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To address the impact of load variations, external environmental changes, and the tuning of the parameters on Permanent Magnet Synchronous Motors (PMSMs) used in ships, this study proposes an Active Disturbance Rejection Control (ADRC) strategy for PMSMs, optimized by the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, based on the PMSM model, the study addresses the limited disturbance rejection capability of the traditional fal function in the Extended State Observer (ESO) of conventional ADRC. To improve the accuracy of the state observer, the faln function is introduced as a replacement for the traditional fal function. Second, due to the numerous parameters in ADRC, which are difficult to tune, the QPSO algorithm—known for its strong global search capabilities and fast convergence speed—is utilized for parameter optimization. Additionally, the position update formula within the optimization algorithm is revised and optimized. Finally, simulation experiments are conducted using the Matlab/Simulink platform, where practical conditions, such as load fluctuations and random noise, are incorporated. The simulation results demonstrate that, compared to PSO-ADRC control, IPSO-ADRC control, and ICFO-ADRC control, the proposed method offers a superior dynamic response. Specifically, the speed control accuracy is improved by 46.7%, torque ripple is reduced by 50.8%, and harmonic distortion decreases by 23.1%. These results highlight the significant advantages of this method in enhancing system robustness, dynamic response speed, and steady-state accuracy, making it particularly suitable for PMSM control systems in complex dynamic environments, such as those encountered on ships. [ABSTRACT FROM AUTHOR]

The fuzzy controller is a popular choice for permanent magnet synchronous motor (PMSM) control systems because of its advantages, such as straightforward design, and no reliance on the precise mathematical model of the motor. But the existing pure PI-like fuzzy control strategy still has some disadvantages, such as poor adaptive ability and large overshooting. This work redevelops the structure and rules of the adaptive fuzzy controller, and proposes and proves an improved adaptive PI-like fuzzy control algorithm for the PMSM system. Firstly, a parallel dual fuzzy controller structure is constructed to facilitate the adaptive adjustment of the "PI-like fuzzy controller". Secondly, the error acceleration parameter rv(k), which contains the PMSM speed information, is set and normalized to accurately identify the dynamic response stages of the PMSM system. Lastly, an adaptive fuzzy rule table is designed based on the dynamic response waveform of the PMSM system, and the control characterization is analyzed. The simulation and experimental results of the PMSM system show that the improved adaptive PI-like fuzzy controller has a broad dynamic adjustment range, the PMSM can rapidly and smoothly reach the given speed during the startup stage with small overshooting, the speed drop is low when the load is abruptly added, the PMSM system can quickly return to the steady state with a strong adaptive ability, and its dynamic performance indicators surpass those of the PID controller and traditional PI-like fuzzy controller. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) offer advantages such as high torque, power density, and energy efficiency, making them essential in power electronics, aerospace, and daily life. Designing PMSM control systems is complex yet crucial for optimizing performance. Field Oriented Control (FOC) is widely adopted due to its benefits including low torque ripple, high DC bus voltage utilization, and stable variable frequency speed regulation, aligning with diverse motor applications. However, achieving high-performance requires dynamic current loop control. Traditional Proportional-Integral (PI) control, though widely used, suffers from limited bandwidth and overshoot. Alternatively, deadbeat control, leveraging current prediction, offers superior dynamic response. In this study, deadbeat control demonstrates a significant improvement of approximately 20% in current loop response compared to PI control, as evaluated through simulations. Addressing challenges posed by varying motor parameters, this study incorporates Luenberger observer for parameter disturbance compensation, further enhancing control robustness by 15%. The effectiveness of the proposed method, emphasizing deadbeat control, is validated through Simulink simulations, promising a robust future for PMSM control.

To improve the slip rate control effect for different road conditions during emergency braking of wheel hub motor vehicles, as well as to address the problems of uncertainty and nonlinearity of the system when the electro-mechanical braking system is used as the actuator of the ABS, a hierarchical control strategy of the anti-lock braking system (ABS) using active disturbance rejection control (ADRC) is proposed. Firstly, a vehicle dynamics model and an ABS model based on the EMB system are established; secondly, a speed observer based on the dilated state observer is used in the upper layer to design a pavement recognition algorithm, which recognizes the current pavement and outputs the optimal slip rate; then, an ABS controller based on the ADRC algorithm is designed for the lower layer to track the optimal slip rate. In order to verify the performance of the pavement recognition method and control strategy, vehicle simulation software is used to establish the model and simulation. The results show that the road surface recognition method can quickly and effectively recognize the road surface, and comparing the emergency braking control effects of PID and SMC under different road surface conditions, the ADRC strategy has better robustness and reliability, and improves the braking effect. [ABSTRACT FROM AUTHOR]

