Search Results (785)

With the rapid development of urban rail transit, train operation control is required to meet increasingly stringent demands in terms of energy consumption, comfort, punctuality, and precise stopping. The optimization and tracking control of speed profiles are two critical issues in ensuring the performance of automatic train operation systems. However, conventional model predictive control (MPC) methods are highly dependent on parameter settings and show limited adaptability, while heuristic optimization approaches such as the whale optimization algorithm (WOA) often suffer from premature convergence and insufficient robustness. To overcome these limitations, this study proposes an optimized model predictive controller using the multi-objective whale optimization algorithm (MPC-MOWOA) for urban rail train tracking control. In the improved optimization algorithm, a nonlinear convergence mechanism and the Tchebycheff decomposition method are introduced to enhance convergence accuracy and population diversity, which enables effective optimization of the initial parameters of the MPC. During real-time operation, the MPC is further enhanced by integrating a fuzzy satisfaction function that adaptively adjusts the softening factor. In addition, the control coefficients are corrected online according to the speed error and its rate of change, thereby improving adaptability of the control system. Taking the section from Lvshun New Port to Tieshan Town on Dalian Metro Line 12 as the study case, the proposed control algorithm was deployed on a TMS320F28335 embedded processor platform, and hardware-in-the-loop simulation experiments (HILSEs) were conducted under the same simulation environment, a unified train dynamic model, consistent operating conditions, and an identical evaluation index system. The results indicate that, compared with the Fuzzy-PID control method, the proposed control strategy reduces the integral of time-weighted absolute error nearly by 39.6% and decreases energy consumption nearly by 5.9%, while punctuality, stopping accuracy, and comfort are improved nearly by 33.2%, 12.4%, and 7.1%, respectively. These results not only verify the superior performance of the proposed MPC-MOWOA, but also demonstrate its capability for real-time implementation on embedded processors, thereby overcoming the limitations of purely MATLAB-based offline simulations and exhibiting strong potential for practical engineering applications in urban rail transit. Full article

To mitigate rotor vibrations in magnetic bearing systems arising from mass imbalance, this study proposes a novel suppression strategy that integrates the crested porcupine optimizer (CPO) with an enhanced linear active disturbance rejection control (ELADRC) framework. The approach introduces a disturbance estimation and compensation scheme based on a linear extended state observer (LESO), wherein both the LESO bandwidth ω₀ and the LADRC controller parameter ω_c are adaptively tuned using the CPO algorithm to enable decoupled control and real-time disturbance rejection in complex multi-degree-of-freedom (DOF) systems. Drawing inspiration from the crested porcupine’s layered defensive behavior, the CPO algorithm constructs a state-space model incorporating rotor displacement, rotational speed, and control current, while leveraging a reward function that balances vibration suppression performance against control energy consumption. The optimized parameters guide a real-time LESO-based compensation model, achieving accurate disturbance cancelation via amplitude-phase coordination between the generated electromagnetic force and the total disturbance. Concurrently, the LADRC feedback structure adjusts the system’s stiffness and damping matrices to improve closed-loop robustness under time-varying operating conditions. Simulation studies over a wide speed range (0~45,000 rpm) reveal that the proposed CPO-ELADRC scheme significantly outperforms conventional control methods: it shortens regulation time by 66.7% and reduces peak displacement by 86.8% under step disturbances, while achieving a 79.8% improvement in adjustment speed and an 86.4% reduction in peak control current under sinusoidal excitation. Overall, the strategy offers enhanced vibration attenuation, prevents current saturation, and improves dynamic stability across diverse operating scenarios. 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

