenges due to the inherent solar power fluctuations and small storage capability of solar microinverters. Thus, the reliable operation of a solar-powered motor drive requires a control methodology that delivers optimal performance for the fluctuating solar power along with fast dynamic behavior to handle source/load transients. The proposed control strategy quickly modulates the drive frequency based on Lie theory in response to any transients, maximizing the motor's torque output. The proposed control method has better dynamic performance, ease of start-up, and is simpler to implement compared to existing observer-based vector control methods. Theoretical analysis as well as simulation and experimental results prove the effectiveness of the proposed control method.

For a class of permanent magnet synchronous motors (PMSM) characterized by strong coupling, significant nonlinearity, load uncertainties, and external disturbances, this paper investigates an extended state observer-based direct model-free adaptive predictive compensation control (ESO-dMFAPCC) method for speed regulation. Initially, a dynamic linearization model of a PMSM system with unknown external disturbances is developed. Subsequently, a novel direct model-free adaptive predictive compensation controller, theoretically equivalent to an ideal controller, is proposed. An extended state observer (ESO) is designed to estimate the state variable, capturing the characteristics of the external disturbance and system uncertainty. On this basis, control gains with online adaptive capabilities are designed to achieve adaptive speed control for the PMSM system. The primary advantage of the proposed control method lies in its novel framework for directly constructing a predictive compensation controller, which simplifies the controller structure design process by reducing it to a parameter-optimization problem. Moreover, utilizing only the PMSM system’s input/output (I/O) data, the proposed ESO-dMFAPCC is purely data-driven and exhibits strong robustness against external disturbances. Furthermore, the stability of the closed-loop PMSM system is rigorously analyzed. Finally, the simulation results demonstrate that, compared to PI control, sliding mode control (SMC), and model-free adaptive predictive control (MFAPC), the proposed method offers superior dynamic performance while significantly enhancing the anti-disturbance capabilities of the PMSM system.

The increasing use of Switched Reluctance Machines (SRMs) across various applications has been driven by their notable advantages and advancements in power electronics. However, controlling SRMs remains inherently complex due to their nonlinear characteristics and parameter variations at different operating points. To address this challenge, this paper proposes the implementation of a Linear-Quadratic-Integral Regulator (LQI) for precise speed control of SRMs. The LQI controller, renowned for its optimal control capabilities, offers several benefits, such as favorable dynamic response, and robustness against disturbances and changing parameters. Unlike traditional controllers, the absence of an inner current control loop results in reduced torque ripple at the phase commutation frequency, simplifying design and implementation. By using PWM for voltage command, torque ripple at the switching frequency is reduced, and noise due to magnetostriction is minimized. A comprehensive design procedure for the proposed LQI speed control scheme is outlined, followed by a comparative analysis with established SRM controllers based on Proportional-Integral (PI) and Hysteresis (HC) controllers. Experimental validation of the proposed controller is provided, illustrating its superior performance across various operating points. The results demonstrate improved dynamic response, enhanced disturbance robustness, reduced torque ripple, a superior switching frequency spectrum, and minimal computational burden for embedded implementation.

The hybrid AC-DC microgrid is a system that links AC and DC microgrids using a bidirectional AC-DC interface converter. The presence of dynamic loads is a major obstacle to the implementation of the microgrid concept, and those 50 % include asynchronous motors. This paper presents a control system for a hybrid microgrid that powers an electrical motor using the field-oriented control (FOC) method. This study investigates the significance of third-harmonic injected PWM (THIPWM) in reducing the necessary voltage across the DC bus while still achieving the required AC voltage at motor terminals. MATLAB/Simulink simulation tool and OPAL-RT for real-time simulations were used for validation of the proposed control. The outcomes show the tracking of the reference currents, achieving accurate torque control, managing various operating modes like grid-connected having SPWM and THIPWM, or operation with PV and battery sources. The research also highlights lower settling time values as well as lower total harmonic distortion (THD) compared to SPWM in different load conditions using THIPWM.

Abstract At present, the speed of the reel of soybean combine harvester is mainly adjusted manually, and basically does not have the ability to automatically adjust the speed of the reel according to changes in crops or working speed. In the maize-soybean strip intercropping mode, soybean plants are densely planted. During harvesting, the feeding rate increases, making it even more necessary to adjust the reel speed according to the growth status of the crops. This article uses PLC as the main control platform to design a reel speed control system for the existing combine harvester. Tests have shown that the control system can meet the requirements for reel speed control. The relative error of the reel control system’s speed test is 2.6%, the relative error of the operating speed is 6.77%, and the maximum relative error of the automatic control of the reel speed is 5.38%. Compared with traditional reel, it reduces header loss by 2.12%. The soybean combine harvester reel speed control system designed in this article can realize automatic adjustment of the reel speed, reduce feeding losses caused by changes in crop height, changes in feed volume, and untimely adjustment of the reel, and can reduce header loss, increased crop yield.

