Search Results (52)

The steer-by-wire (SBW) system represents an optimal solution for achieving intelligent vehicle steering. However, the current reliability of SBW motors and electronic control units remains limited. Disturbances, including variations in the external road environment and time-varying parameters, can significantly impact vehicle stability. To address these challenges, a hierarchical control strategy is proposed in this paper. In the upper layer, model predictive control (MPC) is employed to optimize the sideslip angle and yaw rate by tracking their reference values, thereby enhancing the stability of the SBW system. In the lower layer, a composite reaching law sliding mode control based on an extended state observer (ESO-CRLSMC) is developed to address dual-motor parameter mismatch and speed synchronization issues, thereby ensuring the reliability of the dual-motor system. Finally, hardware-in-the-loop experiments demonstrate that under time-varying disturbances and parameter mismatches, the proposed controller not only ensures vehicle handling stability but also improves steering response speed, robustness, and synchronization performance. Full article

Electric unmanned vehicles applied in complex terrains such as agricultural, forestry, and deep-space exploration scenarios are often required to travel on uneven roads. In particular, during climbing processes, their driving stability and terrain adaptability are of critical importance. To address the above challenges, an electric unmanned vehicle with variable-diameter wheels is proposed. By adjusting the wheel diameter, the vehicle can modify its pitch and roll angles to adapt to uneven terrains. The core research focuses on the relationship between quasi-static stability and wheel diameter variation. First, the configuration and working principle of the electric unmanned vehicle with variable-diameter wheels are introduced, with particular emphasis on the mechanism principle of the novel variable-diameter wheel. A kinematic model between the electric cylinder input and wheel diameter in the variable-diameter wheel is established. On this basis, based on the FASM (Force-Angle Stability Margin)—a stable cone theory, the relationships between stability and wheel diameter variation were investigated separately under lateral, longitudinal, and 45° steering composite conditions on a slope. The results indicate that the unmanned vehicle can achieve omnidirectional attitude adjustment. Finally, the relationship between the electric cylinder input and stability is derived, which can provide a theoretical basis for the quasi-static stability control of outdoor electric unmanned vehicles. Full article

Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer and the vehicle may turn more often, which raises propulsion effort and hurts stability. To reduce these problems, a hybrid path planning method called ${\theta }^{\ast }$-APF-Q is proposed, and it combines global planning, learning-based decisions, and local adjustment in a three-layer structure. First, an any-angle ${\theta }^{\ast }$ global planner is employed to generate a near-optimal backbone trajectory by line-of-sight pruning, thereby reducing redundant waypoints and limiting detours. Second, an enhanced tabular Q-learning model is executed in an expanded eight-direction action space, and policy learning is guided by a multi-objective reward that jointly encourages distance reduction, alignment with ocean current and wind-induced forces for energy saving, smooth heading variation to suppress excessive steering, and maintenance of a safety margin near obstacles. Third, an adaptive artificial potential field (APF) module is used for real-time local correction, providing repulsion in high-risk regions and assisting trajectory smoothing to reduce unnecessary turning operations. A decision bias strategy further couples instantaneous APF forces with long-term state–action values, while the influence weight is adaptively adjusted according to environmental complexity. The algorithm is validated on the randomly generated marine grid maps and on the real-world satellite map scenario, with comparisons against a conventional four-direction Q-learning baseline. Across randomized tests, average path length, turning frequency, and the composite energy indicator are reduced by 22.3%, 55.6%, and 26.4%, respectively, and the success rate increases by 16%. The results indicate that integrating global guidance, adaptive learning, and local reactive decision making supports practical, energy-aware USV navigation. Full article

Reliable steering regulation under varying road curvature and actuator constraints remains a central challenge in autonomous electric vehicles (AEVs). Many exiting approaches rely on reactive error correction or treat preview information solely as a reference adjustment, limiting anticipation and physical consistency. This study proposes a prediction–preview steering control (PSC) framework in which future curvature information within state propagation and constraint handling enables forward-looking steering decisions while respecting dynamic and actuator limits. The method is evaluated using a lateral-heading vehicle model with real-road geometric variation. Experimental results indicate significant improvement in tracking performance, reducing lateral RMSE from 0.1747 m to 0.0074 m with a maximum deviation of 0.0889 m and limiting heading RMSE to 0.0867° (maximum 1.2046°). Steering angle commands remain bounded within ±8.7°, while steering angle rate is maintained within 40–60°/s, ensuring smooth and dynamically admissible operation. The proposed strategy offers a computationally efficient solution for embedded AEV steering systems and demonstrates improved robustness under practical curvature transitions. Full article

