Search Results (1,820)

To further enhance the economic performance of fuel cell vehicles (FCVs), this study develops a model-adaptive model predictive control (MPC) strategy. This strategy leverages the dynamic relationship between proton exchange membrane fuel cell (PEMFC) output characteristics and temperature to track its optimal operating temperature (OOT), addressing challenges of temperature control accuracy and high energy consumption in the PEMFC thermal management system (TMS). First, PEMFC and TMS models were developed and experimentally validated. Subsequently, the PEMFC power–temperature coupling curve was experimentally determined under multiple operating conditions to serve as the reference trajectory for TMS multi-objective optimization. For MPC controller design, the TMS model was linearized and discretized, yielding a predictive model adaptable to different load demands for stack temperature across the full operating range. A multi-constrained quadratic cost function was formulated, aiming to minimize the deviation of the PEMFC operating temperature from the OOT while accounting for TMS parasitic power consumption. Finally, simulations under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions evaluated the OOT tracking performance of both PID and MPC control strategies, as well as their impact on stack efficiency and TMS energy consumption at different ambient temperatures. The results indicate that, compared to PID control, MPC reduces temperature tracking error by 33%, decreases fan and pump speed fluctuations by over 24%, and lowers TMS energy consumption by 10%. These improvements enhance PEMFC operational stability and improve FCV energy efficiency. Full article

The piston-type expander (PTE), as the primary output component, significantly influences the performance of an energy storage system. This paper proposes a non-cam variable valve actuation system for the PTE, supported by a mathematical model. An enhanced S-curve trajectory planning method is used to design the valve lift curve. The study investigates the effects of various valve lift design parameters on output power and efficiency at different rotational speeds, employing orthogonal design and SPSS Statistics 27 (Statistical Product and Service Solutions) simulations. A grey comprehensive evaluation method is used to identify optimal valve lift parameters for each speed. The results show that valve lift parameters influence PTE performance to varying degrees, with intake duration having the greatest effect, followed by maximum valve lift, while intake end time has the least impact. The non-cam PTE outperforms the cam-based PTE. At 800 rpm, the optimal design yields 7.12 kW and 53.5% efficiency; at 900 rpm, 8.17 kW and 50.6%; at 1000 rpm, 9.2 kW and 46.8%; and at 1100 rpm, 12.09 kW and 41.2%. At these speeds, output power increases by 18.37%, 11.42%, 11.62%, and 9.82%, while energy efficiency improves by 15.01%, 15.05%, 14.24%, and 13.86%, respectively. Full article

The dynamic model of a wheeled wall-climbing robot exhibits stage-specific changes when traversing different types of obstacles and during various stages of obstacle negotiation. Previous studies often employed remote control methods for obstacle-crossing control, which fail to dynamically adjust the torque distribution of magnetic wheels in response to real-time changes in the dynamic model. This limitation makes it challenging to precisely control the robot’s speed and attitude angles during the obstacle-crossing process. To address this issue, this paper first establishes a staged dynamic model for the wall-climbing robot under typical obstacle-crossing scenarios, including steps, 90° concave corners, 90° convex corners, and thin plates. Secondly, an adaptive controller based on a radial basis function neural network (RBFNN) is designed to effectively compensate for variations and uncertainties during the obstacle-crossing process. Finally, comparative simulations and physical experiments demonstrate the effectiveness of the proposed method. The experimental results show that this method can quickly respond to the dynamic changes in the model and accurately track the trajectory, thereby improving the control precision and stability during the obstacle-crossing process. Full article

