Abstract Field-oriented control (FOC) is a widely used strategy that allows independent control of torque and flux in induction motor systems. By applying Park transforms to convert three-phase signals into a dq reference frame, accurate motor speed regulation is achieved while maintaining constant flux for optimal performance and efficiency. Traditional field-oriented control (TFOC) assumes constant rotor resistance; however, variations in this parameter can degrade control accuracy and stability. This research presents an enhanced field-oriented control (EFOC) system that effectively compensates for rotor resistance variations by directly regulating the flux vector in the dq frame, ensuring flux stability and reducing reactive power consumption. Additionally, sliding mode control (SMC) is applied to the outer and inner loops to enhance system robustness, minimise torque ripples and improve dynamic response. Simulation results confirm that the proposed EFOC maintains the flux at its nominal value under resistance changes, achieving a 20% reduction in reactive power, approximately 6% improvement in power factor and reducing torque ripples from 13 N · m −1 to 0.3 N · m −1 , demonstrating superior performance compared with conventional proportional-integral (PI)-based FOC systems.

Abstract The demand for lightweight, thermally stable, and energy-efficient power systems in aerospace applications has prompted the investigation of advanced magnetic materials beyond traditional alloys, such as Hiperco 50. This study aims to address the limitations of high core loss, poor efficiency, and thermal instability in conventional aerospace generators. A high-speed (10,000 RPM, 500 Hz) radial flux permanent magnet generator (RFPMG) was designed using Finemet as the stator material, soft magnetic composites (SMC) for the rotor, and Litz wire windings to minimise eddy current and copper losses. Electromagnetic and thermal simulations were conducted using ANSYS Maxwell and Motor-CAD to ensure adherence to aerospace standards (IEC 60034-1, SAE ARP4990, IEEE 1812-2014). The results indicate that the proposed Finemet–SMC design achieves a 59% increase in output power (7.49 kW vs. 4.71 kW), an 11% improvement in efficiency (96.19% vs. 86.47%), and a 60% reduction in total losses, with stator and winding temperatures reduced by up to 45%. The torque ripple decreased by 44%, ensuring a smoother performance. These findings demonstrate that the proposed configuration not only surpasses standard aerospace requirements but also establishes a lightweight, cost-effective, and sustainable generator design suitable for next-generation hybrid and electrified aircraft propulsion systems.

Abstract DC motor speed control is fundamental in modern industrial and robotic systems, where high precision, robustness and energy efficiency are required. Conventional integer-order Proportional–Integral–Derivative (PID) controllers often fail to capture the non-linearities and parameter variations inherent in real DC motors. This study proposes a control framework combining fractional-order (FO) system identification with an optimised fractional-order proportional-integral-derivative (FOPID) controller. The five FOPID parameters are optimised using four metaheuristic algorithms: Grey Wolf Optimizer (GWO), Firefly Algorithm (FA), artificial bee colony (ABC) and ant colony optimisation (ACO). Experimental validation on a MATLAB/Simulink R2024 (The MathWorks, Inc., Natick, MA, USA) , an Arduino board, and a DC motor platform demonstrates that the particle swarm optimisation (PSO) FOPID controller achieves a settling time of 1.08 s with 2.00% overshoot and a control effort of 1.8 V/√s. Compared to the extended Ziegler–Nichols tuned FOPID, the PSO approach achieves 98.57% faster settling while maintaining comparable overshoot and demonstrating superior energy efficiency. Among the metaheuristic algorithms tested, PSO demonstrates the best overall performance with the lowest identification error and the most energy-efficient control effort. These results confirm the superiority of the metaheuristic optimisation approach over conventional tuning methods in terms of dynamic response, precision, and robustness for fractional-order control systems.

Abstract This paper presents the concept of an advanced rail balancer (ARB) for next-generation traction substations (TSSs), along with its control algorithm based on a novel advanced resonant controller. The ARB utilises a cascaded H-bridge (CHB) converter topology and is designed to effectively address several key issues in modern TSSs, including load current symmetrisation in the distribution power grid, reactive power compensation, harmonic currents from railway vehicles and active filtering of higher-order harmonics in the grid current. The proposed asymmetric control of the ARB converter includes individual ARB phase current control using an advanced resonant controller. The resonant controller algorithm provides the desired frequency response without offset and uses direct latency compensation to mitigate the effects of low switching frequency and large control sampling times. Experimental results show very good performance of the proposed control algorithm, especially for grid current harmonics filtration with low ARB switching frequency, large sampling times and control latency.

Abstract This article focuses on the scalar control of four induction machine structures: the conventional induction machine, the double-star machine, the open-ended stator winding machine and the dual open-end stator winding machine. To compare these different structures under the same conditions, we used a single induction machine prototype to design these four machine structures. In addition, we also developed a single scalar control algorithm to control these four machines, which is implemented in a field-programmable gate array board FPGA. The experimental results showed the efficiency of the operation of this prototype as well as that of the algorithm developed for the four machines. The experimental results also presented the significant advantage of open-end stator winding structures over star winding structures. The increase in three-phase windings allows for better power segmentation, which is better when the three-phase stator windings are open-ended.