To address the stringent cost and performance requirements of commercial Satellite-on-the-Move (SOTM) terminals, we propose a Genetic Algorithm (GA)-based design for a millimeter-wave Phased-Array-Fed Lens (PAFL). This antenna is specifically intended to be the electronic scanning module within a hybrid mechanical–electronic steering architecture. In this hybrid configuration, wide-angle coverage is handled by mechanical positioning, while the PAFL is responsible for high-precision fine tracking and jitter compensation within a critical ±15° field of view. By utilizing a small-scale active array to illuminate a large passive planar lens, this design significantly reduces hardware costs compared to full phased arrays. To mitigate phase aberrations and gain loss inherent in such compact focal-to-diameter (F/D) systems, a two-stage co-optimization strategy is introduced. It globally optimizes the lens phase distribution and subsequently synthesizes feed excitation codebooks to dynamically correct residual errors. A Ka-band prototype comprising an 8 × 8 active feed and a 28 × 28 transmitarray lens was fabricated. Measurements demonstrated stable scanning within the required ±15° range with a gain variation of less than 1.5 dB, achieving a peak directivity of 28.9 dBi and sidelobe levels below −12 dB. Full article

Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency. Current approaches often decouple these factors or exhibit high computational overhead, limiting applicability in real-time or large-scale missions. This work proposes a hierarchical trajectory planning framework designed to address these coupled constraints in an integrated manner. The framework consists of two stages: (i) a current-biased sampling-based planner (CB-RRT*) is introduced to incorporate ocean current information into the path generation process. By leveraging flow field distributions, the planner improves path geometric continuity and reduces steering variations compared with benchmark algorithms; (ii) spatial–temporal alternating optimization is performed within underwater safe corridors, where Bézier curve parameterization is utilized to jointly optimize spatial shapes and temporal profiles, producing dynamically feasible and energy-efficient trajectories. Simulation results in dense obstacle fields, heterogeneous flow environments, and large-scale maps demonstrate that the proposed method reduces the maximum steering angle by up to 63% in downstream scenarios, achieving a mean maximum turning angle of 0.06 rad after optimization. The framework consistently attains the lowest energy consumption across all tests while maintaining an average computation time of 0.68 s in typical environments. These results confirm the framework’s suitability for practical AUV applications, providing a computationally efficient solution for generating safe, kinematically feasible, and energy-efficient trajectories in real-world ocean settings. Full article

Vehicular Ad Hoc Networks (VANETs) enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications for enhancing road safety. However, reliable driver stress assessment remains challenging due to noisy sensing, inter-driver variability, and context dynamics. This paper proposes a Fuzzy-based Driver Stress Detection System (FDSDS) that employs an Interval Type-2 Fuzzy Logic System (IT2FLS) to model uncertainty. The FDSDS considers four complementary inputs—Heart Rate Variability (HRV), Galvanic Skin Response (GSR), Steering Angle Variation (SAV), and Traffic Density (TD)—to estimate Driver Stress Level (DSL). Extensive simulations (14,641 test points) show monotonic associations between DSL and the inputs, which reveal that physiological indicators dominate average influence (finite-difference sensitivity: GSR 0.357, SAV 0.239, TD 0.239, HRV 0.235). Under severe physiological conditions (HRV = 0.1, GSR = 0.9), the system consistently outputs high stress (mean DSL = 0.813; range 0.622–0.958), while favorable physiological conditions (HRV = 0.9, GSR = 0.1) yield low stress even in challenging traffic (range 0.044–0.512). The IT2FLS uncertainty bands widen for intermediate conditions, aligning with the inherent ambiguity of moderate stress states. These results indicate that combining physiological, behavioral, and environmental factors with IT2FLS yields interpreted, uncertainty-aware stress estimates suitable for real-time VANET applications. Full article

This paper presents a 3-bit transmissive intelligent surface (TIS) using a novel technique that employs a unit cell comprising loaded semi-loop dipole resonators. The two resonators are anti-symmetrically oriented along the H-plane, functioning as transmitter and receiver on opposite sides of the TIS. The unit cell, with 13.2 mm periodicity, achieves 360° phase variation in 45° steps while maintaining insertion loss below 2 dB at 10 GHz. A 17 × 17 array TIS is designed using ray tracing and phase shift compensation techniques, with phase profiles distributed across eight discrete varactor states. The implemented TIS demonstrates a 10.8 dB gain enhancement for a horn antenna source at 10 GHz while preserving antenna matching, polarization, and radiation efficiency. The design achieves beam steering capabilities up to 60° with ±2° precision across elevation, azimuth, and inclined angles, maintaining an average steering gain loss of 3 dB over a 400 MHz bandwidth. These characteristics make the proposed design particularly effective for modern wireless coverage extension and tracking applications. Full article

