Search Results (231)

Maglev transportation has emerged as a new option for long-distance travel between cities with the rapid development of transportation infrastructure. The fatigue issues of the maglev train–track–bridge coupling system, induced by increased train speeds, have garnered considerable attention. This study focuses on the continuous girder bridge of low-to-medium-speed maglev dedicated lines. A multi-vehicle coupling model and a refined vehicle–track–bridge system were constructed. These were based on the maglev equivalent stiffness-damping theory. Dynamic stress is solved using the modal superposition method. Fatigue performance under multiple working conditions is then evaluated. This evaluation uses the rainflow counting method and Miner’s linear damage theory. Dynamic stress is solved using the modal superposition method, and fatigue performance under multiple working conditions is evaluated based on the rainflow counting method and Miner’s linear damage theory. Key findings include the following: Dynamic stress peaks in the track structure reach 29.4 MPa at high-strength bolts and 20.1 MPa at bridge fasteners, significantly exceeding those in the bridge, identifying these as fatigue-sensitive zones. During a single train passage, the stress amplitudes are mainly concentrated in the low-stress amplitude range, yet annual accumulated damage at the critical node track tie and bridge fastener junction reaches 4.99 × 10⁻⁴. Increasing the train speed to 160 km/h amplifies total damage at the track tie and bridge fastener junction by 365%, with nonlinear growth in fastener damage. This research provides theoretical insights for optimizing speed-up strategies and maintenance protocols in low-to-medium-speed maglev systems. Full article

This paper presents a novel Adaptive Sliding Mode Control (ASMC) strategy for nonlinear multivariable systems subjected to parameter uncertainties and external disturbances. The proposed control scheme guarantees robust and smooth state convergence via an adaptive mechanism that dynamically adjusts the switching gain. Unlike conventional SMC techniques, this adaptive formulation effectively mitigates the chattering phenomenon through a continuously updated boundary layer and eliminates the need for prior knowledge of the uncertainty bounds. The effectiveness of the synthesized controller is validated on an autonomous electric vehicle (AEV) platform, a system characterized by strong dynamic coupling. MATLAB/Simulink (version 2022b) simulations are conducted under various operational scenarios, including load variations and strict trajectory tracking. Comparative results with a traditional SMC demonstrate superior convergence, significant chattering reduction, and an optimized energy consumption profile, leading to a 22% reduction in equivalent CO₂ emissions. This approach provides a viable and energy-efficient control framework for modern autonomous EVs. Full article

The occurrence of short-wave corrugation with wavelengths of 32–44 mm on curved sections of urban express railway lines is particularly pronounced, yet the underlying initiation mechanisms have remained insufficiently understood. Furthermore, conventional mitigation strategies—including the installation of rail dampers and passive grinding—entail substantial maintenance expenditures, thereby hindering their large-scale application. To elucidate the initiation mechanisms of rail corrugation and to formulate effective control measures, the characteristic corrugation parameters under various track structure configurations across an entire alignment were first measured and systematically analyzed. Dynamic interaction models between vehicles and three distinct track typologies were subsequently developed, together with a comprehensive analytical framework for corrugation evolution. The wheel–rail dynamic response characteristics and corrugation growth rates corresponding to each track type were examined, and the wheel–rail coupled vibration modes that exacerbate corrugation propagation in urban express lines were identified. The instantaneous wear behavior of the rail under differing creep regimes was also investigated, leading to the proposal of a novel self-mitigating approach for rail corrugation. The results demonstrate that the excitation frequency of rail corrugation is predominantly confined to the 600–700 Hz range, exhibiting a fixed-frequency characteristic that remains invariant with respect to curve radius, track structure type, and operational speed. An interesting finding is that, although the intrinsic vibration properties of different track structures diverge significantly, the third-order bending resonance of the rail segment situated between bogie wheels is largely unaffected by track-borne vibrations and manifests as a localized wheel–rail resonance within the vehicle–track coupled system. This particular resonance markedly accelerates corrugation development and is identified as the critical governing factor for corrugation initiation in urban express lines, regardless of the underlying track configuration. Furthermore, rail instantaneous wear displays a substantial phase shift under varying creep conditions, with the wear profiles under creep saturation (full sliding) and low creep (rolling–sliding) exhibiting a distinct anti-phase relationship. This insight underpins a novel self-wear suppression strategy: by intentionally mixing rolling–sliding and full-sliding operational regimes, destructive interference between the out-of-phase wear contributions is achieved, resulting in a considerably attenuated corrugation growth rate compared with exclusive rolling–sliding operation. This methodology thus offers a promising and fundamentally new alternative for the long-term management of rail corrugation through intrinsic wheel–rail interaction. Full article