It is well established that combining exercise with medication may benefit functionality in individuals with PD (Parkinson’s disease). However, the long-term evolution of gait biomechanics under this combination remains poorly understood. Objectives: This study aims to analyze the evolution of spatiotemporal gait parameters, kinetics, and kinematics throughout a long-term exercise program conducted in water and on dry land. Methods: We have compared the trajectories of biomechanical variables across the treatment phases using statistical parametric mapping (SPM). A cohort of fourteen individuals with PD (mean age: 65.6 ± 12.1 years) participated in 24 sessions of aquatic exercises over three months, followed by a three-month retention phase, and then 24 additional sessions of land-based exercises. Three-dimensional gait data and spatiotemporal parameters were collected before and after each phase. Two-way ANOVA with repeated measures was used to compare spatiotemporal parameters. Results: The walking speed increased while the duration of the double support phase decreased. Additionally, the knee extensor moment consistently increased in the entire interval from midstance to midswing (20% to 70% of the stride period), approaching normal gait patterns. Regarding kinematics, significant increases were observed in both hip and knee flexion angles. Furthermore, the abnormal ankle dorsiflexion observed at the foot strike disappeared. Conclusions: These findings collectively suggest positive adaptations in gait biomechanics during the observation period. Full article

The extended operation of high-speed railways has led to an increased incidence of tunnel lining defects, with falling debris posing a significant safety threat. Within tunnels, single-train passage and trains-crossing events constitute the most frequent operational scenarios, both generating extreme aerodynamic environments that alter debris trajectories from free fall. To systematically investigate the aerodynamic differences and underlying mechanisms governing falling debris behavior under these two distinct conditions, a three-dimensional computational fluid dynamics (CFD) model (debris–air–tunnel–train) was developed using an improved delayed detached eddy simulation (IDDES) turbulence model. Comparative analyses focused on the translational and rotational motions as well as the aerodynamic load coefficients of the debris in both single-train and trains-crossing scenarios. The mechanisms driving the changes in debris aerodynamic behavior are elucidated. Findings reveal that under single-train operation, falling debris travels a greater distance compared with trains-crossing conditions. Specifically, at train speeds ranging from 250–350 km/h, the average flight distances of falling debris in the X and Z directions under single-train conditions surpass those under trains crossing conditions by 10.3 and 5.5 times, respectively. At a train speed of 300 km/h, the impulse of C_Fx and C_Fz under single-train conditions is 8.6 and 4.5 times greater than under trains-crossing conditions, consequently leading to the observed reduction in flight distance. Under the conditions of trains crossing, the falling debris is situated between the two trains, and although the wind speed is low, the flow field exhibits instability. This is the primary factor contributing to the reduced flight distance of the falling debris. However, it also leads to more pronounced trajectory deviations and increased speed fluctuations under intersection conditions. The relative velocity (CRV) on the falling debris surface is diminished, resulting in smaller-scale vortex structures that are more numerous. Consequently, the aerodynamic load coefficient is reduced, while the fluctuation range experiences an increase. Full article

Accurate accounting of residual film recovery operation areas is essential for supporting targeted implementation of white pollution control policies in cotton fields and serves as a critical foundation for data-driven prevention and control of soil contamination. To address the reliance on manual screening during preprocessing in traditional residual film recovery area calculation methods, this study proposes a DBSCAN-MFI field-road trajectory segmentation method. This approach combines DBSCAN density clustering with multi-feature inference. Building on DBSCAN clustering, the method incorporates a convex hull completion strategy and multi-feature inference rules utilizing speed-direction feature filtering to automatically identify and segment field and road areas, enabling precise operation area calculation. Experimental results demonstrate that compared to DBSCAN, OPTICS, the Grid-Based Method, and the DBSCAN-FR algorithm, the proposed algorithm improves the F1-Score by 7.01%, 7.13%, 7.28%, and 4.27%, respectively. Regarding the impact on operation area calculation, segmentation accuracy increased by 23.61%, 25.14%, 20.71%, and 6.87%, respectively. This study provides an effective solution for accurate field-road segmentation during mechanical residual film recovery operations to facilitate subsequent calculation of the recovered area. Full article

Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain–computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency’s base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application. Full article

The southeastern coastal region of China has long been affected by typhoon disasters, which pose significant threats to the safety of offshore structures. Therefore, predicting extreme wind speeds corresponding to various return periods on the basis of limited typhoon samples is particularly important for wind-resistant design. This study systematically predicts extreme typhoon wind speeds for various return periods and quantitatively assesses the sensitivity of key parameters by employing a Monte Carlo stochastic simulation framework integrated with a typhoon straight-line trajectory model and the Yan Meng wind field model. Focusing on Guangzhou (23.13° N, 113.28 °E), a representative coastal city in southeastern China, this research establishes a modular analytical framework that provides generalizable solutions for typhoon disaster assessment in coastal regions. The probabilistic wind load data generated by this framework significantly increases the cost-effectiveness and safety of wind-resistant structural design. Full article

