Search Results (402)

The rapid integration of renewable energy into power systems has made voltage oscillations caused by the intermittency of wind and solar power a critical operational challenge. To mitigate these issues, this paper proposes a multi-mode coordinated reactive power control strategy to enhance voltage stability in renewable energy clusters. The approach integrates two key indicators: voltage sensitivity for steady-state regulation and an improved multi-renewable energy station short circuit ratio (MRSCR) that accounts for dynamic power interactions. Validation is conducted using a hardware-in-the-loop (HIL) platform combining real-time RMS-based simulation with physical controllers. Case studies on an offshore wind cluster demonstrate that the proposed method reduces voltage fluctuation amplitude more effectively than conventional automatic voltage control (AVC), successfully suppressing oscillations. The results confirm that the strategy exhibits stronger adaptability to varying grid conditions and offers a scalable solution for oscillation mitigation in large-scale renewable energy integration. Full article

In the development of multi-vehicle cooperative hardware-in-the-loop (HIL) simulation platforms based on machine vision, accurate vehicle pose estimation is crucial for achieving efficient cooperative control. However, monocular vision systems inevitably suffer from limited fields of view and insufficient image resolution during target detection, making it difficult to meet the requirements of large-scale, multi-target real-time perception. To address these challenges, this paper proposes an engineering-oriented multi-camera cooperative vision detection method, designed to maximize processing efficiency and real-time performance while maintaining detection accuracy. The proposed approach first projects the imaging results from multiple cameras onto a unified physical plane. By precomputing and caching the image stitching parameters, the method enables fast and parallelized image mosaicking. Experimental results demonstrate that, under typical vehicle speeds and driving angles, the stitched images achieve a 93.41% identification code recognition rate and a 99.08% recognition accuracy. Moreover, with high-resolution image (1440 × 960) inputs, the system can stably output 30 frames per second of stitched image streams, fully satisfying the dual requirements of detection precision and real-time processing for engineering applications. Full article

Steering angle control in self-driving cars is usually organized in layers combining trajectory planning, path tracking, and low-level actuator control. The steering controller converts the planned path into a desired steering angle and then ensures accurate tracking by the electric power steering (EPS). However, automotive electric power steering (AEPS) systems face many problems caused by model uncertainties, disturbances, and unknown system dynamics. In this paper, a robust finite-time control strategy based on an adaptive backstepping scheme is proposed to handle these problems. First, radial basis function neural networks (NNs) are designed to approximate the unknown system dynamics. Then, an adaptive sliding-mode disturbance observer (ASMDO) is introduced to address the impacts of the lumped disturbance. Enhanced control performance for the AEPS system is implemented using a combination of the above technologies. Numerical simulations and a hardware-in-the-loop (HIL) experimental verification are performed to demonstrate the significant improvement in performance achieved using the proposed strategy. Full article

Hybrid AC/DC microgrids (HMGs) are increasingly recognized as a solution for the transition toward future energy systems because they can combine the efficiency of DC networks with an AC system. Despite these advantages, HMGs still have challenges in protection, cybersecurity, and reliability. Conventional protection schemes often fail due to reduced fault currents and the dominance of power electronic converters in islanded or dynamically reconfigured topologies. At the same time, IEC 61850 protocols remain vulnerable to advanced cyberattacks such as Denial of Service (DoS), false data injection (FDIA), and man-in-the-middle (MITM), posing serious threats to the stability and operational security of intelligent power networks. Previous surveys have typically examined these challenges in isolation; however, this paper provides the first integrated review of HMG protection across three complementary dimensions: traditional protection schemes, cybersecurity threats, and artificial intelligence/machine learning (AI/ML)-based approaches. By analyzing more than 100 studies published between 2012 and 2024, we show that AI/ML methods in simulation environments can achieve detection accuracies of 95–98% with response times under 10 ms, while these values are case-specific and depend on the evaluation setting such as network scale, sampling configuration, noise levels, inverter control mode, and whether results are obtained in simulation, hardware in loop (HIL)/real-time digital simulator (RTDS), or field conditions. Nevertheless, the absence of standardized datasets and limited field validation remain key barriers to industrial adoption. Likewise, existing cybersecurity frameworks provide acceptable protection timing but lack resilience against emerging threats, while conventional methods underperform in clustered and islanded scenarios. Therefore, the future of HMG protection requires the integration of traditional schemes, resilient cybersecurity architectures, and explainable AI models, along with the development of benchmark datasets, hardware-in-the-loop validation, and implementation on platforms such as field-programmable gate array (FPGA) and μPMU. Full article