With the rapid development of urban rail transportation, environmental vibrations caused by subways operating are becoming more serious. They have affected people’s living comfort, the stability of precision scientific instruments, and the safety of buildings. In order to make a good and real prediction of metro-induced vibration, this paper constructs a kind of analytical–numerical hybrid method to evaluate the dynamic response of the whole vehicle–track–tunnel–soil system. First, an analytical metro vehicle–track coupled dynamic model is established according to the Zhai method, and then the wheel–rail force acting on trains can be acquired. And then we use a 3D numeric model which incorporates the track, tunnel and ground with a finite element method. At last, the wheel–rail force is used as an excitatory load, and then the train–track–tunnel–ground coupling system dynamic response analytic–numeric model can be created. Based on this, the influence of track quality level, train speed and tunnel burial depth on the characteristics of ground surface vibration is studied in sequence. From the results, we can see that given the same track quality condition, increasing train speed greatly increases the response amplitude on the ground surface; improving track quality is very effective to reduce response amplitude; and with the tunnel buried deeper, the vibration level would be obviously reduced, local peak response would be suppressed and deep tunnels would have better vibration suppression under higher-speed operation conditions. Full article

Rear-view human tracking and re-identification remain critical challenges for robotic visual sensing in unmanned vehicles, particularly under adverse weather conditions and severe occlusion. Conventional deep learning models often suffer from feature contamination and trajectory drift under dynamic illumination. To overcome these bottlenecks, we propose a lightweight tracking framework driven by spatiotemporal prediction and multimodal feature fusion. Specifically, an ego-motion-aware Kalman prediction mechanism maintains temporal continuity during complete occlusions. Upon target reappearance, a multi-factor descriptor—fusing color histograms with geometric constraints—is employed within a dynamic Mahalanobis search region. This is coupled with a specular-reflection-penalized adaptive learning rate (η_k) that actively freezes template updates during severe environmental degradation conditions. Evaluated on a custom Mecanum-wheeled robot, the proposed method achieves a peak precision of 94.2% and a tracking success rate of 93.4%. Extensive experiments in extreme rainy night scenarios demonstrate a 35% reduction in average tracking error, maintaining a Center Location Error (CLE) below 11 pixels. Furthermore, the system achieves a rapid target re-identification response of 72.83 ms during occlusion phases. Ultimately, this framework delivers a highly robust and real-time solution for autonomous navigation in complex dynamic environments. Full article

This paper presents a robust adaptive fault-tolerant control (FTC) strategy for the path-following maneuvering of a high-speed unmanned aerial vehicle (UAV) formation system. The designation integrates an actuator gain-adaptive mechanism which is capable of compensating partial loss of effectiveness and bias faults, with a cascaded event-triggered mechanism (ETM) that regulates both control-command updates and adaptation loops. To handle strong coupling and modeling uncertainties in the UAV dynamics, unknown nonlinear terms are approximated using a fuzzy logic system (FLS), and dynamic surface control (DSC) is employed to avoid differential explosion. A boundary-regulated intermediate control term further enhances robustness against time-varying gains. The cascaded ETM reduces communication and computation by enforcing update thresholds on control inputs and parameter-update signals. Lyapunov analysis establishes semi-global uniform ultimate boundedness of all closed-loop signals. Comparative simulations indicate improved tracking accuracy and reduced channel load relative to representative baselines. Full article

Thermal management is a key factor for the efficiency, performance, and reliability of battery electric vehicles (BEVs), particularly in systems with strongly coupled components and heterogeneous thermal dynamics. This study proposes a centralized nonlinear model predictive control (NMPC) strategy for component cooling in BEVs, designed to maintain temperatures within optimal ranges while minimizing energy consumption and respecting actuator constraints. A reduced-order physics-based model is developed in MATLAB/Simulink R2024b, and the NMPC is implemented using CasADi, incorporating coolant temperatures as stabilizing states and a systematic parametrization of sampling time, prediction horizon, and weighting factors. The considered thermal management system consists of hydraulically coupled subsystems with different overall time constants, for which a single-horizon NMPC formulation is applied. Simulation results show that the proposed controller accurately tracks thermal dynamics across components with varying inertia and effectively captures cross-coupling effects. Sensitivity analyses indicate that variations in sampling time and prediction horizon have a limited impact on temperature trajectories and energy consumption, demonstrating robustness and real-time applicability. Compared to a rule-based controller, the NMPC achieves up to 30% reduction in energy consumption depending on ambient conditions and driving cycles, while improving temperature regulation, particularly for the high-voltage battery, with up to 2 K lower peak temperatures and a more balanced temperature distribution. These findings demonstrate that centralized NMPC is a suitable and efficient approach for thermal management in directly coupled BEV subsystems with heterogeneous dynamics. Full article