This study focuses on the problems of poor control performance, synchronization performance and stability in multi-motor permanent magnet drive systems in mining belt conveyors when a Proportional Integral Derivative (PID) controller is used to control the multi-motor. In this paper, a system model for three-motor synchronous control of a mine belt conveyor is established. On this basis, an Enhanced first-order Active Disturbance Rejection Controller (Efal_ADRC) is designed based on an optimized nonlinear function. Additionally, a weighted arithmetic mean is used to enhance the compensator of the ring coupling control structure. Finally, the system model is evaluated and simulated using various algorithms. Results show that synchronous control of a multi-Permanent Magnet Synchronous Motor (multi-PMSM) drive system based on the Efal_ADRC ring coupling control structure has better anti-interference ability, control accuracy and synchronization, which is conducive to the stable and efficient safe operation of the belt conveyor. [ABSTRACT FROM AUTHOR]

In response to the chattering issue inherent in sliding mode observers during rotor position estimation and to enhance the stability and robustness of sensorless control systems for surface-mounted permanent magnet synchronous motors (SPMSM), this study proposes a rotor position estimation algorithm for SPMSM based on an improved super-twisting sliding mode observer (ISTSMO) and a second-order generalized integrator (SOGI) structure. Firstly, the super-twisting algorithm is introduced to design the observer, which effectively attenuates the sliding mode chattering by using continuous control signals. Secondly, SOGI is introduced in the filtering stage, which not only effectively addresses the time delay issues caused by traditional low-pass filters but also enables the observer to extract rotor position information by monitoring only the back electromotive force (back-EMF) signal of the α -phase, thereby simplifying the observer structure. Finally, the proposed scheme is experimentally compared with the traditional sliding mode observer on the YXMBD-TE1000 platform. The experimental results showed that during motor acceleration and deceleration tests, the average speed estimation error was reduced from 141 r/min to 40 r/min, and the maximum position estimation error was reduced from 0.74 rad to 0.29 rad. In load disturbance experiments, the speed variation decreased from 781 r/min to 451 r/min, and the steady-state speed fluctuation was significantly reduced. These results confirm that the proposed observer exhibits superior stability and robustness. [ABSTRACT FROM AUTHOR]

The potential of knee-wheeled wheel-legged robots (KW-WLRs) might be underestimated within the domain of wheel-legged robots (WLRs). When transitioning into legged motion mode, KW-WLRs exhibit passability comparable to traditional legged mobile robots on rough terrain. However, this comes at the cost of reduced flexibility due to the larger inertia of legs, when carrying a redundant knee-wheel. To address this challenge, a wheel-leg cooperation strategy is proposed, that leverages the redundant knee-wheel to enhance legs motion. Analyzing the characteristics of hydraulic actuators for the legs and a permanent magnet synchronous motor (PMSM) for the wheel in our KW-WLR mechanism, we design a delayed nonlinear model predictive controller (DNMPC) to collaboratively control the wheel-leg system. Simulation results demonstrate that our proposed wheel-leg collaborative strategy and controller, which consider the actuators' characteristics, improve toe-end tracking performance across diverse working conditions and enhance the overall KW-WLR's locomotion skill. [ABSTRACT FROM AUTHOR]

Switched reluctance motors (SRMs) have gained widespread attention across various industries due to their inherent advantages, including simple construction, high efficiency, and the absence of permanent magnets. However, a critical aspect of SRM operation is the need for position sensors. Consequently, extensive research has been conducted on position-sensorless control techniques for SRMs. However, unexpected faults are critical for achieving position-sensorless control performance. This paper proposes a fault-tolerant position-sensorless control method for SRMs based on an adaptive sliding mode observer (ASMO) that specifically addresses the issue of converter faults. The proposed method estimates the faulty phase voltage in real-time, enabling the design of a dynamic model of the SRM that accurately reflects the fault conditions. The paper presents a theoretical analysis of the convergence condition of the estimation error. As a result, the proposed method can accurately estimate the faulty phase voltage and the rotor position using only phase current measurements, without the need for a dedicated position sensor. Simulation results are provided to demonstrate the performance of the proposed control method and its superiority compared to the existing sliding mode observer-based position-sensorless control method. [ABSTRACT FROM AUTHOR]