Electric vehicles (EVs) as a sustainable safety system are being increasingly used and receiving attention from researchers for several reasons including optimal performance, affordability for consumers, and environmental safety. EV speed control is a crucial issue that requires reliable and intelligent controllers for maintaining this matter. The primary goal of this research is to design the linear and nonlinear Proportional, Integral, and Derivative (PID) controllers to control EV speed based on the minimum value of ITAE plus ISU (integral square of control signal) as well as satisfy the constrain on response overshoot. All the proposed PID controllers, conventional PID controller, arc tan PID controller, and nonlinear PID controller (NL-PID) are used in cascade with EV model. In all these PID controllers, a filter is used with the derivative term to avoid the effect of the noise. The tuning of the proposed controller gains is achieved using Aquila Optimization algorithm. The controllers’ parameter tuning is primarily determined by reducing the Integral Time Absolute Error (ITAE) and integral square control signal. Numerical simulation, system modelling, and controller design are done using MATLAB. By comparing the results, the proposed controllers’ efficacy is demonstrated. The proposed NL-PID controller provides promising EV speed regulation control and robustness to external disturbances. Where, the performance specifications of the proposed NL-PID controller when using unit step input with step disturbance of 0.2 and −0.2, and the model parameters increased by 25% from its nominal value are the rise time is 2.608, settling time is 2.608, overshoot is 0.073%, the maximum control signal is 6.230, number of slope sign change in the control signal is 231, and ITAE is 17.6064. The comparative results show the NL-PID controller’s superior performance for tracking the reference signal with the lowest peak for the control signal, rejection of disturbances, ability to overcome model uncertainties, and lowest ITAE value.

The hybrid AC-DC microgrid is a system that links AC and DC microgrids using a bidirectional AC-DC interface converter. The presence of dynamic loads is a major obstacle to the implementation of the microgrid concept, and those 50 % include asynchronous motors. This paper presents a control system for a hybrid microgrid that powers an electrical motor using the field-oriented control (FOC) method. This study investigates the significance of third-harmonic injected PWM (THIPWM) in reducing the necessary voltage across the DC bus while still achieving the required AC voltage at motor terminals. MATLAB/Simulink simulation tool and OPAL-RT for real-time simulations were used for validation of the proposed control. The outcomes show the tracking of the reference currents, achieving accurate torque control, managing various operating modes like grid-connected having SPWM and THIPWM, or operation with PV and battery sources. The research also highlights lower settling time values as well as lower total harmonic distortion (THD) compared to SPWM in different load conditions using THIPWM. [ABSTRACT FROM AUTHOR]

Autonomous driving technology, as a key component of intelligent transportation systems, has gained considerable attention in recent years. While significant progress has been made in areas such as path planning, obstacle detection, and navigation, relatively less focus has been placed on vehicle speed control, which plays a critical role in ensuring safe and efficient operation, especially in complex and dynamic road environments. This paper addresses the challenge of speed tracking by proposing a genetic algorithm-based optimization method for PID controller parameters. Traditional PID controllers often struggle with maintaining accuracy and response time in highly variable conditions, but by optimizing these parameters through the genetic algorithm, substantial improvements in speed control precision and adaptability can be achieved, enhancing the vehicle's ability to navigate real-world driving scenarios with greater stability. The experimental results clearly indicate that the autonomous vehicle, after PID parameter optimization using a genetic algorithm, demonstrated the following speed tracking errors: when the road surface adhesion coefficient was 0.5, the maximum speed tracking error was 0.22, the average value was 0.063, and the standard deviation was 0.124; when the adhesion coefficient was 0.6, the maximum speed tracking error was 0.180, the average value was 0.056, and the standard deviation was 0.099; when the adhesion coefficient was 0.8, the maximum speed tracking error was 0.179, the average value was 0.056, and the standard deviation was 0.098. This method significantly improved the controller's performance in maintaining the desired speed, even under challenging conditions. These findings highlight the potential of genetic algorithms in supporting the future development of autonomous driving technology, ensuring its successful integration into intelligent transportation systems. [ABSTRACT FROM AUTHOR]