This paper presents a proposed architecture for an artificial intelligence-driven unmanned aerial vehicle (UAV) system intended for tactical intelligence, surveillance, and reconnaissance (ISR) missions. The architecture brings together electro-optical imaging, long-wave infrared sensing, two-dimensional light detection and ranging (LiDAR), inertial navigation support, onboard edge computing, and resilient communication links within a unified system-level framework. Unlike many existing approaches that treat perception, autonomy, communication, and safety as loosely coupled functions, the proposed architecture combines multi-modal sensing, operator-supervised autonomy, and a safety-oriented decision validation layer intended for future integration with Ansys SCADE. The system is structured around operational and sensor-performance requirements used to justify the selection and interaction of the main onboard subsystems. At the architectural level, the proposed framework is intended to support target detection, tracking, environment awareness, and mission-level decision support under degraded visibility, constrained communication, and contested operating conditions. The paper therefore contributes a requirement-driven and safety-aware ISR UAV architecture that provides a scalable basis for future implementation, validation, and multi-UAV extension. Full article

Underwater wireless optical communication (UWOC) for autonomous underwater vehicles is severely compromised by the coupling of oceanic optical turbulence and platform motion. Traditional static statistical models fail to capture the temporal evolution of these stochastic processes, hindering effective real-time beam tracking. This paper proposes a unified dynamic framework and a hybrid intelligent control strategy to address beam misalignment in turbulent environments. First, a physically motivated stochastic differential equation (SDE) model is derived from the Radiative Transfer Equation via diffusion approximation. Validated by an inverse Fokker–Planck approach, this model accurately reconstructs drift fields for diverse channel conditions, serving as a dynamic generator for time-varying fading. Second, to maintain robust link alignment, a hybrid Fuzzy-Reinforcement Learning control strategy is developed. This approach integrates the interpretability of fuzzy logic with the adaptive optimization of Q-learning, incorporating a supervisor mechanism to handle deep fading events. Numerical simulations and hardware-in-the-loop (HIL) experiments demonstrate the system’s efficacy. The proposed controller achieves a median alignment error of 3.64 mm and reduces transient errors by over 80% compared to classical PID controllers during signal recovery. These results confirm that the proposed framework significantly enhances link stability and tracking robustness for AUVs in complex random media. Full article

To address the significant degradation of steering performance in dual-motor steer-by-wire (DMSBW) systems caused by the coupling of communication delays and motor faults, a robust fault-tolerant control strategy is proposed under the dual-motor collaborative driving mode. First, a matrix polytopic model is employed to describe the nonlinearities introduced by delays, establishing a delay-dependent DMSBW system dynamics model. Second, for electrical faults such as internal motor short circuits that cause a sudden drop in rotational speed, an adaptive motor-switching fault-tolerant mechanism is designed based on a smooth monitoring function to achieve rapid fault detection and steering function reconstruction. Furthermore, considering the coupled impact of delays and faults, a robust linear quadratic regulator (LQR) controller with feedforward compensation is designed to enhance system fault tolerance and robustness. Simulation results demonstrate that under steering wheel angle step input with delays, the proposed strategy achieves a rapid response without significant overshoot, and the steady-state tracking error is significantly reduced. In variable-speed single lane change maneuvers with coupled delays and severe motor faults, the peak and root mean square (RMS) errors of the front wheel angle are reduced to 0.0112 rad and 0.0031 rad, respectively. Compared to the delay-compensated nonlinear model predictive control (NMPC) and sliding mode control (SMC) strategies that do not account for delays, the peak error is reduced by 15.79% and 45.37%, while the RMS error decreases by 27.91% and 35.42%, respectively. Additionally, the peak and RMS errors of the sideslip angle and yaw rate are substantially reduced, validating the strategy’s excellent steering fault tolerance, robustness, and vehicle handling stability. Full article

The dynamic performance of medium- and low-speed maglev vehicle–track coupling systems, as well as the dynamic response of the vehicle body and suspension frame under suspension electromagnet failure, is of great significance for the safe operation of maglev tracks. Based on vehicle–track coupling dynamics theory, and considering the spatial dynamic magnetic rail relationship in combination with the suspension control system, a dynamic vehicle–track model incorporating suspension electromagnet failure is established. The effect of such failures on electromagnet suspension force and overall vehicle performance are analyzed. The results indicate that the theoretically calculated electromagnetic force differs significantly from the actual force. Under four electromagnet operating conditions, lateral displacement has the greatest influence on suspension force. By considering the magnetic saturation of ferromagnetic materials and the leakage effect of suspension gaps, a spatial dynamic magnetic orbit relationship is established. A single-pole suspension electromagnet fault has little effect on overall vehicle performance. When the suspension electromagnet on one side fails, the suspension frame tilts toward that side and is supported and operated by a sled. When three suspension points fail, the entire suspension frame loses its suspension state and operates fully under sled support. When a suspension frame electromagnet becomes stuck, severe fluctuations in suspension force and vehicle vibration acceleration occur. These fluctuations increase with vehicle operating speed, seriously endangering operational performance. The findings provide a fundamental theoretical basis for the safe operation and maintenance of medium- and low-speed maglev vehicles under fault conditions. Full article

