Search Results (262)

Electric vehicle (EV) wireless power transfer (WPT) systems face two fundamental challenges that hinder their widespread adoption: sensitivity to coil misalignment and the need for battery-friendly fast charging protocols. This paper presents a comprehensive and integrated framework that addresses both challenges through a three-pronged approach combining advanced coil geometry optimization, multi-stage current method (MSCM) charging, and reinforcement learning (RL)-based adaptive control. First, a memetic algorithm hybridizing global exploration and local refinement is employed to design coil geometries that are inherently resilient to misalignment. The optimized coils maintain strong magnetic coupling under lateral displacements up to ±75 mm by strategically sizing the secondary coil’s outer diameter to be smaller than the primary coil’s, ensuring it remains within the optimal magnetic flux region. Second, an MSCM charging protocol is developed and optimized with the objective of balancing charging speed against battery thermal stability and state of health (SoH). The proposed strategy determines optimal current levels for each charging stage, reducing temperature rise compared to conventional CC-CV charging. Third, a novel Deep Q-Network (DQN) RL agent is implemented for real-time adaptive control of the WPT system. The RL controller dynamically adjusts phase shift in response to varying coupling conditions, load disturbances, and battery state, outperforming traditional PI controllers with 23% faster settling time and improved efficiency under dynamic misalignment scenarios. Finite element analysis (FEA) simulations validate the electromagnetic performance of the optimized coils, while an experimental prototype demonstrates the integrated system’s performance. Results show that the combined approach achieves 91.2% DC-DC efficiency under nominal conditions and maintains over 83% efficiency under lateral misalignments up to ±75 mm, fully complying with SAE J2954 alignment tolerance requirements. The MSCM charging protocol, guided by the memetic algorithm, limits battery temperature rise during a full charge cycle, while the RL controller ensures stable power delivery under real-world dynamic conditions. This work establishes a new paradigm for holistic WPT system design, demonstrating that synergistic optimization of magnetic structures, charging protocols, and intelligent control can simultaneously achieve misalignment resilience, fast charging, and adaptive robustness. Full article

Sliding mode control (SMC) is widely used for permanent magnet synchronous motor (PMSM) drives because of its robustness against disturbances and parameter uncertainties. However, the reaching law still faces a trade-off between fast convergence and chattering suppression. Reaching laws based on the signum function usually provide rapid convergence but induce severe high-frequency oscillations, whereas smooth or exponential modifications may reduce discontinuity at the expense of slower transient response or excessively large control gains. To address these limitations, this paper proposes a novel sine-logarithmic reaching law combined with a power-rate term for PMSM sliding mode control. The proposed structure avoids both the conventional smooth and exponential approximations commonly used in the literature. A theoretical analysis is presented, and the convergence behavior is characterized in terms of practical finite-time stability. The effectiveness of the proposed reaching law is evaluated in MATLAB/Simulink through comparative PMSM simulation studies, including start-up, steady-state, load-disturbance, parameter-variation, and total harmonic distortion (THD) tests against representative reaching laws from the literature. The simulation results indicate that the proposed method provides a favorable trade-off among rapid reaching, chattering attenuation, steady-state accuracy, and harmonic performance. These findings suggest that the proposed reaching law is a competitive alternative for PMSM sliding mode control. Full article