This work advances a 6G-ready, micro-granular SDN fabric that unifies high-performance edge data planes with intent-driven, multi-domain orchestration and cloud offloading. First, edge and cell-site whiteboxes are upgraded with Smart Network Interface Cards and embedded AI accelerators, enabling line-rate processing of data flows and on-box learning/inference directly in the data plane. This pushes functions such as traffic classification, telemetry, and anomaly mitigation to the point of ingress, reducing latency and backhaul load. Second, an SDN controller, i.e., ETSI TeraFlowSDN, is extended to deliver multi-domain SDN orchestration with native lifecycle management (LCM) for whitebox Network Operating Systems—covering onboarding, configuration-drift control, rolling upgrades/rollbacks, and policy-guarded compliance—so operators can reliably manage heterogeneous edge fleets at scale. Third, the SDN controller incorporates a new NFV-O client that seamlessly offloads network services—such as ML pipelines or NOS components—to telco clouds via an NFV orchestrator (e.g., ETSI Open Source MANO), enabling elastic placement and scale-out across the edge–cloud continuum. Together, these contributions deliver an open, programmable platform that couples in-situ acceleration with closed-loop, intent-based orchestration and elastic cloud resources, targeting demonstrable gains in end-to-end latency, throughput, operational agility, and energy efficiency for emerging 6G services. Full article

Achieving precise prediction and intelligent control remains a pivotal challenge in cutting processes. This need is addressed through a comprehensive survey of three critical enabling technologies: cutting force/temperature modeling, tool path planning, and vibration suppression. First, the evolution of cutting force and temperature modeling is analyzed, tracing its progression from traditional analytical methods and finite-element numerical simulations to data-driven models such as machine learning (ML) and physics-informed neural networks. This analysis highlights multiphysics coupling and model–data fusion as key to enhancing prediction accuracy. Subsequently, the evolution of tool path planning is examined, showing its development from a geometric interpolation problem into a multi-objective optimization challenge incorporating dynamic constraints, involving computational geometry, graph theory, and meta-heuristic algorithms. Finally, stability analysis based on time-delay differential equations, state identification via signal processing and ML, and active control strategies for vibration suppression are discussed. In conclusion, mathematical methods are shown to be fundamentally integrated throughout the ‘perception–prediction–decision–control’ closed-loop of the cutting process. This integration provides a solid theoretical foundation and technical support for building high-performance manufacturing systems dedicated to complex curved critical components. Full article

With the increasing demand for high efficiency and high power density in photovoltaic power generation, triangular current mode (TCM) control has garnered significant attention due to its capability to achieve zero voltage switching (ZVS) for switches. However, TCM is inherently a variable-frequency control method. Traditional modeling approaches based on fixed-frequency assumptions neglect the non-linear characteristics and sideband effects introduced by frequency variations, failing to accurately describe the dynamic behavior of the system. This paper proposes a multi-frequency small-signal modeling method tailored for TCM inverters. Small-signal models characterizing the impact of duty cycle perturbations and frequency modulation perturbations on the output voltage are derived, and the joint effect of both the duty cycle and switching frequency is analyzed. On this basis, a loop gain expression incorporating sideband frequency components is derived using Mason’s gain formula. Finally, the proposed model is verified through simulation. The results demonstrate that, compared with the multi-frequency model, which only considers the effect of duty cycle control, the proposed multi-frequency model can more accurately predict the dynamic response of TCM inverters across a wide frequency range, providing a precise theoretical basis for the control system design of variable-frequency inverters. Full article

The characteristics of the dynamic reactive power demand of a hybrid multi-infeed HVDC transmission system during the post-fault recovery period are analyzed and a coordinated control strategy to improve the fault recovery characteristics of the hybrid multi-infeed HVDC transmission system is proposed in this paper. During the process of fault recovery, the LCC-HVDC adopts a progressive staggering recovery strategy. At the same time, according to the reactive power shortage of LCC-HVDC, the dynamic power limiter is used to adjust the upper and lower limit values of the outer loop power controller of VSC-HVDC, and the reactive power generated by the VSC-HVDC can be rapidly adjusted. Therefore, the problem of excessive reactive power demand during the recovery process can be solved and the reactive power demand can be satisfied with the proposed strategy. Moreover, the ability of VSC-HVDC to provide reactive power support can be fully utilized. Finally, a simulation model of a hybrid tri-infeed HVDC system is built using PSCAD/EMTDC (Version 4.6.2) software to verify the effectiveness of the proposed control strategy. Full article