This paper proposes a novel hierarchical control architecture that combines Model Predictive Control (MPC) with Explicit Model-Following Control (EMFC) to enable accurate and efficient trajectory tracking for quadrotor electric Vertical Takeoff and Landing (eVTOL) aircraft operating in urban environments. The approach addresses the challenges of strong nonlinear dynamics, multi-axis coupling, and stringent safety constraints by separating the planning task from the fast-response control task. The MPC layer generates constrained velocity and yaw rate commands based on a simplified inertial prediction model, effectively reducing computational complexity while accounting for physical and operational limits. The EMFC layer then compensates for dynamic couplings and ensures the rapid execution of commands. A high-fidelity simulation model, incorporating rotor flapping dynamics, differential collective pitch control, and enhanced aerodynamic interference effects, is developed to validate the controller. Four representative ADS-33E-PRF tasks—Hover, Hovering Turn, Pirouette, and Vertical Maneuver—are simulated. Results demonstrate that the proposed controller achieves accurate trajectory tracking, stable flight performance, and full compliance with ADS-33E-PRF criteria, highlighting its potential for autonomous urban air mobility applications. Full article

In order to improve the lubrication efficiency in the bearing cavity, this study establishes a simulation model of the fluid domain of the bearing cavity based on the computational fluid dynamics (CFD) method and systematically studies the flow characteristics of the lubricant and its lubrication mechanism in the high-speed rotary bearing. In the process of high-speed bearing operation, the lubricant is subject to the combined effect of centrifugal force and contact pressure, gradually spreads to both sides of the steel ball, and forms a stable oil film after injection from the nozzle. However, due to the influence of high pressure distribution in the contact area, the actual formation of the oil film coverage is relatively limited. In order to further optimize the lubrication effect, this study focuses on investigating the influence law of different injection speeds and rotational speeds on the bearing air curtain effect. The results of the study show that when the air curtain effect is enhanced, there will be significant shear interference on the trajectory of the lubricant, which is manifested in the phenomenon of “buckling” at the end of the lubricant, thus reducing the lubrication efficiency. To address this problem, this study innovatively proposes the air curtain obstruction coefficient K as a quantitative evaluation index, and through numerical simulation, it is found that the lubricant can effectively overcome the air curtain obstruction and achieve a better lubrication coverage when the value of K is reduced to below 0.4. Based on this finding, the study further confirmed that the lubrication efficiency of bearings can be significantly improved under different operating conditions by rationally regulating the injection rate. Full article

Addressing lateral-directional control challenges during unmanned aerial vehicle (UAV) landing in complex wind fields, this study proposes a drift angle control strategy that integrates coordinated heading and trajectory regulation. An adaptive radius optimization method for the Dubins approach path is designed using wind speed estimation. By developing a wind-coupled flight dynamics model, we establish a roll angle control loop combining the $L_1$ nonlinear guidance law with Linear Active Disturbance Rejection Control (LADRC). Simulation tests against conventional sideslip approach and crab approach, along with flight tests, confirm that the proposed autonomous landing system achieves smoother attitude transitions during landing while meeting all touchdown performance requirements. This solution provides a theoretically rigorous and practically viable approach for safe UAV landings in challenging wind conditions. Full article

Fin-stabilized guided rockets exhibit ballistic characteristics such as low initial velocity, high flight altitude, and long flight duration, which render their impact point accuracy and flight stability highly susceptible to the influence of wind. In this paper, the four-dimensional nonlinear angular motion equations describing the changes in attack angle and the law of axis swing of a dual-spin rocket are established, and the phase trajectory and equilibrium point stability characteristics of the nonlinear angular motion system under windy conditions are analyzed. Aiming at the problem that the equilibrium point of the angular motion system cannot be solved analytically with the change in wind speed, a phase trajectory projection sequence method based on the Poincaré cross-section and stroboscopic mapping is proposed to analyze the effect of wind on the angular motion bifurcation characteristics of a dual-spin rocket. The possible instability of angular motion caused by nonlinear aerodynamics under strong wind conditions is explored. This study is of reference significance for the launch control and aerodynamic design of guided rockets in complex environments. Full article