This paper deals with the tuning of the parameters of a fractional-order PI controller for the speed control of an electric servo drive in which the torque is set by a torque generator. The controller parameters are tuned using the multiple dominant pole method (MDPM), while the fractional order integrator is approximated by the Oustaloup method. The input parameters required for tuning the controller using MDPM are calculated using the optimization algorithm presented in this paper. This algorithm selects the optimal parameters from a set of points in three-dimensional space, based on the symmetry around a central point. The controller tuning is performed for the normalized control loop model. The obtained optimized normalized fractional order PI controller can then be applied to a real servo drive with specific parameters. The proposed tuning was also verified experimentally, comparing the obtained closed-loop responses with those of the integer-order PI controller. Both simulation and experimental results showed a significant reduction in the integral of the absolute error at the disturbance step compared to a control loop using an integer-order PI controller. This results in a faster output response to load torque steps and a smaller control error in a real servo drive. [ABSTRACT FROM AUTHOR]

To address the impact of load disturbances on the full-speed-range control of an interior permanent magnet synchronous motor (PMSM), an active disturbance rejection control (ADRC) method is proposed. The speed loop employs phased field-weakening control (FW) based on ADRC, while the current loop utilizes proportional-integral-derivative (PID) control. Starting from the motor parameters, the Lagrange multiplier method was used to derive the critical speeds for the maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) ratios, and the timing for the field-weakening control was analyzed. A full-speed-range control model of the motor was established, and an ADRC-based speed loop controller was designed to achieve smooth transitions between high speeds and anti-disturbance solid capabilities. Based on the proposed control strategy, a 21 kW PMSM was used as the research object, and a full-speed-range control simulation model was developed in MATLAB/SIMULINK to verify the strategy. Compared to the traditional PID control, the simulation results demonstrate that the proposed strategy effectively observes and compensates for load disturbances, significantly reducing initial torque oscillations under three different operating conditions. After a sudden load increase, torque oscillations were reduced by 16%, with the stator current reaching steady state 0.03 s faster, response speed improving by 0.02 s, smooth transitions between speed ranges, and enhanced anti-disturbance performance. [ABSTRACT FROM AUTHOR]

In the dental prosthetics field, BLDC micro-motors are primarily used for implant procedures. As dental materials become increasingly harder, there is a growing demand for higher performance motors to precisely process these materials. However, difficulties in motor development arise due to price competitiveness. Dental motors require capabilities such as low-speed, high-torque for drilling operations and high-speed rotation for precise machining of high-strength dental materials. Additionally, there is a requirement to address thermal issues to prevent user burns due to motor-generated heat. Typically, motors requiring precision control utilize high-resolution position sensors like encoders or resolvers to acquire and control the rotor's position. However, the high cost of these sensors and constraints such as increased device size pose challenges in maintaining price competitiveness. In this paper, we propose a control algorithm that satisfies the requirements for low-speed, high-torque and high-speed operation requirements for precision machining without hardware modifications using a BLDC micro-motor equipped with an inexpensive Hall sensor commonly used in the dental field. Furthermore, the algorithm is designed to ensure stable operation even in the event of Hall sensor failure or malfunction, and its effectiveness is validated through experiments. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Anhui University of Technology (Natural Science) is the property of Anhui University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, the speed of the reel of soybean combine harvester is mainly adjusted manually, and basically does not have the ability to automatically adjust the speed of the reel according to changes in crops or working speed. In the maize-soybean strip intercropping mode, soybean plants are densely planted. During harvesting, the feeding rate increases, making it even more necessary to adjust the reel speed according to the growth status of the crops. This article uses PLC as the main control platform to design a reel speed control system for the existing combine harvester. Tests have shown that the control system can meet the requirements for reel speed control. The relative error of the reel control system's speed test is 2.6%, the relative error of the operating speed is 6.77%, and the maximum relative error of the automatic control of the reel speed is 5.38%. Compared with traditional reel, it reduces header loss by 2.12%. The soybean combine harvester reel speed control system designed in this article can realize automatic adjustment of the reel speed, reduce feeding losses caused by changes in crop height, changes in feed volume, and untimely adjustment of the reel, and can reduce header loss, increased crop yield. [ABSTRACT FROM AUTHOR]

This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time. [ABSTRACT FROM AUTHOR]

