Search Results (2,266)

Reliable estimation of vehicle states, tire cornering stiffness, and the tire-road adhesion coefficient are essential for vehicle lateral stability and intelligent chassis control. Under low adhesion, nonlinear tire operation, and weak excitation, lateral-force residuals are jointly affected by cornering-stiffness variation, adhesion-coefficient variation, and tire-force saturation, which may cause erroneous parameter adaptation. This paper proposes a reliability- and sensitivity-guided co-estimation method for vehicle states, tire cornering stiffness, and the tire-road adhesion coefficient. A hierarchical framework is developed based on a planar 3-DOF vehicle model and a Fiala-type nonlinear tire model. Front- and rear-axle lateral-force pseudo-measurements are reconstructed from lateral acceleration and yaw angular acceleration, without requiring additional tire-force sensors. Parameter-update reliability is evaluated by considering lateral excitation, longitudinal slip, adhesion utilization, and normalized lateral-force residual consistency. Normalized lateral-force sensitivities are then used to allocate the residual between the cornering-stiffness and adhesion-coefficient update channels. CarSim/Simulink co-simulations under high-, intermediate-, and low-adhesion double-lane-change maneuvers demonstrate that the proposed method improves sideslip-angle and lateral-velocity estimation accuracy, suppresses erroneous cornering-stiffness adaptation, and provides more stable estimates of the tire-road adhesion coefficient. Full article

Bioinspired superhydrophobic surfaces (SHS) have been proven to afford high corrosion inhibition to the underlying metal. Targeting brass metal, this paper presents a biomimetic mineralization route for obtaining SHS. Calcium carbonate is first synthesized in an ethanol solution containing an organic curing agent through CO₂ gas introduction, resulting in colloidal material. Subsequent modification with stearic acid yields the SHS. Electrochemical impedance spectroscopy (EIS) experiments reveal that the biomimetic calcium carbonate cluster coating significantly improves the corrosion inhibition performance. After the coverage of the CaCO₃ SHS, the low-frequency impedance modulus value increases to 4.6 × 10⁵ Ω cm², which is enhanced compared with the bare brass with 3.2 × 10³ Ω cm². Meanwhile, the corrosion current density value decreases substantially from 2.31 × 10⁻⁶ mA/cm² for bare metal to 1.30 × 10⁻⁸ mA/cm² for the SHS surface. This demonstrates its high anti-corrosion properties. Acid-base corrosion tests further confirm the good resistance of the coating to an alkaline environment. Moreover, the coating exhibits anti-freezing adhesion and self-cleaning properties, surpassing the bare brass. The combined characteristics of the biomimetic calcium carbonate SHS coating highlight the promising potential in corrosion protection applications. Full article

Wall-climbing robots are specialized robotic systems capable of adhering to vertical surfaces and moving freely to perform tasks using various adhesion mechanisms. These robots hold significant application potential across multiple domains. Based on their adhesion methods, climbing robots can be categorized into several types: negative pressure adhesion, magnetic adhesion, electrostatic adhesion, bio-inspired adhesion, and thrust-based adhesion. In terms of locomotion, they can be classified into wheeled, tracked, legged, and hybrid configurations. This paper reviews the current research status of various wall-climbing robots, highlighting their respective advantages and disadvantages, and discusses future development directions. Additionally, the path planning of wall-climbing robots is broadly divided into global and local approaches. The underlying principles of commonly used path planning algorithms are introduced, along with an analysis of future trends in this area. Full article