Distributed energy systems (DES), such as photovoltaics (PV), heat pumps (HPs), and electric vehicles (EVs), are being rapidly integrated into low-voltage (LV) grids, while measurement coverage remains limited. This paper presents a concept for an LV grid digital twin designed to enable real-time state estimation (SE) and operation-oriented studies under constrained measurement availability. Based on this concept, an exemplary digital twin is developed and demonstrated for a test area with a high PV penetration. The proposed digital twin integrates a topology-aware grid model, realistic parameterization, standardized IEC 61850 data modeling, and a real-time estimation pipeline that processes heterogeneous measurement data, including PV inverter power and voltage as well as transformer and feeder measurements. The approach is demonstrated through software-in-the-loop (SIL) experiments using historical playback and accelerated simulations, as well as hardware-in-the-loop (HIL) tests for real-time operation. The SIL results show that the digital twin enables continuous grid monitoring, enhances transparency for distribution system operators (DSOs), and leverages existing infrastructure to increase the effective PV hosting capacity. Selective PV curtailment mitigates congestion and restores normal operation, indicating a potentially cost-effective alternative to grid reinforcement. The HIL experiments emphasize the importance of high-quality, standardized data. The achieved accuracy depends on data availability and synchronization, highlighting the need for improved data integration. Overall, the proposed approach provides a viable pathway toward data-driven planning and operation of LV grids with high DES penetration. Full article

This paper addresses the critical scalability challenge in Hardware-in-the-Loop (HIL) channel emulation for massive RIS-assisted 6G environments. We propose a Two-Dimensional Dynamic Logic Resource Allocation (2D-DLRA) architecture that decouples physical RF ports from baseband processing resources through hierarchical pooling at both the session level and the multipath level. By jointly virtualizing Logical Units (LUs) and Multipath Processing Units (MPUs), the proposed architecture overcomes the dual inefficiency of port underutilization and path-level sparsity inherent in conventional static designs. A rigorous analytical framework combining hierarchical queuing theory and non-cooperative game theory is developed to characterize system capacity, blocking probability, and user contention under heterogeneous workloads. Simulation results demonstrate that, under a strict QoS constraint of 1% blocking probability, the proposed 2D-DLRA architecture achieves a multi-fold increase in supported user capacity compared to static allocation with the same hardware resources. Moreover, for an end-to-end emulation error threshold of 3%, 91.8% of users meet the QoS requirement, compared to only 73.6% in static architectures. The results further show that dynamic pooling enables near-saturated hardware utilization, in contrast to the single-digit utilization typical of static designs in sparse RIS scenarios. These findings confirm that 2D-DLRA provides a scalable and hardware-efficient solution for large-scale RIS channel emulation, offering practical design guidelines for next-generation 6G HIL testing platforms. Full article

Hardware-in-the-loop (HIL) simulation is an essential tool for rapid and cost-effective development and validation of power-electronic systems. The primary objective of this work is to validate and fine-tune a PLECS-based HIL model of a single dual active bridge (DAB) DC-DC converter, thereby laying the foundation for building more complex models (e.g., multiple converters connected in series or parallel). To this end, the converter is experimentally characterized, and the HIL model is validated across a wide range of operating conditions by varying the PWM phase-shift angle, voltage gain, switching frequency, and leakage inductance. Power transfer and efficiency are analyzed to quantify the influence of these parameters on converter performance. These experimental trends provide insight into the optimal modulation range and the dominant loss mechanisms of the DAB under single phase shift (SPS) control. A detailed comparison between HIL simulations and hardware measurements, based on transferred power and efficiency, shows close agreement across all the tested operating points. These results confirm the accuracy and robustness of the proposed HIL model, demonstrate the suitability of the PLECS platform for DAB development and control validation, and support its use as a scalable basis for more complex multi-converter studies, reducing design time and prototyping risk. Full article

This paper presents a controller-hardware-in-the-loop simulation (C-HILS) framework for validating models, evaluating control performance, and assessing the thermal lifetime of a tens-of-kilowatt inverter. The real inverter and the C-HILS platform were operated in parallel, and accuracy was quantified using phase-current root mean square error, voltage spectral analysis, and total harmonic distortion (THD). Across a wide range of SVPWM and DPWM cases, deviations remained within 2–5%, confirming close agreement between experiment and simulation. Using the validated C-HILS system, sampling frequency and output power were swept while comparing current tracking, THD, average switching frequency, semiconductor losses, and efficiency. SVPWM achieved lower THD, whereas DPWM reduced average switching frequency and switching losses, improving efficiency. C-HILS waveforms were then applied to a Foster thermal network to reconstruct the junction–temperature trajectory; $T_{\mathrm{j}}\left(t\right)$, and $\mathsf{\Delta }{T}_{\mathrm{j}}$ and $T_{\mathrm{j},\mathrm{m}\mathrm{i}\mathrm{n}}$ were mapped to lifetime using the Bayerer model. For a representative cyclic mission, $\mathsf{\Delta }{T}_{\mathrm{j}}$ decreased from approximately $25.6 °\mathrm{C}$ with SVPWM to about $17.5 °\mathrm{C}$ with DPWM, increasing the estimated lifetime from approximately 1.36 years to 9.14 years. These results demonstrate that the proposed C-HILS framework provides a unified pre-prototype tool for model verification, control strategy comparison, and quantitative thermal reliability assessment of shipboard propulsion inverters. Full article

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

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

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