Search Results (542)

In flexible DC distribution systems, the three-phase four-leg (3P4L) converter demonstrates excellent performance in addressing three-phase load imbalance problems, but suffers from DC-side second harmonics and complex multi-parameter control coordination. In this paper, a flying capacitor zero-sequence leg-based 3P4L (FCZS-3P4L) converter is proposed, which introduces the three-level flying capacitor structure into the fourth zero-sequence leg, making it possible to suppress the DC-side second harmonics by using the flying capacitor for energy buffering. Meanwhile, a modulated model predictive control (MMPC) strategy for proposed FCZS-3P4L is presented. This strategy utilizes a dual-layer control strategy based on a phase-split power outer loop and a model predictive current inner loop to simultaneously achieve AC three-phase imbalance current compensation and the energy buffering of the flying capacitor, thereby eliminating the complex parameter coordination among multiple control loops in conventional control structures. A MATLAB-based simulation model and Star-Sim hardware-in-the-loop (HIL) semi-physical experimental platforms are built. The results show that the proposed FCZS-3P4L converter and corresponding MMPC control can effectively reduces three-phase current unbalance by 19.57%, and reduce the second harmonic amplitude by 57%, i.e., decreasing from 14.74 V to 6.31 V, simultaneously realizing DC-side second harmonic and AC-side three-phase unbalance suppression. Full article

The energy efficiency and hover stability of unmanned aerial vehicles are critical factors, since improper battery utilization and unstable control are major sources of operational failures and accidents. The proportional–integral–derivative (PID) controller, which is applied in approximately 97% of multirotor unmanned aerial vehicle (UAV) systems, is widely used due to its simplicity; however, it is sensitive to external disturbances and often fails to ensure optimal energy utilization, resulting in reduced flight time. Therefore, the experimental investigation of advanced control methods in a real physical environment is well justified. The objective of the present research is the comparative evaluation of seven control strategies—PID, linear quadratic controller with integral action (LQI), model predictive control (MPC), sliding mode control (SMC), backstepping control, fractional-order PID (FOPID), and H∞ control—using a single-degree-of-freedom drone test platform in a MATLAB R2023b-Arduino hardware-in-the-loop (HIL) environment. Although the theoretical advantages and model-based results of the aforementioned control methods are well documented, the number of real-time comparative HIL experiments conducted under identical physical conditions remains limited. Consequently, only a small amount of unified and directly comparable experimental data is available regarding the performance of different controllers. The measurements were performed at a reference height of 120 mm under disturbance-free conditions and under wind loading with a velocity of 10 km/h applied at an angle of 45°. The controller performance was evaluated based on hover accuracy, settling time, overshoot, and real-time measured power consumption. The results indicate that modern control strategies provide significantly improved energy efficiency and faster stabilization compared to the PID controller in both disturbance-free and wind-loaded test scenarios. The investigations confirm that several advanced controllers can be applied more effectively than the PID controller to enhance hover stability and reduce energy consumption. Full article

This work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of $\pm 50\%$; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects. Full article

The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the first stage, a metric learning-based distributed model predictive control (ML-DMPC) strategy is proposed to enable cooperative longitudinal control among heterogeneous vehicles, explicitly incorporating inter-vehicle interactions to improve speed tracking, ride comfort, and platoon-level energy efficiency. In the second stage, a multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is developed for real-time energy management, achieving an optimal power split between the engine and battery while reducing Q-value overestimation and accelerating learning convergence. Simulation results across multiple standard driving cycles demonstrate that the proposed framework outperforms conventional distributed model predictive control (DMPC) and multi-agent deep deterministic policy gradient (MADDPG)-based methods in fuel economy, stability, and convergence speed, while maintaining battery state of charge (SOC) within safe limits. To facilitate future experimental validation, a dSPACE-based hardware-in-the-loop (HIL) architecture is designed to enable real-time deployment and testing of the proposed control framework. Full article

The increasing complexity of spacecraft On-Board Software (OBSW) necessitates advanced development and testing methodologies to ensure reliability and robustness. This paper presents a digital twin approach for the development and testing of embedded spacecraft software. The proposed electronic digital twin enables high-fidelity hardware and software simulations of spacecraft subsystems, facilitating a comprehensive validation framework. Through real-time execution, the digital twin supports dynamical simulations with possibility of failure injections, enabling the observation of software behavior under various nominal or fault conditions. This capability allows for thorough debugging and verification of critical software components, including Finite State Machines (FSM), Guidance, Navigation, and Control (GNC) algorithms, and platform and mode management logic. By providing an interactive and iterative environment for software validation in nominal and contingency scenarios, the digital twin reduces the need for extensive Hardware-in-the-Loop (HIL) testing, accelerating the software development life-cycle while improving reliability. The paper discusses the architecture and implementation of the digital twin, along with case studies based on a modular OBSW architecture, demonstrating its effectiveness in identifying and resolving software anomalies. This approach offers a cost-effective and scalable solution for spacecraft software development, enhancing mission safety and performance. Full article