Battery housings are critical structural and safety components in electric vehicles, fulfilling multiple functions related to mechanical protection, crashworthiness, thermal management, fire resistance, electromagnetic shielding, and integration of battery modules into the vehicle body. While metallic housings, particularly aluminum and steel, remain dominant in industrial applications, increasing attention is being given to composite materials as lightweight alternatives capable of improving energy efficiency and extending driving range. However, the growing use of composites in battery enclosures raises important questions regarding recyclability, end-of-life management, and compatibility with circular economy principles. This review critically examines the current state of the art in composite materials used for electric vehicle battery housings, with particular emphasis on glass- and carbon-fiber-reinforced thermoplastics, thermoset composites, sandwich structures, and hybrid multi-material systems. The paper discusses the functional requirements imposed on battery housings and analyzes how these requirements influence material selection and design strategies. Particular attention is devoted to recycling pathways applicable to composite battery enclosures, including mechanical recycling, thermal treatment, chemical recycling, and reuse-oriented approaches, as well as to the limitations associated with mixed-material assemblies, adhesives, coatings, and integrated functions. The review also addresses circular design strategies for battery housings, including design for disassembly, material traceability, modularity, and the incorporation of recycled polymers and secondary reinforcements into new housing systems. Current research gaps are identified in the integration of structural performance, fire safety, manufacturability, and recyclability within a single design framework. The analysis shows that thermoplastic composites currently offer the most promising route toward circular battery enclosures, while thermoset-based systems still face significant challenges in high-value recycling. The paper concludes by outlining future research directions required for the development of lightweight, safe and recyclable composite battery housings aligned with sustainable mobility and circular economy goals. Full article

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints. Full article

Corner-module electric vehicles equipped with four-wheel independent drive, four-wheel independent steering, and electromechanical braking (EMB) actuators provide a flexible platform for software-defined chassis control, but braking-in-turn maneuvers impose severe longitudinal–lateral coupling and competition for tire adhesion resources. This paper proposes an actuator-oriented hierarchical coordinated control strategy for EMB-based corner-module vehicles. At the upper level, a Model Predictive Controller optimizes lateral tire force allocation under a tire-friction-ellipse hard constraint and coordinates the four-wheel steering response. At the lower level, a three-intensity adaptive braking-force distribution algorithm converts the vehicle-level demand into wheel-level EMB clamping-force commands while considering braking intensity, steering intensity, load transfer, and yaw stability. To improve actuator tracking accuracy, the EMB subsystem combines nonlinear actuator modeling, offline parameter identification, online recursive-least-squares correction, and force–speed–position cascade control. MATLAB (R2025b)/Simulink-CarSim co-simulation and EMB hardware-in-the-loop (HIL) tests verify the proposed strategy under fixed-angle emergency braking and lane-change braking conditions with high, low, and variable-adhesion roads. The results show improved trajectory tracking and yaw stability, reduced braking-torque fluctuation, and faster EMB clamping-force response, demonstrating the suitability of the proposed actuator-level coordination method for intelligent electric chassis applications. Full article

Sustainable binders are becoming decisive enabling materials for fiber-based packaging and cellulosic nonwovens because they govern strength, coating integrity, barrier performance, printability, wet durability, and end-of-life behavior. However, replacing fossil-derived latexes, fluorinated finishes, or persistent wet-strength systems with renewable alternatives is not a simple material substitution problem. This perspective argues that sustainable binders must be evaluated through an industrial lens that integrates performance, scalability, cost, process compatibility, food-contact safety, and recyclability. The discussion examines current binder limitations, emerging bio-based alternatives including starch, cellulose derivatives, nanocellulose, proteins, lignin, tannins, chitosan, hemicelluloses, and reactive green crosslinking systems, and the specific opportunity to move from bulk binder replacement toward engineered cellulose–cellulose junctions. Enzyme-assisted activation of cellulose surfaces, especially routes that generate controlled carboxyl and aldehyde functionality, is highlighted as a promising platform for low-energy bonding of recyclable all-cellulose webs when paired with rigorous spectroscopy, mechanical testing, and multiscale modeling. The central conclusion is that the next generation of sustainable binders will be selected not by renewable content alone, but by their ability to deliver reliable performance within high-throughput manufacturing and credible recovery pathways. Full article

