Search Results (493)

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

The synergistic co-production of ceramic fibers from fly ash and coal gangue offers a promising path for their high-value utilization. However, research in this area remains limited, hindering its broader application. This study employs numerical simulations to assess the influence of high-wheel rotational speed and melt temperature on the mass of inter-wheel melt transfer, as well as their effects on ligament size and slag-ball fraction. The results show that the high wheel, responsible for melt pre-fragmentation and transfer, plays a crucial role in determining the mass of inter-wheel melt transfer and controlling ligament dimensions. In contrast, the low wheel does not directly affect ligament size but aids in transforming pre-fragmented droplets into ligaments and modulates their dispersion. Melt temperature impacts both transfer mass and ligament size by modifying melt properties. The slag-ball fraction increases with the melt temperature and decreases with the high-wheel speed, while the low-wheel speed has a negligible effect. Under the optimal operating conditions of a melt temperature of 1745 °C and equal rotational speeds of 10,000 rpm for both the high and low wheels, a ligament structure with a relatively concentrated size distribution is obtained, with the slag-ball fraction effectively controlled within the range of 8–13%. Full article

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

This paper presents a mathematical modeling-based vision-guided triple-loop control method for lane tracking of an underactuated bicycle robot. To describe the coupling between lateral balance and path tracking, a reaction-wheel-based inverted-pendulum model is established using the Lagrange formulation. Based on the linearized dynamics, the transfer function between the flywheel rotational speed and the motor torque is derived, providing a mathematical basis for designing the gain-scheduled triple-loop PID controller. To generate continuous control inputs under practical visual disturbances, an improved Hough transform, a near-field multi-layer sliding window detector, and a multi-scenario finite-state-machine strategy are incorporated for lateral deviation estimation and path reconstruction. A cascaded smoothing filter is further introduced to reduce high-frequency command fluctuations and improve the closed-loop control response. Real-vehicle experiments on an STM32F407-based underactuated bicycle robot demonstrate that the proposed framework achieves stable dynamic balance and robust lane tracking. Compared with a conventional Hough-transform and sliding window method, the lateral RMSE is reduced by 40.2%, 39.85%, and 32.35% in straight, left-turn, and right-turn scenarios, respectively. Full article

This paper presents an FPGA-based hardware-in-the-loop (HIL) platform for real-time simulation testing of anti-lock braking system (ABS) electronic control units (ECUs). The proposed system integrates a Temporal Convolutional Network (TCN) model implemented on FPGA hardware to provide real-time predictions of wheel speed sensors under complex braking scenarios. The FPGA acceleration achieves low-latency processing with a total end-to-end latency of 10.61 µs per prediction cycle, corresponding to approximately 94.3 Ksamples/s, which is suitable for closed-loop automotive testing. Experimental results show that the TCN model provides accurate prediction based on mean squared errors below 0.001043 for key parameters such as wheel speed sensors and lateral acceleration. The modular architecture of the simulator allows extensibility to other automotive ECUs and provides a scalable solution for real-time system validation in safety-critical applications. Full article

As an important oil crop in China, peanuts require mechanized sowing to enhance production efficiency. This paper analyzes the influence of peanut seed physical characteristics on the design of seed metering devices and systematically introduces the working principles, advantages, and disadvantages of mechanical (internal cell-fill, cell-wheel, and spoon-wheel types) and pneumatic (air suction, air pressure, and air-blowing types) seed metering devices. This paper reviews the development status of peanut seed metering devices and provides examples of these devices mounted on sowing machines. It is found that European and American researchers mainly conduct research on high-speed pneumatic seed metering devices, and, though China’s peanut seed metering device development has made progress and resulted in products with different technical levels, they still fall short of international advanced products in terms of high-speed seeding capability and seeding accuracy. In the future, research should be strengthened in areas such as the in-depth development of pneumatic seed metering devices, the adaptability of mechanical seed metering devices to high-speed operation, and intelligent monitoring and control systems. Full article

Modern vehicles rely on in-vehicle network protocols such as Controller Area Network (CAN) protocol, but these protocols were designed without encryption or authentication. Therefore, the vehicles are exposed to cyber attacks. Motion-based Intrusion Detection Systems (MIDSs) exploit correlation between physically related signals to detect attacks. However, we show that MIDSs are vulnerable, because correlation coefficient is invariant to positive linear scaling. Hence, an adversary may manipulate a signal while keeping its correlation high. In this paper, we propose a Correlation Scaling Attack (CSA) that forges wheel speed signals by scaling their original value while keeping the temporal trend consistent with the other signal. We analyze that correlation coefficient remains unchanged when the signal is forged. Consequently, the CSA evades conventional MIDSs. To mitigate this limitation of MIDS, we exploit covariance between two signals as a complementary indicator, since covariance provides magnitude information. We evaluate the proposed attack and defense mechanism using CAN log data collected from a real vehicle. Experimental results verify the effectiveness of CSA, and we demonstrate that CSA can be detected by observing covariance between two signals. Our research not only indicates that the CSA is a significant threat to cars, but provides a feasible mitigation exploiting the covariance. Full article