This paper investigates the consensus tracking problem for a class of high-order nonlinear multi-agent systems subject to actuator faults, sensor faults, unknown disturbances, and model uncertainties. To effectively address this problem, a hierarchical fault-tolerant control framework with fuzzy adaptive mechanisms is proposed. First, a distributed output predictor based on a finite-time differentiator is constructed for each follower to estimate the leader’s output trajectory and to prevent fault propagation across the network. Second, a novel state and actuator-fault observer is designed to reconstruct unmeasured states and detect actuator faults in real time. Third, a sensor-fault compensation strategy is integrated into a backstepping procedure, resulting in a fuzzy adaptive consensus-tracking controller. This controller guarantees the uniform boundedness of all closed-loop signals and ensures that the tracking error converges to a small neighborhood of the origin. Finally, numerical simulations validate the effectiveness and robustness of the proposed method in the presence of multiple simultaneous faults and disturbances. Full article

This paper investigates an optimal decision-making and optimization framework for networked systems operating under the coupled effects of stochastic transmission delays, packet dropouts, and input delays, which is a critical unresolved challenge in data-driven intelligent systems deployed over shared communication networks. Such uncertainty-aware optimization problems exhibit strong similarities to modern recommender and decision support systems, where multiple performance criteria must be balanced under dynamic and resource-constrained environments while addressing the disruptive impact of coupled network-induced uncertainties. By explicitly modeling stochastic transmission delays and packet losses in the sensor to controller channel, together with input delays in the actuation loop, the problem is formulated as a stochastic optimal control task with multi-stage decision coupling that captures the interdependency of communication uncertainties and system performance. An optimal feedback policy is derived based on a discrete time Riccati recursion explicitly quantifying and mitigating the cumulative impact of network-induced uncertainties on the expected performance cost, which is a capability lacking in existing frameworks that treat uncertainties separately. Numerical simulations using realistic traffic models validate the effectiveness of the proposed framework. The results demonstrate that the proposed decision optimization approach offers a principled foundation for uncertainty-aware optimization with potential applicability to data-driven recommender and intelligent decision systems where coupled uncertainties and multi-criteria trade-offs are pervasive. Full article

Unmanned aerial vehicles (UAVs) are widely utilized for environmental monitoring, precision agriculture, infrastructure inspection, and various defense missions, including reconnaissance and surveillance. Their cybersecurity is essential because any compromise of communication, navigation, or control systems can cause mission failure and introduce significant safety and security risks. Therefore, this paper examines the existing literature on UAV cybersecurity and highlights that most previous surveys focus on listing different types of attacks or communication weaknesses, rather than evaluating the problem from a control systems perspective. Considering control systems is important because the safety and performance of a UAV depend on how cyberattacks affect its sensing, decision-making, and actuation loops; modeling these attacks and their impact on system behavior provides a clearer foundation for designing secure, resilient, and stable control strategies. Based on a comprehensive review of the literature, it presents a mathematical framework for characterizing common cyberattacks on UAV communication and sensing layers, including time-delay switch, false data injection, denial of service, and replay attacks. To demonstrate the impacts of these attacks on UAV control systems, a simulation of a two-UAV leader-follower multi-agent system is conducted in MATLAB. Defense algorithms from the existing literature are then organized into a hierarchical framework of prevention, detection, and mitigation, with detection and mitigation further categorized into model-based, learning-based, and hybrid approaches that combine both. The paper concludes by summarizing key findings and highlighting challenges with current defense strategies, including those insufficiently addressed in existing research. Full article