In this review we focus on the role of in-car sound, specifically the artificial engine sounds, on drivers' speed perception and control, a topic that has received little attention so far. Previous studies indicate that removing or reducing engine sound leads drivers to underestimate speed and, consequently, to drive faster. Furthermore, evidence suggests that specific sound frequencies could play a role in this process, highlighting the importance of in-car sound features. First, we show that the amount of research in the field is scarce and rather outdated, and that this is largely due to the fact that industrial research is subject to very few publications. Then, we examine benefits and limitations of different research paradigms used and we propose a protocol to investigate systematically the phenomenon. In particular, we argue for the benefits of a wider use of psychophysical methods in speed perception, a field that has been typically explored by means of driving simulation. Finally, we highlight some methodological and statistical limitations that might impact the interpretation of the evidence considered. Our methodological considerations could be particularly useful for researchers aiming to investigate the impact of sound on speed perception and control, as well as for those involved in the design of in-car sounds. These are particularly relevant for the design of electric vehicles, which represent a challenge but also the ideal testing ground to advance the knowledge in the field. [ABSTRACT FROM AUTHOR]

In industry, three-phase induction motors (IMs) are the most used motors. IMs' high starting supply current and large accelerating torque make their starting process a potential source of unnecessary energy waste, especially when it is repeated. To overcome the starting problems of an IM, an intelligent soft-starter controller based on an artificial neural network (ANN) is proposed. The controller contains two soft starting control schemes namely, current and speed controls. That allows one to select the soft starting control scheme based on the load requirements and/or supply utility capability. Current and speed controls are to keep the motor's starting current and accelerating torque constant at a specified level, respectively. That is achieved by selecting the appropriate firing angles for the thyristors in the soft starter. The online determination of the thyristor firing angles is resolved through the application of the ANN approach. First, the ANN models for two control techniques are developed using a MATLAB Neural Network Toolbox for training. Based on the current-speed and torque-speed characteristics, two ANN models are trained for a range of thyristor firing angles. Secondly, the ANN models of two control schemes are implemented using MatLab/Simulink for a 3kW three-phase IM. A variety of simulation tests prove that a validation on control technique's effectiveness under various load conditions (noload, pump, constant, and linear loads) and reference values exists. The least inrush current and smooth acceleration are obtained with two control schemes. [ABSTRACT FROM AUTHOR]

The generation of sustainable energy through wind and hydrokinetic turbines, which convert the kinetic energy from fluid flows into mechanical energy, presents an attractive solution for diversifying the country energy matrix in response to climate change. Consequently, numerous studies have investigated the aerodynamic and hydrodynamic behaviors of various wind and hydrokinetic turbines using numerical simulations to understand their interaction with the surrounding fluid flows and enhance their performance. However, to validate these studies and aiming at improving the turbine design, experimental studies on a laboratory scale employing wind tunnels and hydraulic channels are essential. This work addresses the development and implementation of a reliable control system for experimentally evaluating the power coefficient (Cp) versus the tip speed ratio (TSR) curve of wind and hydrokinetic turbines. The control system, based on a DC motor acting as a generator and aligned with a commercial torque sensor, enables a precise control over the experimental setup. By obtaining and comparing the experimental performance curves of Cp versus TSR for both wind and hydrokinetic turbines with numerical results, the effectiveness and accuracy of the developed control system are demonstrated. A satisfactory fit between numerical and experimental results was achieved, underscoring the utility and reliability of the control system for assessing the turbine performance. [ABSTRACT FROM AUTHOR]

This paper employs artificial neural networks (ANNs) in estimating and controlling the speed of a separately excited DC motor, which is also one of the most essential modern techniques used in control applications and to enhance the effectiveness of separately excited DC motor speed control. For speed control of a separately excited DC motor, the paper compares the performance of the conventional proportional integral (PI) and artificial neural networks (ANNs). A neural network (NNs) based controller is an extremely appreciated technology for accomplishing high-recital speed control. The simulation results are expose to demonstrate the efficacy and advantage of the control system of a separately excited DC motor (SEDCM) using ANNs over the traditional control arrangement. The integration of (ANNs) has shown significant improvements in performance in speed control of DC motors separately excited, chiefly in dynamic response and robustness in contradiction of load variations. This technique leverages the adaptive competences of ANNs to improve traditional control methods. In this manuscript, two controller for speed control of SEDCM are designed and simulated using matlab Simulink program. The first one is the PI controller and the second is the ANN controller, the results of the ANN controller based speed control of the SEDCM give quick response, low overshot, small settling time, zero steady state error when sudden change happened in reference speed or load torque. In addition, as compared with the results of PI controller which give slow response, high overshot, large settling time under the same conditions. [ABSTRACT FROM AUTHOR]