When a virtual power plant (VPP) participates in energy and reserve markets, day-ahead bids, reserve commitments, and real-time delivery share the same internal resource constraints, while aggregate models cannot readily trace internal responsibility allocation or identify local shortfalls. This study develops a fixed-responsibility partitioning framework for day-ahead bidding and real-time delivery. Fixed membership mappings are screened under a unified budget; resource-type-specific reserve certificates and sustained-delivery constraints are embedded in a finite-support Wasserstein distributionally robust optimization (WDRO) model; and hierarchical model predictive control executes the market commitments. The 365-day evaluation retains three non-dominated candidates, $\mathrm{K}\in \{6,7,8\}$, whose mean operational-fitness objective values are reduced by 25.5%–43.1% relative to the corresponding independent spectral-clustering baselines. In a common 30-day ablation study, detailed partition responsibilities increase mean daily adjusted operating profit by CNY 6442–8162 and reduce settlement deviation by 1.19–1.23 MW relative to two equivalent-responsibility rules. Here, adjusted operating profit is profit net of balancing-correction cost, settlement deviation is the schedule–settlement difference, and partition tracking error is the deviation between partition targets and aggregated resource response. The directions of these effects remain consistent across two independent 10-day windows, and hierarchical real-time coordination further reduces settlement deviation and partition tracking error. Public-aggregate-data replays show that the profit and deviation effects of WDRO relative to sample average approximation (SAA) vary with market settings and node-downscaled inputs. As an engineering extension, bounded contracted-capability factors, defined as multipliers on market-committable capability, increase mean daily adjusted operating profit by 6.00% in the 2024 holdout set, and attribution analysis indicates that most of this gain is associated with the average contracted-capability factor and released fast-response headroom. Under the tested settings, fixed-responsibility partitioning enables the continuous transfer of resource-type-specific day-ahead responsibilities into real-time resource control and makes the trade-offs among profit, delivery quality, and execution cost explicit. Full article

Defibrillators require high-performance power electronic converters capable of rapidly charging a high-voltage capacitor and delivering controlled therapeutic waveforms while ensuring patient safety. This paper presents a predictive control strategy for the flyback converter of a defibrillator, including both its charging and discharging stages, together with the design and verification workflow for its implementation. The proposed system integrates the FPGA implementation of the charging-stage control with a hardware-in-the-loop (HIL) emulation of the flyback converter and discharge stage within a unified model-based design framework. The charging stage consists of a flyback converter regulated by a Finite Control Set Model Predictive Control (FCS-MPC) strategy, while the discharge stage employs a full-bridge converter to generate truncated exponential biphasic (BTE) waveforms. To regulate the switching frequency without sacrificing the fast dynamic response of predictive control, the Period Control Approach (PCA) is incorporated into the FCS-MPC. The proposed solution is benchmarked against conventional FCS-MPC and a hysteresis controller, highlighting the advantages of PCA-based predictive control in terms of switching-frequency regulation while preserving accurate current tracking. The proposed control system and the corresponding defibrillator model are developed in MATLAB/Simulink and automatically translated into synthesizable VHDL using HDL Coder. This approach enables FPGA implementation of the control strategy and HIL emulation of the power converters without manual HDL programming. The proposed methodology covers the entire workflow, from simulation to real-time FPGA implementation and HIL emulation. Simulation results demonstrate accurate current tracking, proper BTE waveform generation, and improved switching-frequency regulation compared with both conventional FCS-MPC and hysteresis-based control. HIL experiments on a Xilinx Artix-7 FPGA confirm the real-time operation of the implemented predictive controller interacting with the emulated flyback converter. The experimental results are consistent with the simulation results. This work provides a solid foundation for the development and validation of digitally controlled defibrillators based on advanced predictive control techniques. The results demonstrate the feasibility of the proposed approach in both simulation and reconfigurable hardware. Full article

Fuel cell systems require high-efficiency DC–DC interfaces capable of regulating rapid voltage variations while respecting the operational constraints of proton-exchange membrane fuel cells (PEMFCs). The floating interleaved boost converter (FIBC) is a strong candidate for this purpose due to its reduced current ripple, improved power sharing, and lower component stress. The design of control strategies for FIBCs supplied by PEMFCs remains challenging because explicitly enforcing fuel cell operational constraints under fast converter dynamics is inherently difficult, particularly when detailed fuel cell models are unavailable or undesirable, as reflected in existing approaches such as classical linear regulators and single-model predictive schemes. Therefore, this paper proposes a multi-model finite control set model-based predictive control (MM-FCS-MPC) strategy for FIBC converters supplied by PEMFCs. The method employs multiple discrete prediction models with cost functions defined by the converter switching mode, integrates a fuel cell-aware reference-generation mechanism to ensure nominal and safe PEMFC operation by enforcing current and power constraints within the predictive framework, and enables fast, accurate output-voltage regulation. Detailed modelling of the FIBC, component sizing, and PEMFC characteristics is provided. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance, with a rise time of approximately 4.4 ms, a $\pm 2\%$ settling time of 10.3 ms, a maximum overshoot of only 0.056%, and a phase delay of approximately 4.26°, compared with 9.6° for the conventional PI voltage-tracking baseline. Full article

