Search Results (606)

Bidirectional electric vehicle (EV) chargers must regulate current and voltage accurately in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes while respecting the tight real-time computational budget of embedded hardware. This paper compares three controllers, active disturbance rejection control (ADRC), a linear model predictive controller (MPC), and a reduced-order quadratic-programming predictive variant (rMPC), for a bidirectional charger supplying a 96-cell nickel–manganese–cobalt battery modeled as a second-order equivalent circuit. The gains of all three controllers are tuned systematically with particle swarm optimization (PSO) and a genetic algorithm (GA) against a weighted objective covering current tracking, terminal-voltage error, disturbance recovery, and control effort. Each controller is evaluated over a constant-current/taper charging profile, a V2G discharge, and injected load disturbances, with robustness quantified by a 500-run Monte Carlo sweep of inductance, resistance, and temperature. After tuning, every controller meets the 5% normalized-error target across all phases, with constant-current tracking error below 2%. ADRC’s mean per-step solve time (∼14.5 $\mathrm{\mu }$s) is 19–30× smaller than that of the predictive controllers. A 20-seed statistical comparison at equal budget shows PSO and GA to be statistically equivalent tuners, and the decisive practical distinction among three comparably accurate and robust controllers is computational: ADRC offers bounded, negligible worst-case timing, while rMPC and MPC offer marginally faster disturbance recovery at a far higher and less predictable solve cost that exceeds the real-time budget at the 99th percentile. Full article

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

The surface of nodular cast iron is susceptible to wear failure under high-load conditions. In this study, Fe-Cr-Ni wear-resistant coatings were developed on the surface of nodular cast iron using laser cladding technology. The influence of laser power (P), powder feeding rate (F), scanning speed (V), and overlapping rate (φ) on the microhardness and dilution rate of the coatings was analyzed by response surface methodology, while the model’s accuracy was evaluated through analysis of variance. Subsequently, the multi-objective particle swarm optimization algorithm was utilized to identify the optimal process parameters (P = 1350 W, V = 12.5 mm/s, F = 9 g/min, and φ = 45%) based on non-destructive testing results. The predictive model values closely matched the experimental results. The average microhardness of the Fe-Cr-Ni cladding layer was 620.3 HV, which was 2.8 times that of the nodular cast iron substrate. Importantly, the laser cladding layer demonstrated a significant improvement in wear resistance compared to the substrate. The wear mechanisms for the coating predominantly involved mild abrasive wear and adhesive wear, while the substrate primarily experienced severe adhesive wear. This study offers valuable insights for optimizing laser cladding process parameters for nodular cast iron. Full article

Partial shading conditions (PSCs) in photovoltaic (PV) systems generate multiple local maximum power points (LMPPs) and a single global maximum power point (GMPP) in the power–voltage (P–V) characteristics, challenging conventional maximum power point tracking (MPPT) methods. This study presents an FPGA-in-the-Loop (FIL) co-simulation of a Systematic-Sequencing Adaptive Particle Swarm Optimization (SS-APSO) algorithm for MPPT under dynamically varying shading conditions. The proposed method combines deterministic particle initialization, adaptive particle reordering, and switching among wide exploration, re-exploration and exploitation modes to enhance global search capability. The controller is implemented on a Xilinx Artix-7 FPGA using fixed-point arithmetic and a finite-state-machine architecture in VHDL and is evaluated through MATLAB/Simulink–FIL co-simulation for two PV configurations: four series-connected modules (4S) and two parallel-connected strings of two series modules (2S2P). The results demonstrate tracking efficiencies generally exceeding 98% under different shading within 0.181 s for both configurations, while in FIL co-simulation, it reaches the GMPP within 0.203. The close agreement between simulation and FIL co-simulation results demonstrates the effectiveness of the proposed SS-APSO-MPPT controller for PV systems. Full article

The ICARUS experiment is part of the Short-Baseline Neutrino program at Fermilab and aims to search for the possible existence of sterile neutrinos in the O (1 eV) mass range, addressing anomalies observed by the LSND and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber that provides high-resolution three-dimensional imaging and precise calorimetric measurements of ionizing particles. This technology enables detailed studies of neutrino interactions over a wide energy range, from a few $\mathrm{keV}$ to several hundred $\mathrm{GeV}$. Event reconstruction relies on a software framework that applies pattern recognition algorithms to transform raw detector signals into fully reconstructed interaction topologies, including vertices, particle tracks, and electromagnetic showers. In some cases, however, a single particle track may be incorrectly split into multiple segments, leading to underestimated energy reconstruction and potential failures in particle identification. Since these effects can result in the loss of otherwise valid events, we designed a stitching algorithm that identifies the broken tracks and reconnects (“stitches”) the segments. This approach improves track reconstruction quality, energy estimation and overall event reconstruction efficiency. Full article

