Search Results (635)

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. 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

High penetration of wind farms into the power grid lowers system inertia and compromises stability. This paper proposes a grid-forming control strategy for Permanent Magnet Synchronous Generator (PMSG) wind turbines based on DC-link voltage matching and virtual inertia. First, a relationship between grid frequency and DC-link voltage is established, replacing the need for a phase-locked loop. Then, DC voltage dynamics are utilized to trigger a real-time switching of the power tracking curve, releasing the rotor’s kinetic energy for inertia response. This is further coordinated with a de-loading control that maintains active power reserves through over-speeding or pitch control. Finally, the MATLAB/Simulink simulation results and RT-LAB hardware-in-the-loop experiments demonstrate the capability of the proposed control strategy to provide rapid active power support during grid disturbances. 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 study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible experimental platforms, the system is designed to bridge theoretical concepts of power conversion and control with practical implementation. The proposed setup employs cascaded current and voltage control loops to achieve constant current (CC) and constant voltage (CV) charging modes, while its modular hardware architecture allows modification of key parameters such as inductance, capacitance, and circuit topology. The control algorithms are implemented on a microcontroller, and real-time data acquisition is integrated using the ThingSpeak platform for monitoring system behaviour. Experimental results show that the current control loop recovers to its reference value within approximately 6 ms under abrupt load variations, whereas the voltage control loop settles within approximately 15 ms, demonstrating stable closed-loop performance. In addition, the system successfully charges a 12 V lead-acid battery following a standard CC–CV charging profile. Overall, the proposed experiment kit provides an effective educational platform and a practical basis for further exploration of battery charging strategies and power converter control. Full article

The integration of embedded software in multi-sensor wearable devices has revolutionized real-time monitoring across health, fitness, industrial, and environmental applications. This paper presents a comprehensive approach to designing and implementing embedded software architectures that enable efficient, low-power, and high-accuracy data acquisition and processing from heterogeneous sensor arrays. We explore key challenges such as synchronization of sensor data streams, real-time operating system (RTOS) integration, power management strategies, and wireless communication protocols. The reviewed framework supports modular scalability, allowing for seamless incorporation of additional sensors or features without significant system overhead. Future research directions of the embedded software include Hardware-in-the-Loop and real-world validation, on-device machine learning and edge intelligence, adaptive sensor fusion, energy harvesting and power autonomy, enhanced wireless communications and security, standardization and interoperability, as well as user-centered design and personalization. By adopting this focus, we can highlight the potential of the embedded software to support proactive decision-making and user feedback through edge-level intelligence, paving the way for next-generation wearable monitoring systems. 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 regulation of output voltage in power converters often demands nonlinear control techniques; however, their implementation is challenging when deployed on low-cost hardware with limited computational resources. To address this difficulty, the modeling via the sector nonlinearity technique is adopted to represent the converter dynamics as a convex combination of linear vertex models. Building on this representation, this article proposes a vertex-based convex PI controller that significantly reduces the required online computations compared to conventional convex controllers relying on full-state feedback. In the proposed scheme, the inductor current is used solely to evaluate the weighting functions, avoiding the need to compute control gains associated with this state. The effectiveness of the method is demonstrated through offline simulations and validated using hardware-in-the-loop experiments. Full article

Virtual synchronous generator (VSG) technology introduces synthetic rotational inertia and damping into inverter-based systems, thereby enhancing regulation performance under grid-connected operation. However, the output characteristics of VSGs are strongly influenced by virtual inertia and damping. This paper develops a self-tuning inertia–damping coordination mechanism for VSGs. The coupling between virtual inertia and damping with respect to grid power quality is systematically investigated, and a power-angle dynamic response model for synchronous generators (SGs) under extreme operating conditions is established. Building on these results, an improved adaptive control strategy for the VSG’s virtual inertia and damping is proposed. The proposed strategy detects changes in frequency and load power, enabling adaptive tuning of virtual inertia and damping in response to system variations, thereby reducing frequency overshoot while accelerating the dynamic response. The effectiveness of the proposed strategy is validated by hardware-in-the-loop real-time simulations. 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

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

To address the insufficient adaptability of virtual synchronous generators (VSGs) under traditional fixed-value damping control in multiple application scenarios and the lack of regulatory flexibility in transient damping control with a fixed cutoff frequency, a transient damping-type VSG control strategy with flexibly adjustable cutoff frequency is proposed. The aim is to break through the regulatory limitations of the fixed cutoff frequency, quantify the inverse coordination relationship between the cutoff frequency and the equivalent damping coefficient, establish a dynamic adjustment mechanism of the cutoff frequency based on the system natural oscillation frequency, damping ratio, and power grid parameters, and clarify the value range from 0 to ω_cmax as well as the real-time adaptation algorithm. First, the influence of damping on active power and frequency is analyzed through the VSG model. Second, combined with the characteristic analysis of different damping types, the advantages of transient damping in transient response capability under various operating conditions are derived. Furthermore, the role of the cutoff frequency in transient damping on output characteristics is specifically analyzed, a transient damping design method with flexibly adjustable cutoff frequency is proposed, and the value range of the cutoff frequency is calibrated. Finally, a hardware-in-the-loop experimental platform is established for experimental testing. The strategy effectively eliminates the output power deviation when the system frequency deviates, enhances the transient response capability of the VSG under different operating conditions, and exhibits superior output characteristics. 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

Flexible microscale neural interfaces are advancing current strategies for recording and modulating electrical activity in the brain and spinal cord. The aim of this review is to colligate recent progress in thin-film micro-electrocorticography (μECoG) systems and establish a framework for their translation toward spinal bioelectronic implants. We first outline substrate and electrode material design, ranging from polymeric and hydrogel-based materials to nanostructured conductive materials that enable high-fidelity recording on mechanically compliant platforms. We then summarize structural design rules for μECoG arrays, including electrode size, pitch, and channel scaling, and relate these to data-driven μECoG applications in brain–computer interfaces and closed-loop neuromodulation. Bidirectional μECoG architectures for simultaneous stimulation and recording are examined, with emphasis on safe charge injection, electrochemical and thermal limits, and state-of-the-art hardware and algorithmic strategies for stimulation-artifact suppression. Building upon these cortical technologies, we briefly describe adaptation to spinal interfaces, where anatomical constraints demand optimized mechanical properties. Finally, we discuss the convergence of flexible bioelectronics, wireless power and telemetry, and embedded AI decoding as a path toward autonomous, clinically translatable μECoG and spinal neuroprosthetic systems. Ultimately, by synthesizing these multidisciplinary advances, this review provides a strategic roadmap for overcoming current translational barriers and realizing the full clinical potential of soft bioelectronics. Full article