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article

Hub motor-driven vehicles can control vehicle attitude by regulating the speed and torque of four wheels, supporting safe and stable lane changing and obstacle avoidance. However, under high-speed scenarios, these vehicles often suffer from poor stability, limited comfort, and inadequate trajectory tracking accuracy during lane changing and obstacle avoidance operations. To address these challenges, this study proposes a lane changing obstacle avoidance control strategy for hub motor-driven vehicles based on collision risk prediction. A fuzzy controller featuring a variable weight objective function is designed to balance lane changing efficiency and ride comfort, thereby generating an optimal lane changing and obstacle avoidance trajectory. Furthermore, a linear time-varying model predictive controller (LTV-MPC) is developed, which adaptively adjusts both the weighting coefficient of lateral displacement error in the objective function and the prediction horizon of the controller, enabling dynamic tuning of vehicle trajectory tracking accuracy. A dSPACE hardware-in-the-loop (HIL) platform was established to conduct simulations under typical obstacle avoidance scenarios. The simulation results show that under two easily destabilized conditions—high-adhesion, high-speed, large-curvature, and low-adhesion, medium-speed, large-curvature maneuvers—the proposed optimized control strategy limits the maximum lateral trajectory tracking error to 0.116 m and 0.143 m, representing reductions of 58.6% and 79.6% compared with the baseline control strategy. These results demonstrate that the proposed method enhances trajectory tracking accuracy and stability during lane changing and obstacle avoidance maneuvers. Full article

Accurate prediction of the performance of an internal combustion engine is an essential step towards achieving efficiency and complying with emission standards. This study presents an artificial neural network (ANN) model that uses sensor-derived parameters, such as design power, wheel power, torque, and rotational speed, to predict engine load. Data were collected from a dynamometer and a hardware-in-the-loop (HiL) setup to ensure realistic, sensor-based measurements. The proposed ANN architecture achieved high accuracy (99%) in multiclass classification and strong regression performance ($R^2\approx 0.98$), demonstrating its ability to model complex engine load relationships under normal operating conditions. Performance was validated using 5-fold stratified cross-validation, achieving an average accuracy of $0.988\pm 0.011$, macro-F1 of $0.984\pm 0.011$, and regression $R^2$ of $0.962\pm 0.052$, confirming strong generalization and robustness. The model can be extended to include additional sensor inputs and adapted for use with other powertrain systems, allowing it to be used in a range of automotive and industrial applications. Full article

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems. Full article

In practical engineering, large-scale wind power integration typically requires long-distance transmission lines to deliver power to load centers. The resulting weak sending-end systems lack support from synchronous power sources. Under extreme weather conditions, the rapid increase in active power output caused by high wind power generation may lead to voltage instability. In existing projects, a phenomenon of repeated voltage fluctuations has been observed under fault-free system conditions. This phenomenon is induced by the coupling of the characteristics of weak sending-end systems and low-voltage ride-through (LVRT) discrimination mechanisms, posing a serious threat to the safe and stable operation of power grids. However, most existing studies focus on the analysis of voltage instability mechanisms and the optimization of control strategies for single devices, with insufficient consideration given to voltage fluctuation suppression methods under the coordinated operation of wind power and energy storage systems. Based on the actual scenario of energy storage configuration in wind farms, this paper improves the traditional LVRT discrimination mechanism and develops a coordinated voltage ride-through control strategy for permanent magnet synchronous generator (PMSG) wind turbines and energy storage batteries. It can effectively cope with unconventional operating conditions, such as repeated voltage ride-through and deep voltage ride-through that may occur under extreme meteorological conditions, and improve the safe and stable operation capability of wind farms. Using a hardware-in-the-loop (HIL) test platform, the coordinated voltage ride-through control strategy is verified. The test results indicate that it effectively enhances the wind–storage system’s voltage ride-through reliability and suppresses repeated voltage fluctuations. Full article

DC motors play a fundamental role in robotic and mechatronic systems applied to the manufacturing industry; but broadly speaking, they are necessary in any system where motion is required. In these types of applications, precise control of position and speed is essential. To achieve this, accurate estimation of dynamic parameters such as inertia, viscous friction, and Coulomb friction is necessary to design efficient and sustainable control strategies. This study presents two methodologies for parameter identification based on the analysis of angular position data from a DC motor. The first method uses a constant (step) torque input, while the second is based on ramp excitation. The proposed method is entirely analytical, that is, it is based on the behavior of the system’s responses to the inputs; this makes the procedure practical and does not require computational cost. The experimental platform integrates a hardware-in-loop (HIL) system that allows for real-time acquisition and actuation, with responses processed in MATLAB/Simulink R2022a to provide the basis for estimating the inertia and friction parameters. To validate the values of the physical parameters, a closed-loop proportional-integral (PI) speed control system was implemented. The results confirm the accuracy and consistency of the identified parameters, highlighting their applicability for improving motor control performance in a wide range of robotic applications. Full article