To address the challenge of fatigue life prediction caused by multiple-crack interactions in ship and offshore structures, this study proposes a dynamic Bayesian network (DBN)-based method for predicting the fatigue life of structures with multiple cracks, which is systematically validated through physical experiments. First, a numerical model of a representative structure containing a central hole and multiple initial cracks was established based on the coupled simulation platform of ABAQUS and Franc3D. The nonlinear interaction behavior among multiple cracks under different geometric configurations was systematically investigated. Subsequently, a neural network surrogate model was developed, in which geometric features and crack lengths were employed as inputs and key fracture mechanics parameters were taken as outputs, enabling efficient prediction of complex stress intensity factor (SIF) fields. On this basis, fatigue crack growth experiments were conducted on DH36 high-strength steel specimens containing multiple cracks, and crack evolution data under realistic cyclic loading conditions were obtained. Finally, by coupling the surrogate model with the Paris law as the state transition equation and incorporating sparse experimental observations as dynamic updating information, a dynamic Bayesian network framework based on the particle filtering algorithm was established. This framework enables posterior probability tracking of multiple-crack fatigue states and rolling prediction of the remaining fatigue life. The results demonstrate that the proposed method can effectively mitigate the error accumulation associated with deterministic simulation models during long-term open-loop prediction while relying only on a limited number of discrete observation anchors. Consequently, the prediction accuracy of the fatigue life of multiple-crack systems is significantly improved. Furthermore, under crack co-propagation conditions, the proposed framework exhibits a strong capability to capture the propagation retardation of secondary cracks induced by shielding effects. The proposed method provides a theoretical foundation and technical support for the dynamic assessment of fatigue damage and the development of digital twins for complex structures containing multiple cracks. Full article

Retired lithium-ion batteries retain considerable residual value for second-life energy storage applications, but significant variations in health conditions and complex operating scenarios make accurate remaining useful life (RUL) prediction challenging. To address the limited availability of capacity measurements and the poor adaptability of conventional models to dynamically fluctuating degradation trajectories, a hybrid RUL prediction framework integrating Wavelet Packet Energy Entropy (WPEE), a Fractional-Order Grey Model (FGM), and an Unscented Kalman Filter (UKF) is proposed. WPEE extracted from discharge voltage signals is employed as a degradation indicator, while a Box–Cox transformation enhances its correlation with capacity. An Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm is used to determine the optimal fractional-order parameter, and the optimized FGM is incorporated into the UKF state-transition process for recursive state correction. Validation was conducted using four retired lithium-ion cells and two series-connected battery packs with different health conditions at prediction starting points of 20, 25, and 30 cycles. The results show that the proposed method effectively tracks degradation evolution, with RUL prediction errors within 7 cycles for retired cells and within 6 cycles for battery packs. Across all 18 prediction cases, FGM–UKF achieved an overall mean AE of 3.111 cycles, lower than those of FGM (5.111 cycles), GM(1, 1) (4.000 cycles), and LR (4.278 cycles). These results demonstrate the effectiveness and robustness of the proposed framework for lifetime assessment in second-life battery energy storage systems. Full article

Electro-hydraulic actuator systems are widely used in precision motion-control applications; however, their displacement regulation remains challenging because fast response, low overshoot, and high steady-state accuracy must be achieved simultaneously under strongly dynamic operating conditions. In this study, a proportional-integral-derivative (PID) controller augmented with a Gompertz function (PID-G) is proposed for the position control of a four-way valve-controlled linear actuator, and its parameters are tuned by the recently introduced dholes-inspired optimizer (DIO). First, a control-oriented mathematical model of the electro-hydraulic actuator system is established by combining the valve and actuator dynamics. Then, the PID-G structure is formulated by incorporating a nonlinear Gompertz-based term into the conventional PID framework, and the resulting seven-parameter tuning problem is cast as an optimization task using a composite objective function that accounts for overshoot, steady-state error, rise time, and settling time. The effectiveness of DIO is evaluated comparatively against flood algorithm (FLA), covariance matrix adaptation evolution strategy (CMA-ES), and particle swarm optimization (PSO) under identical simulation conditions. The results show that DIO provides the best optimization performance, yielding the lowest best, average, and standard-deviation values of the objective function among the compared algorithms. In the time domain, the DIO-based PID-G controller achieves the most favorable overall response with a rise time of 0.079511 s, a settling time of 0.099326 s, an overshoot of 0.15110%, and a steady-state error of 0.089317%. The superiority of the DIO-based design is further confirmed by lower values of error based performance metrics (IAE, ISE, ITAE, and ITSE), improved convergence characteristics, and statistically significant advantages in the Wilcoxon test. Additional comparisons with different (PI, PID, 2DOF-PID, and FOPID) controllers also demonstrate that the proposed PID-G structure provides markedly better transient and error-based performance when tuned by DIO. Frequency-domain and varying-setpoint results further indicate satisfactory stability margins, robust tracking ability, and bounded control effort. Overall, the study shows that combining DIO with a Gompertz-augmented PID structure constitutes an effective strategy for high-performance electro-hydraulic actuator displacement control. Full article