Driverless electric formula racing cars are affected by nonlinear vehicle characteristics, perturbations, and parameter uncertainties during races, which can cause problems such as low accuracy and instability in trajectory tracking. Aiming to address such problems, this paper proposes a control method combining a prescribed performance control with adaptive backstepping fuzzy control (PPC-ABFC) to solve the aforementioned issues and improve the trajectory tracking accuracy and stability of racing cars. This control method is achieved by constructing a combined error model and confining the error within a prescribed performance function. The nonlinear terms, disturbances, and unknown parameters of the model are approximated by a fuzzy logic system (FLS). An adaptive parameter update law is designed to update the learning parameters in real time. The virtual control law and the real control law were designed by using the backstepping method. The stability of the PPC-ABFC closed-loop system was rigorously proved by applying the Lyapunov stability theory. Finally, simulations were conducted to compare the proposed PPC-ABFC method with other algorithms at different speeds. The results demonstrated that the PPC-ABFC method effectively enhances the trajectory tracking performance of driverless electric formula racing cars. Full article

In response to the issues of severe pitch oscillation and unstable roll attitude present in existing reinforcement learning-based aircraft cruise control methods during dynamic maneuvers, this paper proposes a precise control method for aircraft cruising based on proximal policy optimization (PPO) with nonlinear attitude constraints. This method first introduces a combination of long short-term memory (LSTM) and a fully connected layer (FC) to form the policy network of the PPO method, improving the algorithm’s learning efficiency for sequential data while avoiding feature compression. Secondly, it transforms cruise control into tracking target heading, altitude, and speed, achieving a mapping from motion states to optimal control actions within the policy network, and designs nonlinear constraints as the maximum reward intervals for pitch and roll to mitigate abnormal attitudes during maneuvers. Finally, a JSBSim simulation platform is established to train the network parameters, obtaining the optimal strategy for cruise control and achieving precise end-to-end control of the aircraft. Experimental results show that, compared to the cruise control method without dynamic constraints, the improved method reduces heading deviation by approximately 1.6° during ascent and 4.4° during descent, provides smoother pitch control, decreases steady-state altitude error by more than 1.5 m, and achieves higher accuracy in overlapping with the target trajectory during hexagonal trajectory tracking. Full article

In modern vehicles, Autonomous Driving Systems (ADSs) are designed to operate partially or fully without human intervention. The ADS pipeline comprises multiple layers, including sensors, perception, localization, mapping, path planning, and control. The Robot Operating System (ROS) is a widely adopted framework that supports the modular development and integration of these layers. Among them, the path-planning and control layers remain particularly challenging due to several limitations. Classical path planners often struggle with non-smooth trajectories and high computational demands. Meta-heuristic optimization algorithms have demonstrated strong theoretical potential in path planning; however, they are rarely implemented in real-time ROS-based systems due to integration challenges. Similarly, traditional PID controllers require manual tuning and are unable to adapt to system disturbances. This paper proposes a ROS-based ADS architecture composed of eight integrated nodes, designed to address these limitations. The path-planning node leverages a meta-heuristic optimization framework with a cost function that evaluates path feasibility using occupancy grids from the Hector SLAM and obstacle clusters detected through the DBSCAN algorithm. A dynamic goal-allocation strategy is introduced based on the LiDAR range and spatial boundaries to enhance planning flexibility. In the control layer, a modified Pure Pursuit algorithm is employed to translate target positions into velocity commands based on the drift angle. Additionally, an adaptive PID controller is tuned in real time using the Differential Evolution (DE) algorithm, ensuring robust speed regulation in the presence of external disturbances. The proposed system is practically validated on a four-wheel differential drive robot across six scenarios. Experimental results demonstrate that the proposed planner significantly outperforms state-of-the-art methods, ranking first in the Friedman test with a significance level less than 0.05, confirming the effectiveness of the proposed architecture. Full article