Cooperative interception by multiple aircraft requires coordination of physical event times and terminal line-of-sight (LOS) directions under strongly coupled three-dimensional motion. A limitation of predictor-based designs is that agreement of estimated terminal times is often interpreted as simultaneous interception without an explicit predictor-to-event error bound. This paper develops a distributed mixed-power fixed-time guidance framework that comprises point-mass kinematics and ideal inner-loop tracking. An exact spherical-coordinate model retains the azimuth–elevation coupling terms. The radial predicted terminal time (RPTT) is used as the distributed coordination state, and a local bound links RPTT to independently detected interception events. A mixed-power consensus protocol is analyzed for a fixed graph and a finite set of connected undirected switching graphs using a common Lyapunov function. An inertial-frame fixed-time observer estimates the unmeasured target acceleration, while a C²-regularized nonsingular fast terminal sliding-mode law regulates the terminal LOS azimuth and elevation. The analysis distinguishes nominal exact fixed-time consensus, perturbed practical fixed-time reachability, and exponential convergence inside the regularization layer. Four-aircraft simulations produced a 0.435 ms independent event-time spread and terminal LOS errors below 0.046°. Relative to a peak-matched single-power protocol, the RPTT agreement time was reduced by 34.3%, with a 0.58% increase in total control effort. The results support the method’s use as a guidance-layer coordination design within the stated sensing, communication, and inner-loop assumptions. Full article

In this paper, an ultra-local model-based finite-time sliding mode control (ULM-FTSMC) method is developed for tracking quadrotor position and attitude in the presence of uncertainties and external disturbances. Based on an ultra-local model technique, the proposed ULM-FTSMC scheme consists of an adaptive neural network observer (ANNO) and a non-singular fast terminal sliding mode controller (NFTSMC). The ultra-local model is employed to approximate complex quadrotor dynamics, thereby reducing the complexity of controller design. The ANNO is designed to estimate the state variables required for subsequent control design and compensate for the lumped disturbances. Furthermore, an improved reaching law incorporating a variable exponent and multiple power terms is developed for the nonsingular fast terminal sliding surface, based on which an NFTSMC is constructed to achieve accurate trajectory tracking within finite time. The stability of the closed-loop system and the finite-time convergence of the tracking errors are rigorously established using Lyapunov theory. Finally, comparative numerical simulations with several existing controllers are conducted to demonstrate the effectiveness and superiority of the proposed method. Full article

In this work, we present the design and numerical validation of a reconfigurable electro-optic photonic integrated circuit architecture for dynamic 1 × 4 optical routing, based on electro-optic phase shifters and a chain of cascaded multimode interference structures, and implemented in an amorphous silicon platform. By exploiting recent developments in symmetry-engineered silicon photonics to enable phase control in a platform compatible with complementary metal-oxide-semiconductor fabrication requirements, this approach addresses the increasing demand for ultra-fast, compact and energy efficient reconfigurable photonic integrated circuits. The photonic circuit design bases its functionality on self-imaging theory and has been optimized through simulations implementing the beam propagation method, while optical performance and electro-optical behavior have been validated through finite difference time-domain simulations and multiphysics modeling. The numerical results obtained confirm the operational performance of the individual building blocks and demonstrate the proposed architecture as being able to perform as a reconfigurable electro-optic platform and provide the dynamic 1 × 4 optical routing. Hence, this architecture provides a scalable platform for reconfigurable silicon photonics, complementary metal-oxide-semiconductor compatible, and the fabrication-ready layout establishes a practical path to experimental validation and future reconfigurable photonic integrated circuits. Full article