Flying cars, or vertical takeoff and landing (VTOL) aircraft, are revolutionary devices that can address traffic congestion and help build a three-dimensional transportation system in the future. Flight safety could deteriorate in actual flight because ducted fans may suffer from significant thrust fluctuations induced by crosswinds. In this article, we propose a variable-speed control method to reduce thrust fluctuations by applying a speed waveform opposite to the fluctuation waveform. We choose a typical condition of a 5 m/s crosswind for validation. The results of unsteady Reynolds averaged Navier‒Stokes (URANS) calculations show that crosswinds lead to fluctuations in all thrusts with the frequency of the blade passing frequency. The total thrust fluctuation amplitude accounts for 6.2% of the total thrust. By applying the variable-speed control where the frequency is identical to the thrust fluctuation frequency, the phase difference is 180 degrees, and the amplitude is approximately the square root of the fluctuation amplitude, the standard deviation of the thrust fluctuation is reduced by 92% without affecting the mean total thrust. Fluctuations in other performance parameters, such as lateral force and pitching moment, are improved as well. This active control method can achieve accurate control without any auxiliary equipment and has good application prospects. It provides a promising idea for solving the problem of performance fluctuations in turbomachinery. [ABSTRACT FROM AUTHOR]

This paper presents an innovative Hybrid Electric Vehicle (HEV) configuration utilizing a fuel cell as the primary energy source and an onboard Photovoltaic (PV) array as a supplementary source. The system features an advanced Model Predictive Control (MPC) enhanced by a Recurrent Neural Network (RNN) to manage the induction motor efficiently. Key components include a PV array, a fuel cell, and an electrolyzer. The PV array supplements the fuel cell during optimal sunlight conditions, while excess energy during idle periods is converted to hydrogen via the electrolyzer and stored in a hydrogen tank for future use. A quadratic bidirectional buck-boost converter (QBBC) regulates voltage, ensuring compatibility between energy sources and the motor. The system's performance is evaluated under various sunlight and speed conditions, with the RNN-based MPC compared to an Artificial Neural Network-based MPC (ANN-MPC) and a traditional Proportional-Integral (PI) controller. An incremental conductance algorithm is implemented for Maximum Power Point Tracking (MPPT) to optimize PV power extraction. The RNN model predicts motor speed, enhancing control precision. Simulations in MATLAB/SIMULINK reveal that the RNN-based MPC outperforms ANN-MPC and PI controllers, demonstrating improved efficiency and speed control. This work contributes to advancing intelligent and energy-efficient HEV technologies. [ABSTRACT FROM AUTHOR]

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This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors. [ABSTRACT FROM AUTHOR]

In this paper, a voltage control system for a PMSM motor based on the QZSDMC converter is proposed, which allows operation in both buck and boost modes as a possible method to make the drive resistant to power grid voltage sags. The authors presented a method for measuring and transforming the output voltage from QZS, enabling the use of a PI controller to control the voltage supplied to the DMC converter. The publication includes simulation and experimental studies comparing the operation of a PMSM motor powered by DMC and the proposed QZSDMC with voltage control. Simulation studies confirm the drive with QZSDMC resistance to voltage sags up to 80% of the rated value. Experimental studies demonstrate the correct operation of PMSM even with a power grid voltage amplitude equal to 40% of the rated value. [ABSTRACT FROM AUTHOR]

Flanders Hydraulics and the Maritime Technology Division of Ghent University have proposed a hybrid testing platform for the development and validation of motion control algorithms for autonomous navigation in shallow and confined water, which consists of towing tank facilities and simulators. Within this framework, the towing tank at Flanders Maritime Laboratory, Ostend, Belgium, featured in large size, tuneable water depth, and a state-of-the-art free-running system, a series of autonomous manoeuvering tests with increasing complexity are designed and carried out. These manoeuvres, including acceleration with heading keeping, path following, speed control, and their combinations, are enabled by implementing guidance and control algorithms on a physical ship model, namely the benchmark ship KVLCC2 at a scale of 1/75. The controllable environment and large space of the tank basin facilitate the upgrade of the existing algorithms and the development of new algorithms. The definition of the autonomous manoeuvres, experimental setup, and parameter settings are elaborated and the effect of implementing different guidance strategies is discussed. Recorded results demonstrate good agreement between the measurements and the expectations, and control accuracy is quantified with evaluation indices. In summary, the feasibility and flexibility of the testing platform and the effectiveness of the algorithms are proved through extensive experiments. [ABSTRACT FROM AUTHOR]