Accurate and robust localization remains the fundamental bottleneck for truly autonomous robotic systems, despite decades of progress in probabilistic estimation and SLAM. This paper provides a critical and comprehensive review of mobile robot localization across sensing modalities, estimation paradigms, and deployment domains, covering ground, aerial, and underwater platforms. Beyond a descriptive survey, we explicitly analyze the limitations and trade-offs of existing approaches. We introduce an updated taxonomy that spans classical proprioceptive and exteroceptive sensors, emerging technologies such as 4D imaging radar and event cameras, and infrastructure-based positioning systems including GNSS and Ultra-Wideband. We revisit the evolution of localization algorithms, from Bayesian filtering techniques (EKF, UKF, and particle filters) to modern graph-based SLAM frameworks and tightly coupled multi-sensor fusion systems. Particular emphasis is placed on the recent paradigm shift toward learning-based and neural implicit approaches, including NeRF-SLAM and Gaussian Splatting, highlighting both their transformative potential and their current impracticality for real-time deployment. Unlike previous surveys, this work provides a unified cross-domain perspective while critically examining scalability, robustness, computational cost, and real-world deployability. We identify key unresolved challenges, including long-term consistency, operation in degraded environments, and the integration of semantic understanding into localization pipelines. Furthermore, we propose standardizing evaluation metrics with a formal Trajectory Completeness formulation to expose tracking brittleness. Finally, we outline future research directions toward resilient, certifiable, and truly autonomous localization systems, emphasizing the critical transition from passive estimation to Active SLAM in unstructured environments. Full article

This paper presents a self-tuning minimal-rule fuzzy logic controller for high-performance induction motor drives operating under field-oriented control. Unlike conventional full-rule fuzzy controllers and reduced-rule designs with fixed post-design scaling, the proposed method combines a fixed nine-rule Mamdani inference structure with a bounded online output gain adaptation mechanism. The nominal gain and adaptation sensitivity are determined offline using Particle Swarm Optimization, thereby retaining operating-condition responsiveness without requiring online optimization, rule reconstruction, or membership-function retuning. The closed-loop behavior is analyzed using a discrete-time Lyapunov framework derived from the induction motor mechanical dynamics under bounded disturbances. The controller is evaluated through fixed-step simulations incorporating measurement noise, 12-bit signal quantization, and a one-sample computational delay. Comparative results against a conventional PI controller and a classical 49-rule fuzzy controller show that the proposed scheme achieves a rise time of 0.15 s, a settling time of 0.26 s, a post-transient mean absolute tracking error of 4 RPM, negligible overshoot, and a torque ripple of approximately 0.44 Nm. Relative to the classical 49-rule FLC, the proposed design reduces the maximum number of fuzzy-rule evaluations per control update from 49 to 9, corresponding to an 81.6% reduction in structural fuzzy-inference complexity. The results indicate a favorable simulation-level trade-off between dynamic performance, disturbance rejection, and structural algorithmic simplicity. Generated-code SIL, Hardware-in-the-Loop testing, target-processor timing measurements, and experimental implementation remain necessary to establish practical embedded feasibility. Full article

Magnetic particle imaging (MPI), as an emerging radiation-free, high-sensitivity molecular imaging technique, holds broad application prospects in fields such as medical diagnosis, angiography, and targeted drug tracking. However, traditional three-dimensional MPI reconstruction algorithms face a problem in balancing reconstruction speed and image resolution. A hybrid reconstruction algorithm (Full Hybrid) based on spatial density constraints and residual iterative optimization is proposed in this work. This paper simulates Lissajous trajectory scanning and the non-linear response of magnetic particles based on the three-dimensional MPI simulation framework. The proposed hybrid method first utilizes the X-space method to obtain a basic spatial prior, then introduces field-free point (FFP) trajectory density to impose spatial weighting constraints on the reconstructed image. Experimental results demonstrated that this hybrid algorithm performs better in the reconstruction of complex three-dimensional topological structures (an H-shaped phantom). Comprehensive evaluation demonstrated that the reconstructed outputs reach a peak signal-to-noise ratio (PSNR) of 12.85 dB, a structural similarity index measure (SSIM) of 0.7321, and a root mean square error (RMSE) of 0.2278. Ablation experiments and comparison experiments further reinforced the advantages of the proposed method. These results demonstrate the numerical feasibility of the proposed reconstruction method for a three-dimensional phantom and provide a basis for further evaluation under multiple simulation conditions and real-scanner measurements. Full article