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

Extremely fast charging (XFC) of high-energy-density lithium-ion batteries is fundamentally constrained by the intrinsic waveform characteristics of conventional variable-current profiles (VCPs), limiting further reductions in charging time while maintaining electro-thermal safety. In the present study, a computational electro-thermally constrained optimization framework is developed in which a square-wave VCP is reformulated using a finite Fourier series to improve XFC performance. An electro-thermal numerical model is employed to evaluate the charging behavior of the resulting Fourier series-based square wave (F-square wave) with the number of harmonic terms ranging from N = 1 to 100. The optimal charging performance is achieved at N = 10, reducing the charging time from 940 to 878 s (6.6%) and satisfying the U.S. DOE 15-min XFC target (900 s). The performance enhancement originates from two complementary effects: the Gibbs overshoot, which locally increases the charging current near the allowable current limit, and the finite-series approximation, which smooths the current transition before and after the waveform discontinuity. Rather than treating the Gibbs overshoot associated with Fourier approximation as an undesirable numerical artifact, this study demonstrates that it can be computationally exploited as a controlled perturbation to accelerate charging while maintaining electro-thermal safety. Although the Fourier perturbation slightly increases the terminal voltage risk near the waveform discontinuity, all electrical and thermal constraints remain satisfied throughout the charging process. These findings demonstrate that finite Fourier perturbation provides an effective computational design strategy for overcoming the intrinsic waveform limitations of discontinuous charging profiles and advancing electro-thermally constrained XFC of lithium-ion batteries. Full article

This paper presents an adaptive event-triggered nonsingular fast terminal sliding-mode control (AETSMC) framework for quadrotor unmanned aerial vehicles subject to aerodynamic disturbances, actuator loss of effectiveness (LOE) of up to 60%, and intermittent denial-of-service (DoS) attacks. First, a nonsingular fast terminal sliding-mode (NFTSM) surface is constructed using fractional powers of the tracking error rather than fractional-order derivatives. This design ensures finite-time convergence while avoiding the singularity associated with conventional terminal sliding-mode schemes. Second, a smooth positive-semidefinite barrier function (Smooth-PSBF) is incorporated into the adaptive gain law. The resulting law provides only the compensation required to maintain the prescribed bound, thereby limiting gain overestimation and chattering. Third, a dual-mode event-triggering mechanism combines an exponentially decaying threshold with a zero-order hold. A positive lower bound on the inter-event interval is derived from the closed-loop dynamics, which excludes Zeno behaviour. Simulations under matched conditions show that the proposed method reduces the pitch-channel root-mean-square error by 79.4% and the integral squared error by 95.8% relative to the first reproduced baseline. In a separate 15-s communication experiment sampled at 1 kHz, the controller generated 128 transmissions instead of 15,000 periodic updates, corresponding to a 99.15% reduction. These results indicate that the proposed framework can improve fault-tolerant tracking while reducing communication demand under intermittent DoS attacks. Full article

This paper proposes an adaptive fault-tolerant control strategy for nonlinear switched systems subject to state constraints and time-varying faults. Within a backstepping framework, multiple Lyapunov integral barrier functions are constructed to enforce state constraints, while radial basis function neural networks and adaptive laws compensate for actuator and sensor faults. Under average dwell time switching, the closed-loop system is rigorously proven to achieve fast practical finite-time stability and excellent output tracking performance, ensuring all signals remain bounded without violating constraints. The approach is validated via a variable-load nonlinear spring–damper simulation. Full article

Variable-speed induction motor drives spend most of their service life at partial load, where rated-flux field-oriented control (FOC) is inefficient and where loss-minimizing control (LMC) recovers a large part of the loss. LMC, however, is brittle in two ways that matter in industry: it is tuned isothermally, so as the windings heat, the rotor-resistance drift detunes the field orientation and corrupts torque; and it treats the loss-optimal flux as a quasi-static set-point, so an abrupt load rise from a light-load, low-flux condition forces a slow flux rebuild that throttles torque. This paper proposes a thermally adaptive economic model predictive controller (TA-EMPC) that retains the energy optimum of LMC while removing both weaknesses. A temperature-coupled total-loss model (machine copper and core loss plus inverter conduction and switching loss) is minimized over a finite horizon subject to a torque-delivery constraint; a reduced-order two-node thermal observer updates the loss-defining resistances online without a temperature sensor; and a load-demand-aware flux-reservation term pre-magnetizes the machine ahead of anticipated torque rises. In simulations on a representative 7.5 kW drive, TA-EMPC matched the energy of static LMC to within 0.3% across pump, conveyor, and fast-cycling duty profiles—both saving 1.4–2.3% of cycle energy relative to rated-flux FOC, and up to about 14.7 efficiency points at very light load—while, unlike LMC, holding the steady torque error below 0.5% when the winding temperature rose by about 95 °C, to a hot steady state near 115 °C (a stator-resistance increase of roughly 37%) (against an 8% error for the non-adaptive scheme) and reducing the torque undershoot during a light-to-heavy load step from about 23% to near zero. All quantitative results reported in this work are obtained entirely in simulation. A per-step operation-count analysis—not an on-target timing measurement—indicates that the condensed quadratic-program formulation with move blocking is executable within the 100 µs sampling interval on a production digital signal controller for the chosen control horizon; experimental validation on a loaded dynamometer bench, together with on-target timing measurement, is identified as future work. The contribution is thus energy-efficient operation delivered with the torque robustness that loss minimization alone does not provide. Full article

