Search Results (2,297)

Mobile robots and autonomous vehicles require tightly regulated direct current (DC) power under rapidly varying load conditions, motivating control strategies that combine fast nonlinear regulation with predictive optimization. This paper proposes a Hybrid Sliding Mode Control and Model Predictive Control (Hybrid SMC + MPC) strategy for a DC-DC buck converter supplying a representative mobile-robot mission load. The controller employs a cascade SMC structure for fast inner-loop regulation and an MPC component that provides finite-horizon duty-cycle correction using planned load information. The MPC problem is formulated in condensed form and solved analytically without an external optimization solver. A Lyapunov-based analysis establishes a sufficient reaching condition for the sliding variable under the ideal averaged-model assumptions, and the condition is verified for the simulated mission. The proposed approach is evaluated in MATLAB using a 10-phase, 10 s load profile with resistance varying from 7 Ω to 100 Ω and is compared with SMC-only, MPC-only, PID, constant-duty, and reconstructed fuzzy-logic benchmarks. In the averaged-model study, the Hybrid SMC + MPC achieves a maximum absolute voltage deviation of 0.388 V, an RMSE of 0.0115 V, and a final-phase mean absolute error of 0.0076 V. It provides the lowest maximum voltage deviation among the principal closed-loop controllers, while PID achieves the lowest RMSE and final-phase error and SMC-only exhibits the shortest mean settling time. Relative to MPC-only, the Hybrid controller reduces the maximum voltage deviation by approximately 43.6% and the mean settling time by approximately 66.1%. An ablation study shows that the MPC contribution substantially improves overall and steady-state regulation accuracy, while load preview primarily reduces the worst-case voltage deviation. Switching-level MATLAB/Simulink validation with explicit 20 kHz PWM and converter parasitics confirms that the output remains within ±2% of the 25 V reference throughout the complete mission, with a maximum absolute deviation of 0.443 V and a maximum steady-state switching ripple of 21.6 mV peak-to-peak. These results demonstrate that the proposed Hybrid SMC + MPC architecture provides a favorable balance between worst-case transient regulation, steady-state accuracy, and predictive control capability for dynamically varying robotic power loads. Full article

Photovoltaic converters operate under varying climatic conditions and non-ideal grid regimes, but factor importance is often assessed either through isolated local metrics or through pooled operating data that hide regime shifts and interaction effects. This study develops a hierarchical framework for sensitivity analysis of operating profiles of grid-connected PV converters under climatic and grid uncertainty. A compact operating-profile formulation is introduced that relates solar radiation, cell and ambient temperature, grid voltage, load, and selected design/control parameters to active power, efficiency, power factor, harmonic distortion, DC bus ripple, clipping behavior, and thermal headroom. The proposed workflow combines local normalized sensitivities for fast ranking around nominal conditions, Morris screening for factor reduction, and Sobol/Saltelli variance-based indices for global prioritization under uncertainty. The framework is demonstrated on a 100 kW synthetic reduced-order benchmark representing a three-phase two-level grid-connected PV inverter with an LCL filter. To clarify the scope of validity, the reduced-order model is cross-checked against switching-level simulations for representative nominal, clipping-prone, high-temperature and grid-stress operating windows. The results show that factor importance is not universal, but depends on the selected KPI, operating regime and uncertainty scenario. In the considered benchmark, grid voltage, cell temperature and equivalent thermal resistance are the dominant total-effect contributors, while the strongest second-order contribution appears between grid voltage and filter inductance under grid-stress conditions. The proposed framework is therefore intended as a reproducible, regime-aware sensitivity workflow rather than as a universal ranking of PV converter parameters. Full article

Conventional flux-reversal permanent magnet motors have problems such as excessive torque ripple and rich harmonic content in direct drive applications such as oil exploration, which restrict their application in high-precision scenarios. To address this issue, this paper presents a hybrid excitation topology that integrates double-armature windings, stator Halbach hybrid permanent magnet arrays, rotor-staggered unequal-tooth and rotor-hybrid permanent magnets. Two-dimensional finite element analysis was conducted using ANSYS Maxwell 2023 R1 to evaluate electromagnetic performance under rated steady-state conditions, rated power 300 kW, rated speed 83 rpm, rated voltage 660 V, rated phase current 307 A, and axial core length 200 mm. The simulation results show that the proposed topology has an average output torque of 34.5 kN·m at rated conditions compared with the traditional flux-to-reverse permanent magnet motor of the same size, and the torque ripple rate is reduced from 27.5% to 17.4%, a relative reduction of 36.8%. The results are based only on numerical simulation and have not been verified by physical prototype experiments. Dynamic control strategies, multi-load transient responses and experimental verification will be carried out in subsequent work. Full article