Conventional epoxy adhesives used in motor insulation structures still suffer from insufficient thermal resistance and difficulty in balancing heat resistance with mechanical reliability. In this study, BMI-modified E-51/MeHHPA/EMI-24 epoxy composites were prepared and evaluated as heat-resistant interfacial adhesives for simulated F-class pole windings. BMI/EP composites with different BMI contents were fabricated by melt blending and characterized in terms of curing kinetics, FTIR, mechanical properties, and thermal performance. The optimized formulation was then applied to bond Nomex insulation paper to the upright plate in a simulated pole winding. The results showed that BMI did not alter the main epoxy/anhydride curing pathway, but restricted segmental motion and improved thermal resistance. The 10phr BMI/EP composite exhibited a favorable balance among thermal performance, mechanical properties, and fracture morphology. The simulated winding prepared with this formulation showed no breakdown or flashover under 6800 V/60 s, with an insulation resistance of 64.49 GΩ. A lower-bound apparent temperature index of approximately 157 °C was obtained using the TGA-derived thermal life equation. These results indicate that this system has preliminary application potential as a heat-resistant interfacial adhesive for F-class motor winding insulation, although a complete thermal life assessment is still required. Full article

Four-wheel independent steering (4WIS) vehicles can improve low-speed maneuverability and high-speed stability by independently regulating the steering angles of all four wheels. However, under large-curvature trajectories, parameter perturbations, and external disturbances, inconsistent coordination among the four-wheel steering angles may increase tire lateral slip, yaw response deviation, and trajectory tracking errors. To address the difficulty of conventional trajectory tracking methods in simultaneously ensuring geometric consistency, tracking accuracy, and robustness, this paper proposes an adaptive sliding mode trajectory tracking control method based on instantaneous center of rotation (ICR) constraints. First, the tire instantaneous turning center (TTC) of each wheel is derived using rigid-body spatial kinematics, and the TTCs are mapped onto a unified vehicle-body reference plane based on the SAE J670 coordinate system to obtain a real-time vehicle-level ICR estimation. Second, a lateral–yaw dynamic model and a trajectory tracking error model are established. The yaw rate and sideslip angle are corrected using ICR geometric information, and an adaptive sliding mode controller is designed with an equivalent control term, adaptive switching gain, adaptive boundary layer, and sideslip suppression term. The uniform ultimate boundedness of the sliding variable and closed-loop tracking errors is proven using Lyapunov theory. Finally, MATLAB (2023a)2024/CarSim (2019) co-simulations are conducted under small-curvature sinusoidal, double-lane-change, large-curvature sinusoidal, low-adhesion, and mass-perturbation conditions. The results show that the proposed ICR-SMC method significantly reduces lateral and heading errors compared with U-LQR and U-SMC, especially under large-curvature and low-adhesion conditions, demonstrating improved tracking accuracy and robustness for 4WIS vehicles. Full article

This paper presents the structural characterization of zinc coatings on S235JR steel elements. The study offers a novel and comprehensive assessment of zinc coatings applied to profiled steel elements through hot-dip galvanizing. It examines coatings formed under real industrial production conditions, providing practical insight into their behavior on complex geometries. The characterization includes metallographic, mechanical, diffraction, and tribological tests. Metallographic observations revealed the layered structure of zinc coatings, consisting of the η, ζ, δ, and Γ phases, each with varying chemical compositions and microhardness. All coatings exhibited similar resistance to damage initiation; however, microscopic analysis revealed differences in their subsequent degradation. The thickest coating showed earlier formation of adhesive cracks, indicating increased stress concentration and a faster progression of damage. Full article

Harmonized laboratory methodologies, notably the CEPI recyclability laboratory test method (latest version 3, released February 2025) and the 4evergreen protocol (latest revision 1, released January 2025), are widely used to assess the recyclability of paper-based materials. However, the extent to which laboratory-scale results reflect pilot-scale behavior remains insufficiently documented. In this work, the recyclability of brown packaging paper was evaluated at both laboratory and pilot scales. Disintegration was performed under identical consistency, temperature, and duration, followed by screening, filtrate analysis, macro-stickies quantification, and paper sheet adhesion evaluation according to the CEPI methodology. In parallel, recycled paper prototypes were produced in a pilot paper machine and were mechanically characterized. The material was classified as technically recyclable in a conventional recycling mill at both scales, with closely aligned recyclability scores. Nevertheless, pilot-scale testing revealed higher dissolved and colloidal substances, increased macro-stickies content, and sheet adhesion phenomena not fully apparent at laboratory scale. These results demonstrate that while laboratory tests are robust for recyclability classification, pilot-scale trials provide essential insights into runnability and operational risks relevant for industrial implementation. Full article

Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints. Full article

This paper proposes a physically based hybrid architecture for controlling mobile robot drives. It combines a model-based controller, an adaptive neural network compensator for residual dynamics, and a Lyapunov-based stability supervision mechanism. Unlike existing hybrid control approaches, the proposed architecture implements a structured injection of neural network correction directly into the physical drive model with a controlled Lyapunov-based adaptation constraint. A mathematical model of the electromechanical drive of a differential mobile platform is developed, taking into account electrical and mechanical dynamics, wheel-to-surface contact interaction, and the system’s energy characteristics. Numerical simulation results demonstrate that the hybrid approach improves tracking accuracy, improves transient response, and ensures stable operation of the control system under parametric uncertainty, adhesion changes, and external disturbances. The proposed architecture maintains the physical interpretability of the model while simultaneously enhancing the system’s adaptability. The obtained results confirm the effectiveness of the developed method and its potential for application in control systems for mobile robotic platforms. Full article

To address the difficulty of real-time detection of seed-filling performance in pneumatic suction seed metering devices under high-speed operation—where seed targets are tiny, prone to adhesion, and affected by motion blur—this paper proposes a lightweight online detection algorithm, YOLOv8n-MA. First, according to the seed adsorption characteristics of the suction holes, the detection targets are divided into three categories: none, one, and two. Second, based on YOLOv8n, the backbone network is replaced with MobileNetV1 to reduce computational cost, and an ACmix attention module is integrated into the Neck to enhance feature representation for the three suction-hole states. Finally, to meet the demand for low-latency inference on resource-constrained devices, the model is deployed on an edge computing controller to achieve real-time detection. Experimental results show that, compared with the original YOLOv8n, the parameters and FLOPs of YOLOv8n-MA are reduced by 34.4% and 59.8%, respectively, while the mean average precision (mAP) is improved by 2.0% to 96.8%, achieving a superior trade-off between accuracy and efficiency over other detection models of the same category, such as YOLOv5n, YOLOv9n, and YOLOv10n. In field tests, the detection accuracy reaches 95.02% at 12 km/h and 92.65% at 15 km/h. The proposed method provides effective technical support for the intelligent monitoring and control of precision seeding under high-speed operation. Full article

This review explicitly focuses on agricultural attachments and executing components that interact directly with soil and crops, rather than the tractor vehicle itself. Operating within complex and variable farmland media environments, the key components of agricultural machinery have long been constrained by bottlenecks such as high-energy draught resistance, severe solid–liquid interfacial adhesion, and intense abrasive wear. Bionic functional surfaces, based on the coupling of micro-geometric morphology and surface-interface physical chemistry, provide a scientific approach to overcoming traditional tribological limitations by reconstructing the contact mechanics and fluid dynamics boundaries at the interface. This paper presents a comprehensive review of the latest research progress regarding bionic functional surfaces in the fields of friction reduction, wear resistance, and anti-adhesion in agricultural machinery. The article systematically categorises typical biological prototypes, such as soil-burrowing animals, aquatic organisms, and plant leaves, alongside their multidimensional feature extraction methods. It provides an in-depth analysis of core interaction mechanisms, ranging from static air cushion effects and dynamic wetting evolution to active electro-osmotic soil detachment, interfacial stress redistribution, and microscopic wear debris capture. Furthermore, it evaluates the efficacy of cross-scale coupled numerical simulation technologies in resolving interfacial interactions. At the engineering application level, this review extensively discusses the field performance of bionic structures in typical operational scenarios, including draught reduction in tillage and land preparation, blockage prevention in seed-metering channels, and low-damage harvesting in agricultural machinery. Finally, countermeasures are proposed to address the fatigue degradation of bionic surfaces under alternating field loads and the barriers to the large-scale fabrication of large-sized components. The paper further highlights the development trend towards the deep integration of bionic tribology with digital twins and intelligent wear-state perception technologies, aiming to provide systematic underlying theoretical and technical references for the research and development of the next generation of intelligent agricultural equipment characterised by low energy consumption and a prolonged service life. Full article