This study investigates the dynamic performance of mountainous crawler tractors during small-radius slope steering, providing theoretical support for power machinery design in hilly and mountainous regions. Addressing the mechanization demands in complex terrains and existing research gaps, a steering dynamics model is established. The model incorporates an amplitude-varied multi-peak cosine ground pressure distribution, employs position vectors and rotation matrices to characterize 3D pose variations in the tractor’s center of mass, and integrates slope angle, soil parameters, vehicle geometry, center-of-mass shift, bulldozing resistance, and sinkage resistance via d’Alembert’s principle. Numerical simulations using Maple 2024 analyzed variations in longitudinal offset of the instantaneous steering center, bilateral track traction forces, and bulldozing resistance with slope, speed, and acceleration. Variable-gradient steering tests on the “Soil-Machine-Crop” Comprehensive Experimental Platform demonstrated model accuracy, with <8% mean error and <12% maximum relative error between predicted and measured track forces. This research establishes a theoretical foundation for predicting, evaluating, and controlling the steering performance/stability of crawler tractors in complex slope conditions. Full article

For mobile agent path planning, traditional path planning algorithms frequently induce abrupt variations in path curvature and steering angles, increasing the risk of lateral tire slippage and undermining operational safety. Concurrently, conventional reinforcement learning methods struggle to converge rapidly, leading to an insufficient efficiency in planning to meet the demand for energy economy. This study proposes LSTM Bézier–Double Deep Q-Network (LB-DDQN), an advanced path-planning framework for mobile agents based on deep reinforcement learning. The architecture first enables mapless navigation through a DDQN foundation, subsequently integrates long short-term memory (LSTM) networks for the fusion of environmental features and preservation of training information, and ultimately enhances the path’s quality through redundant node elimination via an obstacle–path relationship analysis, combined with Bézier curve-based trajectory smoothing. A sensor-driven three-dimensional simulation environment featuring static obstacles was constructed using the ROS and Gazebo platforms, where LiDAR-equipped mobile agent models were trained for real-time environmental perception and strategy optimization prior to deployment on experimental vehicles. The simulation and physical implementation results reveal that LB-DDQN achieves effective collision avoidance, while demonstrating marked enhancements in critical metrics: the path’s smoothness, energy efficiency, and motion stability exhibit average improvements exceeding 50%. The framework further maintains superior safety standards and operational efficiency across diverse scenarios. Full article

To address the challenges of automatic detection caused by the variation of surface normal vectors in automotive steering knuckles, an automatic detection method based on ultrasonic phased array technology is herein proposed. First, a point cloud model of the workpiece was constructed using ultrasonic distance measurement, and Gaussian-weighted principal component analysis was used to estimate the normal vectors of the point cloud. By utilizing the normal vectors, water layer thickness during detection, and the incident angle of the sound beam, the probe pose information corresponding to the detection point was precisely calculated, ensuring the stability of the sound beam incident angle during the detection process. At the same time, in the trajectory planning process, piecewise cubic Hermite interpolation was used to optimize the detection trajectory, ensuring continuity during probe movement. Finally, an automatic detection system was set up to test a steering knuckle specimen with surface circumferential cracks. The results show that the point cloud data of the steering knuckle specimen, obtained using phased array ultrasound, had a relative measurement error controlled within 1.4%, and the error between the calculated probe angle and the theoretical angle did not exceed 0.5°. The probe trajectory derived from these data effectively improved the B-scan image quality during the automatic detection of the steering knuckle and increased the defect signal amplitude by 5.6 dB, demonstrating the effectiveness of this method in the automatic detection of automotive steering knuckles. Full article