With the increasing integration of distributed photovoltaics, energy storage systems, electric vehicle chargers, and intelligent terminals, accurate identification of heterogeneous edge-end devices in distribution networks has become challenging due to their diverse operating characteristics and similar current signatures. This paper proposes an identification method based on current time–frequency features and Random Forest. Equivalent grid-connected current models are developed for five types of edge-end devices, considering different capacity levels, operating states, ripple characteristics, and transient behaviors. A 13-dimensional feature set is extracted from time-domain, frequency-domain, and time–frequency characteristics, covering 11 device subclasses. Feature analysis is conducted to evaluate the separability of the extracted features, and Random Forest is employed for multi-class device identification. The results show that the proposed method achieves an overall accuracy above 98% on independent test samples and 96.91% in the IEEE 33-bus validation case, demonstrating its effectiveness for distribution-network edge-end device identification. Full article

Off-highway vehicle electrification requires traction motors combining high torque density with reliable performance across demanding duty cycles, yet finite-element-based optimization remains computationally demanding for broad design-space exploration. This study addresses the gap with a sensitivity-analysis-based, surrogate-assisted multi-objective optimization framework for a 12-pole/72-slot, 120 kW Interior Permanent-Magnet Synchronous Motor (IPMSM) for a compact wheel-loader drivetrain, coupling Ansys Motor-CAD with Ansys OptiSLang. A Latin Hypercube sensitivity study of thirteen geometric parameters identifies the dominant design drivers, and an evolutionary algorithm operating on the validated surrogate produces a Pareto-optimal set, from which the final design is selected using the CRITIC–TOPSIS method applied to finite-element-validated feasible designs. Relative to the baseline, the validated performance shows a 7.4% increase in continuous torque, a 4.0% increase in peak torque, a 4.0% increase in efficiency, and a 53.5% reduction in torque ripple, with mass essentially unchanged, while also revealing that surrogate predictions were markedly optimistic relative to the finite-element results. These findings demonstrate an efficient, reliable route to high-performance IPMSM design for off-highway applications. Full article

This study presents an experimental validation of an adaptive fuzzy logic controller (AFLC) for solar-driven proton exchange membrane (PEM) hydrogen production under dynamic operating conditions. The proposed solar-driven PEM hydrogen production system was accurately modeled and investigated under various operating conditions using the MATLAB/Simulink simulation platform. In order to evaluate the effectiveness of the proposed controller, the AFLC strategy was experimentally investigated using a dSPACE DS1104 platform and compared with conventional Proportional–Integral (PI) and Fuzzy Logic Controller (FLC) approaches in terms of tracking accuracy, transient response, overshoot suppression, current ripple minimization, and overall hydrogen production stability. Under step-change solar irradiance conditions, the proposed AFLC exhibited superior MPPT performance, achieving improvements of 34.32% and 84.26% in power-tracking accuracy compared with the conventional FLC and PI controllers, respectively. The developed control architecture employs real-time feedback from electrolysis to regulate the duty cycle of the DC–DC converter controlled by maximum power point tracking (MPPT), ensuring precise power delivery to the PEM electrolysis despite fluctuating solar irradiance. Additionally, the solar hydrogen production system’s performance indices were computed. These indices are intended to assess the viability of the AFLC in comparison to the PI and FLC under the same solar irradiance conditions. Full article

Nominal launch-power profiles can lose quality-of-transmission (QoT) margin when span and equipment parameters vary. We study this effect using C-band GNPy 2.14.1 simulations that recompute amplified-spontaneous-emission (ASE) noise and Gaussian-noise (GN)-model nonlinear interference under perturbations. Ten runs use 384 training scenarios and 1024 intensified-stress scenarios with scalar and spectral multipliers of 1.25 and 1.50. In paired within-GNPy comparisons, a finite-sample 5% lower-tail-mean selector, defined as the mean of the 20 worst training utilities, improves fifth-percentile minimum-channel generalized signal-to-noise-ratio (GSNR) margin over nominal optimization by 0.247 dB, with a 95% confidence-interval half-width of 0.014 dB. After normalization to the nominal total launch power, the gain remains 0.179 dB (half-width 0.017 dB), suggesting that spectral shape is a major contributor to the paired difference in this comparison. The gain lies between 0.245 and 0.248 dB when the training-tail fraction varies from 1% to 10%; relaxing the per-channel ceiling from 3.0 to 3.5 dBm removes almost all active bounds while retaining a 0.246 dB gain. Selected profiles mainly raise the low-frequency edge, and the benefit appears near the modeled reach boundary rather than on high-margin metro links. Erbium-doped fiber amplifier noise figure, gain ripple, and reconfigurable optical add-drop multiplexer equalization lead the sensitivity ranking. Reduced Manakov checks preserve power ordering while exposing model offsets. The results describe the specified GNPy configuration, finite search, and synthetic perturbation laws; field-calibrated performance remains to be established. Full article