In response to the severe chattering, slow dynamic response, and limited disturbance rejection capability of switched reluctance motor (SRM) drive systems under conventional control methods, this paper develops an improved nonsingular fast terminal sliding mode controller (INFTSMC). An integral sliding surface is first designed to eliminate the steady-state tracking error. An adaptive exponent and an improved double-power reaching law are then introduced to enhance the convergence performance. In addition, a smooth hyperbolic tangent approximation and a boundary layer design are employed instead of the conventional signum function to suppress chattering while maintaining a rapid response. Theoretical analysis demonstrates that the sliding variable is globally asymptotically stable and enters any prescribed neighborhood of the origin within a fixed time independent of the initial condition, thereby establishing practical fixed-time convergence rather than exact finite-time stability. Furthermore, an error-dependent bandwidth scheduling mechanism is incorporated into the modified super-twisting observer, resulting in an adaptive bandwidth modified super-twisting observer (AB-MSTO) for load torque estimation and compensation. The proposed bandwidth scheduling mechanism improves the balance between transient estimation speed and steady-state noise sensitivity. Finally, a MATLAB/Simulink simulation platform is constructed to evaluate the proposed control strategy. The simulation results demonstrate that the proposed INFTSMC combined with the AB-MSTO improves the dynamic speed regulation performance and disturbance rejection capability of the SRM drive system under different operating conditions. Full article

Rapid valve actions in producing or injecting wells generate pressure surges that propagate as water-hammer waves and interact with the reservoir at the sand face. Although these transients are often treated as operational disturbances, their amplitude, reflection timing, and declining pressure surge contain information on wellbore, fluid, and near-wellbore reservoir properties. This paper presents a coupled wellbore–reservoir interpretation framework for using valve-induced pressure pulses as a rapid diagnostic tool. The approach combines one-dimensional transient pipe-flow equations with analytical/numerical reservoir models. A finite-volume pipe model coupled to an analytical radial-diffusivity solution (hybrid model) is used as a benchmark, and a fully numerical finite-element formulation is then introduced to solve tubing hydraulics and porous-media flow simultaneously. The proposed test configuration places a fast-acting shut-in valve and high-resolution quartz gauge downhole, near the perforations, to reduce signal attenuation and increase sensitivity to reservoir parameters. Verification examples show that the finite-element model reproduces the coupled pressure-wave behavior of the hybrid model. Sensitivity results show that the surge amplitude scales with fluid acoustic impedance and the rate of change. Matrix permeability plays a major role in controlling reflected energy and pressure decline. Low-porosity rock has less storage capacity. It therefore gives a higher pressure response and slower dissipation. Fracture properties, such as half-length and permeability, produce distinct signatures at early times. The results show that reservoir properties strongly control the evolution of the transient pressure surge. This indicates that downhole valve-pulse testing can be used as a short-duration method to estimate near-wellbore and fracture parameters. By reducing the test duration, production interruption, equipment deployment, personnel mobilization, and associated environmental and operational burdens, the proposed analysis can lead to a more resource-efficient, cost-effective, and lower-impact reservoir characterization method. The method provides high-frequency pressure data and can support near-wellbore characterization as a complementary tool to conventional well-testing methods. Full article