To address the susceptibility of conventional vector control systems for permanent magnet synchronous motors (PMSMs) to motor parameter variations and load disturbances, a novel control method combining an improved Grasshopper Optimization Algorithm (GOA) with a variable universe fuzzy Proportional-Integral (PI) controller is proposed, building upon standard fuzzy PI control. First, the diversity of the population and the global exploration capability of the algorithm are enhanced through the integration of the Cauchy mutation strategy and uniform distribution strategy. Subsequently, the fusion of Cauchy mutation and opposition-based learning, along with modifications to the optimal position, further improves the algorithm's ability to escape local optima. The improved GOA is then employed to optimize the contraction-expansion factor of the variable universe fuzzy PI controller, achieving enhanced control performance for PMSMs. Additionally, to address the high torque and current ripple issues commonly associated with traditional PI controllers in the current loop, Model Predictive Control (MPC) is adopted to further improve control performance. Finally, experimental results validate the effectiveness of the proposed control scheme, demonstrating precise motor speed control, rapid and stable current tracking, as well as improved system robustness. [ABSTRACT FROM AUTHOR]

The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, artificial intelligence (AI), machine learning (ML), and deep learning (DL), revealing a significant annual growth rate of 56.4% in research activity. Key findings include the identification of influential journals, authors, countries, and collaborative networks that have driven advancements in this domain. This study highlights emerging trends, such as the integration of renewable energy sources, vehicle-to-grid (V2G) schemes, and the application of AI in EV battery optimization, charging infrastructure, and energy consumption prediction. The analysis also uncovers challenges in addressing information security concerns. By reviewing the top-cited papers, this research underlines the transformative potential of AI-driven solutions in enhancing EV performance and scalability. The results of this study can be useful for practitioners, academics, and policymakers. [ABSTRACT FROM AUTHOR]

A cascade control structure (CCS) is still the most commonly used control scheme in variable speed control (VSC) electrical drives with alternating current (AC) motors. Several tuning methods are used to select the coefficients of controllers applied in CCS. These approaches can be divided into analytical, empirical, and heuristic ones. Regardless of the tuning method used, there is still a question of whether the CCS is tuned optimally in terms of considered performance indicators to provide high-performance behavior of the electrical drive. Recently, artificial intelligence-based methods, e.g., swarm-based metaheuristic algorithms (SBMAs), have been extensively examined in this field, giving promising results. Moreover, the intensive development of artificial intelligence (AI) assistants based on large language models (LLMs) supporting decision-making processes is observed. Therefore, it is worth examining the ability of LLMs to tune the CCS in the VSC electrical drive. This paper investigates tuning methods for the cascade control structure equipped with PI-type current and angular velocity controllers for PMSM drive. Sets of CCS parameters from electrical engineers with different experiences are compared with reference solutions obtained by using the SBMA approach and LLMs. The novel LLM-based Tuning Assistant (TA) is developed and trained to improve the quality of responses. Obtained results are assessed regarding the drive performance, number of attempts, and time required to accomplish the considered task. A quantitative analysis of LLM-based solutions is also presented. The results indicate that AI-based tuning methods and the properly trained Tuning Assistant can significantly improve the performance of VSC electrical drives, while state-of-the-art LLMs do not guarantee high-performance drive operation. [ABSTRACT FROM AUTHOR]

This work presents a sensorless brushless DC motor (BLDCM) drive control, optimized for solar photovoltaic (PV)- and battery-fed light electric vehicles (LEVs). A back-electromotive force (EMF) observer integrated with an enhanced quadrature-phase-locked-loop (QPLL) structure is proposed for accurate rotor position estimation, addressing limitations of existing control methods at low speeds and under dynamic conditions. The study replaces the conventional arc-tangent technique with a QPLL-based approach, eliminating low-pass filters to enhance system adaptability and reduce delays. The experimental results demonstrate a significant reduction in commutation error, with a nearly flat value at 0 degrees during steady-state and less than 8 degrees under dynamic conditions. Furthermore, the performance of a modified single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) in solar-powered LEVs is verified, minimizing current ripple and ensuring smooth motor operation. The system also incorporates a regenerative braking mechanism, extending the vehicle's range by efficiently recovering kinetic energy through the battery with 30.60% efficiency. The improved performance of the proposed method and system over conventional approaches contributes to the advancement of efficient and sustainable solar-powered BLDC motor-based EV technologies. [ABSTRACT FROM AUTHOR]