This paper presents a collaborative second-order voltage ripple suppression control method for single-phase power factor correction (PFC) converters. First, the causes of second-order DC output voltage ripples are analyzed. Then a modified current preprocessing mechanism is presented for the PFC in order to elevate valley power and suppress peak power, which is able to reduce the second-order power fluctuations. Parameter design and constraints are further provided to obtain a reasonable trade-off between current distortion and the suppressing effect. Meanwhile, an active power decoupling (APD) circuit reusing a capacitor half-bridge is also embedded into the PFC converter. For the APD circuit, this paper calculates and determines the capacitor voltage reference for the purpose of absorbing second-order power fluctuations. Moreover, a closed-loop control strategy for APD is presented to achieve tracking of the capacitor voltage. A coordinate control algorithm is introduced to monitor the cooperative relationship between PFC and APD control. Finally, a 400 W PFC prototype is constructed, and experimental tests are then carried out for verification. The results obtained based on the prototype can confirm the feasibility of the control strategy and the correctness of the theoretical analysis discussed in this paper. Full article

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article

Induction motors remain the workhorse of modern industry thanks to their robustness, cost effectiveness and high efficiency, but the growing demands of electrified transport, high-performance automation and Industry 4.0 impose increasingly stringent control requirements. This paper presents an integrated, application-oriented survey of control strategies for high-performance induction motor drives, covering classic scalar V/f control as a baseline and advanced field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC), nonlinear/robust schemes and intelligent/data-driven and digital twin-assisted solutions. The methods are analyzed within a unified framework in terms of dynamic response, torque and flux ripple, current harmonic distortion, efficiency, robustness, implementation complexity and suitability for sensorless and fault-tolerant operation. Emphasis is placed on hybrid strategies that combine classical vector or DTC structures with MPC, fuzzy and neuro-fuzzy logic, neural network-based observers, reinforcement learning and digital twin-enabled monitoring to reconcile fast dynamics with high efficiency, low ripple and lifecycle reliability. Consolidated comparison tables and a hybrid control map highlight typical performance trends, trade-offs between simplicity and performance, and the complementary roles of AI and digital twins as system-level enablers. The survey also outlines promising research directions toward systematically designed hybrid controllers, lightweight digital twins for embedded platforms and experimentally validated benchmarks that can accelerate the industrial uptake of next-generation induction motor drives. Full article

Proton exchange membrane fuel cell (PEMFC) systems typically generate low-voltage and high-current DC power, requiring a step-up converter interface for electric vehicle (EV) and DC microgrid applications. In addition, excessive current ripple and rapid transient loading conditions may increase electrical and thermal stress within the fuel cell stack. Consequently, both converter topology and control strategy play important roles in maintaining stable system operation and favorable PEMFC operating conditions. This paper presents a differential flatness-based nonlinear control strategy for a PEMFC-fed multiphase interleaved boost converter. The proposed control structure combines inner-loop inductor current regulation with outer-loop DC bus energy regulation. This configuration achieves stable voltage control, balanced phase-current sharing, and reduced fuel cell current ripple during transient operating conditions. A two-phase interleaved boost converter prototype was experimentally implemented using a 2.5 kW PEMFC platform and a dSPACE DS1202 MicroLabBox real-time controller. Experimental tests under steady-state and dynamic loading conditions were conducted to evaluate DC bus voltage regulation, transient response, current-sharing capability, and robustness against load disturbances. The experimental results demonstrated that the proposed nonlinear controller achieved faster transient voltage recovery and smaller DC bus voltage deviation compared with a conventional PI-based control approach. In addition, the interleaved converter structure reduced input current ripple at the PEMFC output terminals during dynamic operation. Overall, the results indicate that the proposed control strategy is suitable for PEMFC-powered EV and DC microgrid applications requiring stable DC bus regulation and fast dynamic power control. Experimental results demonstrate that the proposed controller reduces the DC bus voltage recovery time from approximately 150 ms to 50 ms, corresponding to a 66.7% improvement over a conventionally tuned PI controller. In addition, the maximum DC bus voltage deviation is reduced from approximately 1.0 V to 0.5 V while maintaining balanced phase-current sharing with less than 3% mismatch throughout the tested operating conditions. Full article