Conventional maize fertilization suffers from uneven distribution, fertilizer waste, and environmental pollution. To address these issues and achieve precision fertilization for maize, a straight grooved-wheel fertilizer apparatus (SGWFA) was designed and optimized using the discrete element method (DEM). The blocking characteristic of the SGWFA was also evaluated. The optimal configuration (eight grooves, inner diameter of 26 mm) yielded a minimum discharge uniformity coefficient of variation of 2.50% and mild blocking, with a maximum total force of 161.884 N. Furthermore, a nonsingular terminal sliding mode control (NTSMC) algorithm was proposed for the speed loop of the brushless DC (BLDC) motor drive, while the current loop used conventional proportional-integral (PI) control. The overall system achieved dual closed-loop speed and current regulation with finite-time convergence of the speed tracking error. Simulations showed that, compared with conventional PI and fuzzy PI controllers, NTSMC had the smallest overshoot of 3.4%, the shortest settling time of 0.165 s, and the fastest disturbance rejection. Bench tests confirmed that the coefficient of variation under NTSMC was 2.85%, markedly better than fuzzy PI’s 3.15% and conventional PI’s 4.03%. It is also basically consistent with the simulation results. Field tests at 6, 9, and 12 km/h demonstrated over 95% per-row fertilization accuracy, with a maximum relative error of only 4.61%. This integrated system can effectively achieve precise fertilizer application under variable field conditions. Full article

This study examines the influence of adding different profile geometries on the rollability performance of a wheeled robot released from various heights under controlled conditions. Three profile configurations were parametrically designed, computationally modeled, and optimized using a physics-based simulation framework. The optimized designs were then 3D-printed and attached to a robot chassis and evaluated alongside a baseline configuration (no profile addition). Rollability success was defined as the chassis returning to a stable, on-the-wheels configuration after launch. Experiments were conducted across two drop heights (75 cm and 130 cm), two launch speeds (0.8 m/s and 1.5 m/s), and launch angles ranging from −60° to +60°. The results demonstrate strong sensitivity of rollability performance to geometric configuration. Two of the optimized profiles showed significant improvements compared to the baseline. The best-performing profile exhibited robust performance across varying heights, speeds, and angles, whereas the other profile showed substantial performance gains at higher speeds and drop heights. These findings confirm that appropriate geometric optimization of profile structures can substantially enhance rollability stability for wheeled robots under dynamic impact conditions. Full article

To address the coupled challenges of tire dynamic load regulation and ride comfort improvement in hydrogen-powered heavy-duty trucks (HPHDTs) under non-stationary road excitations, this study evaluates a magnetorheological (MR) damper-based semi-active front suspension system. A vehicle–road coupled dynamic simulation model was developed in MATLAB/Simulink (R2025b) using a Class C road profile, and three representative driving conditions, namely acceleration, deceleration, and constant-speed driving, were considered. Four control strategies, namely, interval type-2 (IT2) fuzzy control, type-1 (T1) fuzzy control, skyhook control, and PID control, were comparatively investigated. The results indicate that deceleration is the most critical operating condition, resulting in more severe tire–road interactions and poorer ride comfort than the other scenarios. Among the evaluated strategies, IT2 fuzzy control provides the best overall performance. Compared with the passive suspension, it reduces the front-wheel RMS dynamic load by 63.39% and improves ride comfort by 64.67% under deceleration. The T1 fuzzy and PID controllers provide moderate improvements, whereas skyhook control exhibits relatively limited effectiveness. These findings demonstrate that combining MR dampers with IT2 fuzzy control provides a feasible and robust approach for improving road friendliness, ride quality, and operational stability in advanced heavy-duty vehicle suspension design. Full article

To address the vibration reduction problem of the six-degrees of freedom(6-DOF) half-vehicle model and to improve ride comfort and handling stability, a piezoelectric stack actuator based on the inverse piezoelectric effect was introduced. A 6-DOF half-vehicle dynamic model coupling the cab, body, and wheels was established based on the Lagrange equation. Based on this model, a vertical-pitch dual sliding surface RBF neural network sliding mode control strategy was proposed, with two independent RBF neural networks designed to separately approximate, online, the comprehensive uncertainties in the vertical and pitch channels associated with unmodeled dynamics, external disturbances, and modeling simplifications. The variable-speed reaching law (dsat) function was used to design the sliding mode reaching law, balancing sliding surface convergence speed and vibration suppression. Six indicators, including vertical acceleration of the cab and vertical acceleration of the vehicle body, were selected as performance evaluation metrics to establish the fitness function. Combined with a genetic algorithm, the dual sliding surface coefficients, RBF network parameters, adaptive update rates, and variable-speed reaching law parameters were globally optimized. The vibration reduction effects of four schemes—passive control, traditional sliding mode control, RBF sliding mode control, and genetic algorithm optimized RBF dual-sliding-mode control—were compared and analyzed. Simulation results show that the genetic algorithm optimized RBF dual-sliding-mode control achieves improved vibration suppression in several key ride-comfort-related indices and provides better overall coordination among ride comfort, suspension working space, and tire dynamic deflection. The research results validate the effectiveness of this method and provide a new solution for addressing vehicle vibration reduction problems. Full article