Urban service robots require route planners that are not only collision-free but also consistent with the energetic behavior of differential drive locomotion. Conventional grid planners such as A* are efficient and reliable for geometric navigation, but their usual cost structure prioritizes path length and does not explicitly account for heading changes, concentrated turns, or localization risk near obstacles. This study presents an energy-aware route-planning formulation for differential-drive mobile robots operating in dense polygonal urban environments. The path is encoded through twelve continuous internal waypoints and is evaluated using an interpretable energy proxy that combines translational distance, cumulative absolute rotation, squared rotation, and a clearance-dependent localization risk term. Collision avoidance, boundary compliance, and maximum turn feasibility are handled through a feasibility-first dominance rule, and the resulting constrained problem is solved using a Genetic Algorithm and Particle Swarm Optimization. A* with clearance inflated occupancy grids is included as a deterministic baseline. The final experiments used a dense urban scenario with sixteen polygonal obstacles, an A* grid resolution of 0.10 m, a robot radius of 0.20 m, a safety clearance of 0.10 m, 80 GA individuals, 80 PSO particles, 100 iterations, and 10 independent runs. All methods achieved a 100% feasibility rate. PSO obtained the lowest average energy proxy, 18.505, compared with 18.645 for A* and 18.645 for GA, and reduced average rotation from 4.136 rad to 4.079 rad. However, A* remained much faster, 5.860 s on average, compared with 174.574 s for GA and 175.869 s for PSO. The ranking analysis shows that GA produced the best aggregate score when route quality, time, clearance, and feasibility were weighted equally, while PSO produced the best route quality. External perturbation tests indicate that open-loop execution in narrow corridors is sensitive to bias and waypoint noise, which motivates closed-loop tracking, online replanning, and physical validation in future work. Full article

The advancing perception capabilities of an individual unmanned underwater vehicle (UUV) pose new challenges for multi-target perceptual consistency in underwater bearing-only tracking (BOT) systems. Accurate target state estimation necessitates two key prerequisites: sensor bias compensation and precise data association. The biases encompass both sensor spatial bias and signal propagation delay between the target and sensor. This paper introduces a measurement model that explicitly accounts for these factors. To address the lack of prior target information, an initial target state estimation algorithm is developed based on maximum likelihood estimation (MLE), with a refined bio-inspired variant incorporating particle swarm optimization (PSO). To this end, a cost function is formulated to transform the BOT data association problem into an assignment problem. Thereafter, an iterative multi-target data association (MDA) algorithm, integrated with the expectation-maximization (EM) method, is designed to jointly mitigate the effects of signal delay and spatial bias. Monte Carlo simulation scenarios validate the overall effectiveness of the proposed MDA framework. Specifically, the EM-based spatial bias estimation method demonstrates accurate bias estimation capability. Full article

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments. Full article

Partial shading is one of the main factors that degrade the output performance and operational reliability of photovoltaic (PV) arrays. It not only causes power loss and multi-peak P–V characteristics, but also induces current mismatch, reverse bias, and local hotspot formation. In this study, an electro-thermal PV module model under partial shading conditions is developed and validated, and an improved sparrow search algorithm (ISSA) is proposed for maximum power point tracking (MPPT) of PV arrays under static and dynamic complex operating conditions. The electrical model is established based on the single-diode model with irradiance, temperature, and Bishop reverse bias corrections, while the thermal model considers solar absorption, heat generation, convection, radiation, and heat conduction. The coupled model is validated against published experimental and numerical results. The predicted peak hotspot temperature is 111.9 °C, corresponding to a relative error of 2.7%; the average absolute errors of current and voltage are 0.20–0.25 A and approximately 0.3 V, respectively, and the maximum relative error of peak temperature is 3.7%. Based on the validated model, a MATLAB/Simulink MPPT platform is constructed to compare particle swarm optimization (PSO), the standard sparrow search algorithm (SSA), and the proposed ISSA. The results show that SSA achieves better global tracking performance than PSO under severe partial shading and dynamic irradiance transitions. Furthermore, by introducing Tent chaotic initialization and random walk perturbation, ISSA significantly improves the convergence speed and reduces steady-state power fluctuation while maintaining high tracking efficiency. Under static shading conditions, ISSA reduces the convergence time from 0.44 s to 0.25 s, 0.24 s to 0.15 s, and 0.44 s to 0.26 s for light, moderate, and severe shading cases, respectively. Under dynamic conditions, ISSA also shortens the post-transition convergence time and suppresses output power oscillation. These results demonstrate that the proposed ISSA-based MPPT method is suitable for PV arrays operating under partial shading and dynamic weather conditions. Full article