Sensorless speed estimation replaces fragile shaft encoders in cost-sensitive Permanent Magnet Direct Current (PMDC) motor drives, but classical model-based observers degrade under brush friction, commutation ripple, and thermal drift, while purely data-driven estimators sacrifice physical interpretability. This paper presents a Hybrid Physics-Data-Driven Observer (HPDDO) that couples an identified lumped-parameter electrical model with a compact multilayer-perceptron residual compensator, which is executed in real time on a low-cost ESP8266 microcontroller. Global parameters are identified from a short labeled recording, after which the network corrects only the nonlinear residual that the physics model cannot explain. Under a strictly time-series-aware evaluation (chronological 80/20 split), the proposed estimator achieves an average root mean square error (RMSE) of 4.11 RPM across dynamic PWM sweeps, abrupt load transitions, and a long-duration thermal-drift test, outperforming an extended Kalman filter (9.49 RPM), a sliding mode observer (10.89 RPM), and a pure neural-network estimator (7.14 RPM) implemented on the identical dataset. An ablation study shows that accuracy is insensitive to network size, with a 0.9 kB variant matching the deployed model, and a residual-clamping safeguard bounds the estimation error under unseen operating conditions. The framework provides an accurate, interpretable, and computationally lightweight solution for industrial PMDC drives without dedicated speed sensors. Full article

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF, or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner that re-estimates the gains from a frequency response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action that the auto-tuner cannot provide during its estimation interval. The novelty of this work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA)-tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and a consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work. Full article

Electric tractor traction motors require stable torque under low-speed, heavy-load operation. This study investigates a 40 kW, 3000 r/min V-shaped interior permanent magnet synchronous motor (IPMSM) and develops a rotor structure optimization procedure integrating Maxwell finite element analysis, Latin hypercube sampling, sensitivity screening, and the non-dominated sorting genetic algorithm II (NSGA-II). The magnetic bridge thickness (HRib) and permanent magnet thickness (ThickMag) were ranked highest by the linear screening and retained for the reduced two-variable refinement and set to 1.92 and 5.36 mm, respectively. The optimized design maintained the average electromagnetic torque at 119.40 N·m, while the peak-to-peak cogging torque decreased from 5.94 to 1.66 N·m and the loaded torque ripple coefficient decreased from 17.46% to 2.90%. Prototype tests yielded a peak-to-peak cogging torque of 1.85 N·m, 11.45% above the optimized finite element result. At 800 r/min and approximately 120 N·m, the measured average shaft torque, peak-to-peak torque, and ripple coefficient were 118.50 N·m, 5.83 N·m, and 4.92%, respectively. The larger experimental ripple is consistent with combined electromagnetic, control, measurement, and drivetrain effects absent from the electromagnetic model; their individual contributions are not identified by the present data, so the loaded result is interpreted only as trend-level evidence at the tested point. Full article

The model predictive current control (MPCC) of an interior permanent magnet synchronous machine (IPMSM) requires an accurate motor parameter model to predict future currents and achieve high control performance. However, the inductance parameters of an IPMSM are easily affected by factors such as magnetic field saturation, leading to large current prediction errors, high current ripple, and poor stability. Therefore, an MPCC strategy for an IPMSM based on parameter adaptive feedback correction is proposed. First, based on the mathematical model of the IPMSM in the synchronous rotary coordinate, the cross-coupling relationship between the dq-axis inductance deviations and the current prediction error is derived to form an explicit prediction error model. Then, the influence of the d-axis and q-axis inductance parameter deviations of the IPMSM on the current prediction error is discussed in detail. Next, based on the established mathematical model of the prediction error, the recursive least squares scheme is adopted to identify the d-axis and q-axis deviations of the inductance parameters online. Finally, unlike conventional open-loop RLS correction, a PI-based closed-loop correction loop is designed that feeds the prediction error back to adjust the inductance deviations, thereby forcing the prediction error toward zero while inherently compensating for inverter dead-time effects. Simulations and experiments were conducted, and the results show that the proposed scheme greatly improves the accuracy of current prediction and inductance parameter estimation, and enhances robustness against parameter mismatch and dead-time disturbances. The key novelty lies in the PI-feedback-driven RLS closed-loop structure that simultaneously achieves error elimination and dead-time compensation. Full article