This study presents the development and validation of a simulation-calibrated kinematic formulation for a three-wheeled symmetric pipeline inspection robot operating under cylindrical confinement. The proposed model integrates analytical implementation in MATLAB 2023b with multibody simulation in SolidWorks 2023 to identify semi-empirical correction terms that improve motion prediction under straight and curved pipe conditions. The formulation incorporates curvature-dependent and asymmetry-related effects derived from structured simulation datasets, ensuring consistency between analytical predictions and simulated behavior within the evaluated operating range. Quantitative comparison using statistical indicators demonstrates strong agreement between both approaches, with MAE values of 0.0547 for linear velocity and 13.96 for displacement, RMSE values of 0.0681 and 19.0401, and coefficients of determination of $R^2=0.9997$ and $R^2=0.9476$, respectively. Slightly larger deviations are observed at higher rotational speeds. The results provide a consistent analytical representation of the robot’s motion under the studied geometric constraints and establish a basis for future experimental validation and control-oriented extensions in confined pipeline environments. Full article

Speed information is crucial for controlling distributed electric-drive loaders, especially for driving and operation. Due to complex working conditions, the wheels of the loader often experience different conditions, leading to inaccurate speed estimation. To solve this, this paper proposes a multi-sensor fusion speed estimation method based on a Finite State Machine (FSM). The method uses the FSM to identify the wheel states and adaptively switches between the weighted average method and integration method to estimate the vehicle’s speed accurately. When all wheels are slipping, the acceleration integration method is used, starting from the latest trustworthy speed estimate. When the wheels are not slipping, the speed is estimated using the weighted average of the trustworthy wheels. Additionally, the method addresses the relative motion between the front and rear vehicle bodies caused by articulated steering by using an articulated steering projection method to ensure accurate wheel state estimation from IMU signals. Simulation and hardware-in-the-loop experiments show that the proposed method can accurately estimate vehicle speed under various road conditions. Specifically, under low-adhesion road conditions with all four wheels in a slipping state, it improves speed estimation accuracy by over 75% compared to traditional methods such as simple averaging, selective averaging, and pure integration. Full article

For autonomous vehicles, high-speed cornering can easily lead to degraded handling stability and increased risks of sideslip or even rollover. Therefore, vehicle phase-plane stability-region analysis has become an important topic in active safety-control research. However, most existing studies still construct phase-plane stability regions mainly based on simplified vehicle models, without sufficiently considering the influence of vertical load transfer during cornering on tire lateral forces and stability boundaries. To address this issue, this paper proposes a hierarchical control strategy based on phase-plane analysis for active inward tilt vehicles. This method adopts a three-degree-of-freedom vehicle dynamics model and a tire model. By carefully comparing the phase-plane stability regions of active inward tilt and passive roll vehicles and by further analyzing the state-trajectory convergence characteristics of active inward tilt vehicles under different longitudinal speeds, front wheel steering angles, and road adhesion coefficients, the effects of active inward tilt on stability-region expansion and vehicle-state convergence are revealed. Subsequently, a hierarchical control strategy is proposed as an integrated solution to improve vehicle handling stability. The upper-level controller dynamically adjusts the reference values and objective weights according to whether the vehicle state is located in the stable, critical, or dangerous region. The lower-level NMPC controller optimizes the front wheel steering angle and active suspension forces to achieve coordinated trajectory tracking and stability control. Double lane-change simulation results show that active inward tilt can improve the left–right vertical load distribution and expand the lateral stability region. Compared with passive roll and conventional active inward tilt control, the proposed strategy reduces the phase-plane state convergence area by 68% and 75%, respectively, thereby improving vehicle handling stability and active safety under extreme conditions. Full article

Rapidly removing rainfall from roadways is necessary to avoid vehicle accidents caused by hydroplaning or suddenly unbalanced forces on the front wheels. Ensuring adequate water removal and minimal bypass requires correct sizing of drainage structures. Undepressed curb opening inlets (UCOIs) are often preferred along high-speed roads where curb depressions can cause a loss of vehicle control. Recent work has shown that classic curb inlet design equations can be in error for long curb opening inlets (>2 m). This study provides results of laboratory experiments that build on recent work to evaluate the performance of different curb inlet equations. A new approach using neuro-fuzzy modeling that applies the proven adaptive neuro-fuzzy inference systems (ANFIS) was evaluated for use in sizing UCOI. This study aims to find the method that has the best hydraulic performance. Results show that some earlier models actually estimate inlet lengths better than more recent design equations under some roadway configurations. The use of the ANFIS approach provides the lowest root mean square errors and mean absolute percentage errors when compared to available models and may be adopted in the practice of UCOI inlet design safely. Full article