Abstract This research introduces an innovative on-grid hybrid renewable generation (OG-HRG) system characterised by its distinctive combination of three technologies: solar photovoltaic (PV), gearless permanent magnet synchronous generator (PMSG)-based wind turbines (WTs) and a flywheel energy storage system (FESS). The particularity of this system lies in its advanced incorporation of a multi-layered control approach, which merges the adaptability of fuzzy logic control (FLC), the accuracy of proportional-integral-derivative (PID) control and the resilience of sliding mode control (SMC) into a unified energy management system (EMS). This design has been specifically developed to address intermittency and maintain grid stability. Simulation outcomes validate its effectiveness, showcasing 92.3% renewable energy utilisation, minimal frequency fluctuations (±0.8%) and an 85% decrease in CO 2 emissions. This study offers a new framework for achieving high-efficiency and stable integration of renewable energy sources.

Abstract In the design process of a photovoltaic (PV) power plant, determination of the maximum power that can be extracted from the PV modules is essential, especially for the dimensioning of the individual parts of the plant. This paper presents the determination of the maximum power point (MPPT) of a bifacial PV system using three different cell models. The optimal power point is determined by using a novel multi-verse optimization (MVO) algorithm as the optimization tool. In this research work the MPPT of bifacial PV modules is determined by using the following three PV cell models: ideal single diode model, real single diode model, and two-diode model of PV cell. These cell models are developed for single-sided PV modules and therefore a proper modification of the models is necessary in order to be applied for the investigated modules. The purpose of this optimization procedure is to determine the maximum power of a bifacial PV module by minimizing the power difference between the calculated power and the experimentally determined power for certain atmospheric conditions.

Abstract Sensors calibration plays a crucial role in controlling systems and achieving fault-tolerant control by ensuring accuracy, performance, safety, energy efficiency, and compliance with standards. It is an essential to maintain the reliability and effectiveness of modern control systems across various applications. In this paper, we represent a new algorithm that processes a set of raw data collected by a sensor to find the mapping function that relates the raw data to the real value of the measured signal by the sensor. Working on sensors with an unknown mapping function, unknown parameters, or with external disturbances, that affects their behaviour, represents a problem; moreover, it takes a lot of time and effort to calibrate the sensor before each use. Several techniques were used to overcome these aspects mostly by recording the output of the sensor for different input values that change manually, to calibrate the sensor. However, the represented technique in this paper can easily provide us with the input/output model of a specific sensor by doing only one experiment; it also improves the accuracy of the measurements as it is a self-calibrating technique that reduces the nonlinearity and noise problems to deliver a better estimation of the measured signal, which is validated in this paper experimentally using a low-cost current sensor by comparing the obtained results from this algorithm with the results using the extracted input/output model illustrated in the datasheet. Furthermore, if the sensor is pretty poor, and if the application requires more precision, the provided estimation by the mapping function can be mixed with other sensor/s readings using sensor fusion algorithms to find a more precise value of the input. The represented algorithm can also perform self-calibration while evaluating the functionality of the application and the variations of the temperature and other external disturbances that affect the sensor.

Abstract Solar energy, an available and renewable resource, can be efficiently transformed into electrical energy through the use of photovoltaic (PV) cells. The primary emphasis lies in the significance of maximising power output for economic considerations. In terms of optimising power generation, the implementation of maximum power point tracking (MPPT) techniques is imperative. A range of approaches, such as super twisting (ST) control and modified extremum seeking control (ESC-mod), are explored for their potential in enhancing the efficiency of power-generation systems. The novelty is a combination of these methods; the modified ESC has the role of finding the optimum voltage value of the global maximum power point (MPP) during the partial shading, while the super-twisting improves the performance of the system. The efficacy of the MPPT algorithm is assessed across diverse conditions, encompassing scenarios with load variations and fluctuating irradiances (uniform and non-uniform). The experimental setup involves essential components such as a PV generator, a boost converter and a resistive load. This comprehensive testing aims to evaluate the algorithm’s performance under varying circumstances, providing insights into its adaptability and effectiveness across different operational conditions. The system is modelled, simulated using Matlab–Simulink and implemented using a dSPACE 1104 card. Simulation results indicate that ST control is faster in reaching the permanent regime, but ESC-mod provides smoother performance in the permanent regime. The integration of both ST control and ESC-mod methods proves advantageous by diminishing the response time in the seeking process while concurrently ensuring a consistent and smooth operation in the permanent regime. This combined approach has undergone practical implementation and testing across diverse conditions, encompassing both optimal, healthy states and shaded environments. The results affirm the method’s ability to deliver efficient and stable performance across a spectrum of operating conditions.