This paper presents the application of a proposed hybrid control strategy that is designed to enhance the performance and robustness of a grid-connected wind energy conversion system (WECS) using a Five-Phase Permanent Magnet Synchronous Generator (FP-PMSG). The proposed approach combines the second-order terminal sliding mode control technique (SO-STA) with the super-twisting algorithm (STA), with the main goal of benefitting from both their advantages while addressing their limitations. Indeed, the sole application of the SO-STA ensures rapid convergence and robust performances in nonlinear systems, but it leads to chattering and reduces the whole system's efficiency. Therefore, by incorporating the STA, the obtained hybrid control can mitigate this issue by ensuring smoother control actions and a superior dynamic response. This designed hybrid control strategy improves the adaptability of the control system to wind fluctuations and enhances the system's robustness against external disturbances and uncertainties, leading to higher reliability and efficiency in the wind energy conversion system. Furthermore, the proposed hybrid control allows optimizing the power extraction and boosting the WECS's efficiency. It is worth clarifying that, besides this proposed control, a sliding mode controller is used for the grid side converter (GSC) and DC link voltage to ensure stable power transfer to the grid. The obtained simulation results demonstrate the effectiveness of the proposed strategy in improving the stability, robustness, and efficiency of the studied WECS under dynamic conditions, creating a promising solution for control in renewable energy systems operating under severe conditions. [ABSTRACT FROM AUTHOR]

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In this paper, a review of the advances in brushless synchronous motors is presented because there has been an increasing interest in advanced motor control and to address the weaknesses of conventional motor control. The traditional motor control strategies, for example, proportional plus integral controllers (PIs), are simple and easy to maintain. On the contrary, they require accurate tuning and are affected by motor parameter variations. To address these challenges and many others (power factors, torque ripple, current limit, voltage limit, speed limit), advanced control methods are required to enhance the performance of the motor drive control. The advanced control techniques include model predictive control, slide mode control, reinforcement learning, and fuzzy logic control. This paper provides a comprehensive review of advances in control methods and addresses the challenges and limitations associated with their practical application. [ABSTRACT FROM AUTHOR]

Permanent Magnet Synchronous Motors (PMSMs) are nonlinear, multi-parameter systems that exhibit structural symmetry but are susceptible to parameter variations and external disturbances. These challenges can disrupt the inherent symmetrical characteristics of PMSM dynamics during real-world operations, posing difficulties for achieving efficient control. To address this issue, this paper proposes a Model-Free Generalized Super-Twisting Algorithm Fast Terminal Sliding Mode Control (MFFTSMC-GSTA) method. First, a novel ultra-local model incorporating PMSM uncertainties is established, and the MFFTSMC-GSTA controller is designed to address the system's complex dynamic behavior. By integrating the generalized super-twisting algorithm with the nonsingular fast terminal sliding mode algorithm, the proposed controller ensures finite-time convergence and effectively mitigates chattering. Second, an extended sliding mode disturbance observer is developed to estimate the unknown components of the ultra-local model and provide feedforward compensation, further enhancing system robustness and dynamic performance. The experimental results show that the total harmonic distortion (THD) value of the proposed control method is 1.38%, demonstrating significant improvements in response speed and robustness for motor speed control, and verifying the algorithm's superior performance under complex operating conditions. [ABSTRACT FROM AUTHOR]

In ship propulsion, accurately diagnosing faults in permanent magnet synchronous motor is essential but challenging due to limitations in the intuitive characterization and feature extraction of fault signals. This study presents an innovative approach to motor fault detection by integrating phase-contrastive current dot patterns with an enhanced residual network, enhancing the diagnostic effect. Initially, the research involves creating a dataset that simulates stator currents. It is achieved through mathematical modeling of two common faults in permanent magnet synchronous motors: inter-turn short circuits and demagnetization. Subsequently, the parameters of the phase-contrastive current dot pattern are optimized using the Hunter-Prey Optimization technique to convert the three-phase stator currents of the motor into grayscale images. Lastly, a residual network, which includes a Squeeze-and-Excitation module, is engineered to boost the identification of crucial fault characteristics. The experimental results show that the proposed method achieves a high accuracy rate of 98.5% in the fault diagnosis task of motors, which can accurately identify the fault information and is significant in enhancing the reliability and safety of ship propulsion systems. [ABSTRACT FROM AUTHOR]

A variable fractional-order (VFO) fuzzy sliding mode controller is designed to control the speed of a permanent magnet synchronous motor (PMSM). First, a VFO sliding mode surface is established. Then, a VFO fuzzy sliding mode controller is designed, capable of suppressing the effects of parameter uncertainties and disturbances to achieve precise PMSM speed control. The global stability and finite time convergence of the controlled system state are demonstrated using Lyapunov stability theory. The numerical and experimental results validate the effectiveness of the controller, showing better immunity to disturbances and a smaller overshoot compared to PID and fixed-order fuzzy sliding mode controllers. [ABSTRACT FROM AUTHOR]