This study presents a cost-efficient Hardware-in-the-Loop platform for liquid-level process control education, designed to bridge the gap between theoretical learning and real-world industrial practice. The proposed system integrates NI myRIO and NI myDAQ hardware with LabVIEW-based real-time simulation and controller implementation, enabling flexible experimentation across a range of linear and nonlinear tank models. Through real-time controllers, students can design, tune, and validate classical digital controllers while gaining hands-on experience with real-time process dynamics. Experimental results from Model-in-the-Loop and Hardware-in-the-Loop configurations confirm the high accuracy between simulated and hardware responses, with low normalized root mean square error (NRMSE < 0.07) and high normalized cross-correlation (NCC > 0.99) between MIL and HIL responses. Additionally, learning outcomes were assessed using rubrics and student perception surveys aligned with ABET criteria. The platform successfully satisfies ABET student outcomes (SO1, SO2, SO7) by promoting modeling, system identification, and real-time implementation skills. Student surveys reveal high satisfaction mean = 5.44 and a Cronbach’s α of 0.91367, highlighting enhanced engagement, flexibility, and confidence in control system design. This work demonstrates an adaptable, scalable educational solution that strengthens engineering competencies while keeping implementation costs low. Full article

Traditional diode-based rectifiers (TDRs) in railway traction substations (TSSs) are inefficient at handling bidirectional power flow and cannot recover regenerative braking energy (RBE). Replacing these conventional systems with reversible traction substations (RTSSs) requires detailed modeling, extensive simulations, and validation using real data. This paper presents a DT-oriented real-time modeling and Hardware-in-the-Loop (HIL) platform for the analysis and performance assessment of RTSSs in DC railway systems. The integration of interleaved PWM rectifiers enables bidirectional power flow, allowing efficient RBE recovery and its return to the main grid. Modeling railway networks with moving trains is complex due to nonlinear dynamics arising from continuously varying positions, speeds, and accelerations. The proposed approach introduces an innovative multi-train simulation method combined with low-level transient and power-quality analysis. The validated DT model, supported by HIL emulation using OPAL-RT, accurately reproduces real-world system behavior, enabling optimal component sizing and evaluation of key performance indicators such as voltage ripple, total harmonic distortion, passive-component stress, and current imbalance. The results demonstrate improved energy efficiency, enhanced system design, and reduced operational costs. Meanwhile, experimental validation on a small-scale RTSS prototype, based on data from the Italian 3 kV DC railway system, confirms the accuracy and applicability of the proposed DT-oriented framework. Full article

The solid oxide fuel cell–gas turbine (SOFC-GT) hybrid system is confronted with challenges related to system integration and coordinated control. In this study, a Controller Hardware-in-the-Loop Simulation (C-HILS) platform is constructed to validate its digital solutions. The C-HILS platform integrates the Advanced Process Simulation System (APROS), LabVIEW 2020 programming software, NI PXI hardware, and a distributed control system (DCS). Specifically, bidirectional data transmission between the simulation software and the DCS is facilitated through LabVIEW and PXI, leveraging the OLE for Process Control (OPC) protocol and physical Input and Output (I/O) channels. The dynamic SOFC-GT model developed in APROS demonstrates good consistency with design values, with relative errors below 4%. The DCS configuration employs PID controllers to achieve control over total power, SOFC fuel utilization, and gas turbine rotational speed. Experiments under transient conditions reveal that, despite discrepancies in dynamic responses between C-HILS and full-digital simulations, both can achieve stable control. This C-HILS platform effectively integrates virtual models with physical hardware, offering a reliable environment for verifying SOFC-GT control strategies and digital solutions, and thus facilitating the digital transformation of energy systems. Full article

This paper presents a Field-Programmable Gate Array (FPGA)-based Hardware-in-the-Loop (HIL) simulation of an Interleaved Boost Power Factor Correction (PFC) converter using the Sub-Cycle Average (SCA) modeling technique. The main objective is to achieve accurate real-time simulation performance given the hardware constraints of low-cost FPGAs. By combining the SCA modeling approach with a time-averaging correction method, the proposed model effectively reduces sampling delays and duty-cycle estimation errors arising from asynchronous Pulse Width Modulation (PWM) signal acquisition. The SCA-based converter model and time-averaging correction technique were implemented in MATLAB/Simulink R2024b using the HDL Coder environment. To validate real-time simulation accuracy, power factor improvement was evaluated for a two-phase Interleaved Boost PFC operating at a switching frequency of 60 kHz. Experimental results confirm that the proposed approach enables accurate Controller–HIL testing of power converters, even when implemented on low-cost FPGA platforms such as the Zybo Z7-10 evaluation board. Full article