This paper presents a simulation-based methodology for automated tuning of a triple-loop controller (steering, throttle, and braking) for a Dallara single-seater race car. The approach targets on-track driving at handling limits, where strong nonlinearities and coupled dynamics dominate, treating the vehicle as a black box. Five controller gains are optimized via derivative-free pattern search, using reference trajectories from a professional driver in a Driver-in-the-Loop (DiL) simulator. Human-likeness is promoted by penalty terms on state and control trajectories while maximizing distance over a fixed horizon as a proxy for lap-time reduction. The application uses a high-fidelity multibody vehicle model with realistic tire, suspension, and actuator dynamics in the DiL environment, rather than simplified single-track representations. Contributions are: (i) effective application of derivative-free optimization to complex, high-dimensional, black-box vehicle systems; and (ii) a systematic, reproducible procedure for automatic tuning of controller parameters with a predetermined architecture to reproduce a professional driver’s performance and embed human-likeness. Optimization required approximately 2.4 h. Results show that the optimized controller improves track coverage by 63.6 m (1.1% increase) compared to manual tuning while maintaining a realistic driving style, offering a more systematic and reliable solution than manual, trial-and-error calibration. Full article

Continuous impact vibrations caused by uneven road surfaces (such as speed bumps) can significantly reduce the trajectory tracking accuracy of mobile manipulator. This study proposes for the first time an integrated framework combining a semi-active magnetorheological elastomer (MRE) intelligent isolation system with an active trajectory tracking controller to improve the operational accuracy of mobile manipulator under continuous impact excitation, and numerically evaluates the effect of the MRE isolation system. The working principle and design method of the MRE isolation system for mobile manipulators are described, and a multi-layer MRE isolator is fabricated and experimentally characterized. A semi-active control strategy is developed to adaptively adjust the stiffness and damping of the isolator based on continuous impact input. To further compensate for residual disturbances transmitted through the isolator, an enhanced computational torque control (CTC) and proportional-derivative (PD) controller with predefined-time disturbance observer (DOB) is designed for the mobile manipulator. This ensures that the disturbance estimate converges within a predefined time window, thereby improving the robustness of the closed-loop system. By constructing a comprehensive multibody dynamics model coupling the vehicle, the MRE isolator, and the manipulator, vibration transmission is analyzed and trajectory tracking performance is evaluated. Simulation results under continuous road impact excitation demonstrate that the proposed semi-active MRE intelligent isolation system can significantly suppress base vibration and greatly improve the trajectory tracking accuracy of the mobile manipulator end-effector and its joints. This study proves the feasibility of the semi-active MRE isolation system in the trajectory tracking application of mobile manipulator and provides a new approach for the collaborative design of intelligent vibration isolation and control strategies for mobile robot systems operating in harsh and frequently impacted environments. Full article

This paper presents the design and implementation of a real-time visual tracking system for unmanned aerial vehicles (UAVs), based on the DJIPayload Software Development Kit (PSDK), addressing the challenge of balancing high precision with low latency on resource-constrained edge platforms. By utilizing DJI PSDK to abandon the Robot Operating System (ROS) layer and its associated serialization overhead, the proposed Middleware-Free Architecture reduces end-to-end latency by over 60% to approximately 30 ms. To address computational constraints, a Lightweight Asymmetric De-coupled Visual Servoing (ADVS) strategy is proposed. It adopts orthogonal kinematic de-coupling to bypass Jacobian matrix inversion and integrates a non-linear dead-zone mechanism with dynamics-aware gain scheduling to compensate for sensing anisotropy and gravitational nonlinearity. Simultaneously, a Geometry-Aware Fusion strategy is employed to reject visual outliers, while a Finite State Machine (FSM) strictly enforces temporal consistency. Field experiments in various scenarios verify the system’s stability and tracking capability. Specifically, the platform maintains a robust lock on targets at speeds up to 23 m/s across dynamic maneuvers. The successful implementation of this system confirms that high-performance edge tracking does not rely solely on the scaling of visual model complexity but can also be effectively achieved through the architectural minimization of latency combined with the optimization of theoretically grounded robust control strategies. Full article