The electric solar wind sail (E-sail) is a propellantless propulsion system concept based on the use of a system of very long and thin conducting tethers, which create an artificial electric field that is able to deflect the solar-wind-charged particles in order to generate a net propulsive acceleration outside the planetary magnetospheres. The radial rig of conducting tethers is deployed and stretched by rotating the spacecraft about an axis perpendicular to the nominal plane of the sail. This rapid rotation complicates the thrust vectoring of the E-sail-based spacecraft, which is achieved by changing the orientation of the sail nominal plane with respect to an orbital reference frame. For this reason, some interesting steering techniques have recently been proposed which are based, for example, on maintaining the inertial direction of the spacecraft spin axis or on limiting the excursion of the so-called pitch angle, which is defined as the angle formed by the unit vector perpendicular to the sail nominal plane with the (radial) direction of propagation of the solar wind. In this paper, a different control strategy based on maintaining the pitch angle value constant during a typical interplanetary flight is investigated. In this highly constrained configuration, the spacecraft spin axis can rotate freely around the radial direction, performing a sort of conical motion around the Sun-vehicle line. Considering an interplanetary Earth–Venus or Earth–Mars mission scenario, the flight performance is here compared with a typical unconstrained optimal transfer, aiming to quantify the flight time variation due to the pitch angle value constraint. In this regard, simulation results indicate that the proposed control law provides a rather limited (percentage) performance variation in the case where the reference propulsive acceleration of the E-sail-based spacecraft is compatible with a medium- or low-performance propellantless propulsion system. Full article

In this paper, a methodological framework employing an observer-based nonlinear controller is presented for controlling the lateral velocity of a farm tractor, as well as the yaw velocity of the agricultural implement. This approach relies on measurements obtained from sensors installed on a modern farm tractor, including lateral and longitudinal accelerations, longitudinal velocity, yaw rate, steering angle, and the differential yaw rate between the farm tractor and the implement. The nonlinear observer estimates the longitudinal and lateral velocities of the vehicle, as well as the roll dynamics of the implement, and ensures the exponential convergence of the observed variables. The control objective is formulated to ensure error feedback control, guaranteeing accurate tracking of the lateral velocity and yaw rate of the farm tractor and implement, following the reference patterns for these variables. The reference system is modeled as an “ideal” tractor operating without attachments. To evaluate the proposed controller’s performance, two test maneuvers were conducted. The first test involved the classic U-turn maneuver, commonly executed by tractors, while the second was a double-step maneuver, a standard in ground vehicle testing. Both maneuvers were simulated using MATLAB–Simulink to evaluate the controller’s effectiveness and robustness against parameter variations. Full article

Path planning for intelligent semi-trailers encounters numerous challenges in complex traffic conditions. Serious consequences, such as vehicle rollover, may occur when the traffic conditions change. Therefore, it is vital to consider both the surrounding dynamic traffic conditions and the vehicle’s roll stability during the lane-changing process of intelligent semi-trailers. We propose an innovative path-planning method tailored for intelligent semi-trailers. This path-planning method is designed for semi-trailers on straight-road alignments. Firstly, we employ a fuzzy inference system to process information about surrounding traffic, make lane-changing decisions, and determine the starting point. Secondly, the lane-changing path is generated using a B-spline curve. Subsequently, we apply a particle swarm optimization algorithm to enhance the B-spline curve. Thirdly, we utilize a Transformer model to analyze the nonlinear relationships among information about surrounding traffic, vehicle information, and the roll stability of the intelligent semi-trailer. We establish the roll stability boundary for the vehicle. Finally, we design a multi-objective cost function to select the optimal path. The simulation results demonstrate that the proposed method dynamically adapts the planned path to variations in driving parameters, ensuring trackability while reducing the steering angle, lateral acceleration, and yaw rate. This approach meets the roll stability requirements of intelligent semi-trailers, significantly enhances their stability during lane changing, and provides robust support for safe and efficient operation. Full article

This study presents an innovative control strategy for enabling ships to perform automatic U-turns in restricted waters, with a focus on minimizing energy consumption and reducing wear on the steering gear. The strategy integrates a closed-loop gain-shaping algorithm with nonlinear feedback control, applied to a nonlinear motion mathematical model specifically designed for low-speed operations in shallow waters. The simulations, conducted under a Beaufort wind scale conditions up to No. 5 and water depths of 15 m, demonstrate that ships can successfully execute automatic U-turns within a distance three times their length. The incorporation of nonlinear feedback technology significantly reduces energy consumption and steering gear wear, with specific improvements including a reduction in the average rudder angle by up to 18.26%, a reduction in the mean absolute error (MAE) by up to 3.6%, a reduction in the mean integrated absolute (MIA) by up to 13.55%, and a reduction in the mean total variation (MTV) by up to 36.36%. These enhancements not only optimize the control effect but also extend the service life of the steering gear, thereby contributing to more sustainable maritime operations. Theoretical proofs and Matlab-based simulations validate the effectiveness of the controller, highlighting its potential for energy savings and improved navigational efficiency in challenging maritime environments. Full article