End-to-end (E2E) autonomous driving offers a promising alternative to traditional modular pipelines by mapping raw sensor data directly to vehicle controls, thereby mitigating error propagation. However, prevalent approaches largely rely on dense Bird’s-Eye-View (BEV) feature maps, which incur high computational overhead and necessitate complex post-processing for trajectory generation. To address these limitations, we propose HiPro-AD, a proposal-centric sparse E2E planning framework that fundamentally diverges from dense BEV paradigms. HiPro-AD integrates an efficiency-oriented IM-ResNet-34 encoder with a novel STFormer. This transformer dynamically fuses multi-view spatial features and historical temporal context via a proposal-anchored mechanism, focusing computation strictly on regions relevant to sparse trajectory proposals. Furthermore, trajectory selection is refined by a Pairwise Ranking Scorer, which identifies the optimal plan from diverse candidates based on relative quality. On the NAVSIM benchmark, HiPro-AD achieves a PDMS of 92.6 using only camera input, surpassing prior dense BEV and multimodal methods. On the closed-loop Bench2Drive benchmark, it attains a 37.31% success rate and a driving score of 65.48 with a latency of 67 ms, demonstrating real-time capability. These results validate the efficiency and robustness of our sparse paradigm in complex driving scenarios. Full article

Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In recent years, with the increasing adoption of deep learning (DL) technologies, the research focus in DED has gradually shifted from traditional “process parameter optimization” to “AI-driven process optimization” and “online real-time monitoring”. Given the complex and distinct influence mechanisms of key parameters (such as laser power/arc current, scanning/travel speed) on melt pool behavior and forming quality in the two processes, the introduction of artificial intelligence to address both common and specific issues has become particularly necessary. This review systematically summarizes the application of DL techniques in both types of DED processes. It begins by outlining DL frameworks, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL), and their compatibility with DED data. Subsequently, it compares the application scenarios, monitoring accuracy, and applicability of AI in DED process monitoring across multiple dimensions, including process parameters, optical, thermal fields, acoustic signals, and multi-sensor fusion. The review further explores the potential and value of DL in closed-loop parameter adjustment and reinforcement learning control. Finally, it addresses current bottlenecks such as data quality and model interpretability, and outlines future research directions, aiming to provide theoretical and engineering references for the intelligent upgrade and quality improvement of both DED processes. Full article

To tackle the challenges faced by traditional wheeled tractors, whose steering systems have low flexibility and a large turning radius, and thus make turning hard in small fields and greenhouses, this paper proposes a differential steering control technology for battery-electric unmanned tractors. This innovative approach enables zero-radius turning while delivering environmental and economic advantages. Firstly, the system architecture and key components of the battery-electric unmanned tractor with differential steering are designed, including the mechanical structure, wheel-drive system, electrical system, and power battery. Based on the proposed system architecture, a multi-physics coupled model is established, covering the motor, reducer, battery, driver, vehicle body, and the relationship between tires and road surfaces. A multi-closed-loop control algorithm, regulating both the motor speed and yaw angular velocity of the tractor, is developed for differential steering control. The validation, conducted via a digital simulation platform, yields critical state curves for motor current, torque, speed, and vehicle rotation. This study establishes a novel theoretical framework for unmanned tractor control, with prototype development guided by the proposed methodology. Experimental validation of zero-radius steering confirms the efficacy of differential steering in battery-electric platforms. The research outcomes provide theoretical basis and technical references for advancing intelligent and electric agricultural equipment. Full article

This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article

With the irregular integration of small-capacity distributed generators (DG) and single-phase loads, rural low-voltage distribution transformers are faced with issues such as three-phase imbalance, light-heavy loading, and feeder terminal voltage excursions, impacting the safe and stable operation of the system. To address this issue, a coordinated control strategy based on direct power control (DPC) for low-voltage substation series-parallel coordination is proposed. A flexible interconnection topology for multi-substation series-parallel coordination is designed to achieve coordinated optimization of alternating current–direct current (AC-DC) power quality. Addressing the three-phase imbalance, light-heavy loading, and feeder terminal voltage excursions in rural low-voltage distribution transformers, a series-parallel coordinated optimization control strategy is proposed. This strategy incorporates a DC bus voltage control strategy based on sequence-separated power compensation and a closed-loop control strategy based on phase-separated power compensation, effectively addressing three-phase imbalances and load balancing in each power distribution areas. Furthermore, a series-connected phase compensation control strategy based on DPC is proposed, efficiently mitigating feeder terminal voltage excursions. A corresponding circuit model is established using Matlab/Simulink, and simulation results validate the effectiveness of the proposed strategy. Full article