In order to improve the operation control performance of high-speed maglev trains, an improved finite-time rotor magnetic Field-Oriented Control method was proposed in this paper. Aiming at the stator current control problem of long-stator linear synchronous motors under parametric perturbation, this paper investigates the double-feeding mode, combines the predefined-time stability theory and designs an improved sliding mode controller to optimise the dynamic characteristics of the inner-loop system. In the outer-loop cruise control, the predefined-time sliding mode control is combined with a finite-time disturbance observer, which effectively solves the problems of inaccurate modelling and parameter ingestion. It was verified through simulation and analysis that the control strategy has significant advantages in improving the dynamic tracking performance and anti-interference ability, with the stator current stabilisation time within 0.1 s, the absolute value of the fluctuation error within 20 A, the outer-loop response time within 0.5 s, the maximum speed error within 0.0005 m/s and the maximum displacement error within 0.0005 m. The control strategy has the advantages of improving the dynamic tracking performance and anti-interference ability. [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]

Enhancing the performance of 5ph-IPMSM control plays a crucial role in advancing various innovative applications such as electric vehicles. This paper proposes a new reinforcement learning (RL) control algorithm based twin-delayed deep deterministic policy gradient (TD3) algorithm to tune two cascaded PI controllers in a five-phase interior permanent magnet synchronous motor (5ph-IPMSM) drive system based model predictive control (MPC). The main purpose of the control methodology is to optimize the 5ph-IPMSM speed response either in constant torque region or constant power region. The speed responses obtained using RL control algorithm are compared with those obtained using four of the most recent metaheuristic optimization techniques (MHOT) which are Transit Search (TS), Honey Badger Algorithm (HBA), Dwarf Mongoose (DM), and Dandelion-Optimizer (DO) optimization techniques. The speed response are compared in terms of the settling time, rise time, maximum time and maximum overshoot percentage. It is found that the suggested RL based TD3 give minimum settling time and relatively low values for the rise time, max time and overshoot percentage which makes the RL provide superior speed responses compared with those obtained from the four MHOT. The drive system speed responses are obtained in the constant torque region and constant power region using MATLAB SIMULINK package. [ABSTRACT FROM AUTHOR]

The permanent magnet synchronous motor (PMSM) control is demanding task because, which is a nonlinear multivariable coupling system. The vector control has been widely used in the control of PMSM system, where the rotor position information is needed for close loop control system. The mechanical sensors are used for rotor position, which increased the cost of the system. These sensors can be eliminated by adopting the sensorless control methods. In this paper a new continuous terminal sliding mode observer (CTSMO) based speed sensorless control method is proposed, which is substitute of the conventional SMO (CSMO). The suggested the CTSMO is robust against parametric change and load disturbances, which is used to estimate the speed and rotor position from the back EMF (BEMF) of the PMSM system. The conventional SMO needs the low pass filter (LPF), position compensation techniques and sigmoid function instead of sign function to decrease the chattering phenomena. To eliminate the chattering phenomena effectively the new CTSMO based speed sensorless method is designed in this paper, which has also ability of minimization of the speed and rotor position error. The stability of the proposed technique is analyzed by Lyapunov function, the simulation have been carried out for the validation of the suggested technique.

To address the impact of load variations, external environmental changes, and the tuning of the parameters on Permanent Magnet Synchronous Motors (PMSMs) used in ships, this study proposes an Active Disturbance Rejection Control (ADRC) strategy for PMSMs, optimized by the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, based on the PMSM model, the study addresses the limited disturbance rejection capability of the traditional fal function in the Extended State Observer (ESO) of conventional ADRC. To improve the accuracy of the state observer, the faln function is introduced as a replacement for the traditional fal function. Second, due to the numerous parameters in ADRC, which are difficult to tune, the QPSO algorithm—known for its strong global search capabilities and fast convergence speed—is utilized for parameter optimization. Additionally, the position update formula within the optimization algorithm is revised and optimized. Finally, simulation experiments are conducted using the Matlab/Simulink platform, where practical conditions, such as load fluctuations and random noise, are incorporated. The simulation results demonstrate that, compared to PSO-ADRC control, IPSO-ADRC control, and ICFO-ADRC control, the proposed method offers a superior dynamic response. Specifically, the speed control accuracy is improved by 46.7%, torque ripple is reduced by 50.8%, and harmonic distortion decreases by 23.1%. These results highlight the significant advantages of this method in enhancing system robustness, dynamic response speed, and steady-state accuracy, making it particularly suitable for PMSM control systems in complex dynamic environments, such as those encountered on ships.