Search Results (255)

In an intelligent driving system, the rationality of driving decisions and the trajectory planning scheme directly determines the safety and stability of the system. Existing research mostly relies on high-definition maps and empirical parameters to estimate road adhesion conditions, ignoring the direct impact of real-time road status changes on the dynamic feasible domain of vehicles. This paper proposes an intelligent driving decision-making and trajectory planning method that comprehensively considers the influence factors of vehicle–road interaction. Firstly, real-time estimation of road adhesion coefficients was achieved based on the recursive least squares method, and a dynamic adhesion perception mechanism was constructed to guide the decision-making module to restrict lateral maneuvering behavior under low-adhesion conditions. A multi-objective lane evaluation function was designed for adaptive lane decision-making. Secondly, a longitudinal and lateral coupled trajectory planning framework was constructed based on the traditional lattice method to achieve smooth switching between lateral trajectory planning and longitudinal speed planning. The planned path is tracked based on a model predictive control algorithm and dual PID algorithm. Finally, the proposed method was verified on a co-simulation platform. The results show that this method has good safety, adaptability, and control stability in complex environments and dynamic adhesion conditions. Full article

Aerodynamic ablation of external control surfaces and structural complexity in hypersonic reentry vehicles (HRVs) pose significant challenges for maneuverability and system reliability. To address these issues, this study develops a novel bank-to-turn (BTT) control strategy integrating a single internal moving mass with differential ailerons, eliminating reliance on ablation-prone elevators/rudders while enhancing internal space utilization. A coupled 7-DOF dynamics model explicitly quantifies inertial-rolling interactions induced by the moving mass, revealing critical stability boundaries for roll maneuvers. To ensure robustness against aerodynamic uncertainties, aileron failures, and high-frequency mass-induced disturbances, a dynamic inversion controller is augmented with an L1 adaptive layer decoupling estimation from control for improved disturbance rejection. Monte Carlo simulations demonstrate: (1) a 20.6% reduction in roll-tracking error (L₂-norm) under combined uncertainties compared to dynamic inversion control, and (2) a 72% suppression of oscillations under aerodynamic variations. Comparative analyses confirm superior transient performance and robustness in worst-case scenarios. This work offers a practical solution for high-maneuverability hypersonic vehicles, with potential applications in reentry vehicle design and multi-actuator system optimization. Full article

Unmanned aerial manipulators (UAMs), composed of unmanned aerial vehicles (UAVs) and manipulators, have great application potential in aerial manipulation like precision inspection, disaster rescue, etc. However, strong dynamic coupling exists between UAVs and manipulators. In addition, UAMs meet external disturbances such as gusts of wind during movements. Also, the control performance metrics, such as tracking accuracy and control stability, are seriously affected. Therefore, a cooperative control method is developed for a UAM system with a UAV and a 2-degree-of-freedom manipulator. First, the Euler–Lagrange formulation is employed to study the UAM dynamics like inertial forces and coupling effects. Then, an integral sliding mode control (ISMC) method with an integral term is developed to enhance robustness and eliminate steady-state errors. Finally, the proposed ISMC method is validated through numerical simulations in Matlab R2024a, introducing comparative analyses with the Proportional–Integral–Derivative (PID) and SMC controllers. The simulation results and the comparative analyses validate the effectiveness of ISMC, showing its superiority over the PID and SMC controllers in handling dynamic coupling and external disturbances, where the overshoot of ISMC is reduced by an average of more than 90%. The ISMC method provides a high-performance control strategy to promote the practical application of UAMs in various aerial manipulation tasks and lays the foundation for further optimizing control methods for more complex UAM systems. Full article

Unmanned Aerial Vehicles (UAVs) are known to have limited payloads, which challenges their widespread use in transporting heavy goods. Meanwhile, collaboration between multiple UAVs in performing such a task may be a promising solution. To address the issues associated with the simultaneous use of UAVs, this paper presents a formation control system for transporting a payload suspended via a cable using two UAVs. The control structure is based on a layered scheme that combines a null-space-based kinematic controller with a PID controller associated with each UAV (quadcopters) with a neural correction system. The null-space supervisor controller is designed to generate the desired velocity for the UAV system to maintain formation. This proposal aims to avoid obstacles, balance the weight distribution across each vehicle, and also reduce the payload trajectory tracking error. The PID controller associated with the neural correction system receives these desired speeds and performs dynamic compensation, taking into account parametric uncertainties and dynamic disturbances caused by the movement of the payload coupled to the UAV systems. The stability analysis of the entire control system is performed using Lyapunov theory. Detailed dynamic models of each UAV in the system, the flexible cables, and the payload are presented in a realistic scenario. Finally, numerical simulations demonstrate the good performance of the UAV system control in formation. Full article

With the increasing requirements for speed and travel distance in tracked vehicles on various terrains and the increasing mass ratio of powertrains, the vibration problem of high-power powertrains becomes a critical challenge. In this paper, in order to reflect on the vibration transmission relationship between the powertrain and the complex carrier, the magnetorheological damping system of a powertrain is studied in a whole vehicle model. The transfer matrix and equations of each component, including the magnetorheological mount, are derived by the Rui Method. Then, the electromechanical coupling multibody dynamic model of the vehicle–powertrain magnetorheological damping system is established. Consequently, the fast solution of vehicle–powertrain vibration characteristics under various road excitations is realized. The dynamic and static coupling characteristics of the powertrain system and the factors affecting its performance are analyzed in a moving vehicle. The simulation results indicate that the vibration reduction performance is the worst in the X-direction, whereas the vibration reduction performance is the best in the Y-direction. Under the E-class road condition at 10 m/s, the RMS acceleration reduction in the powertrain is 41.63% in the Y-direction relative to the chassis. Both the resonant frequency of the powertrain and chassis are 86.93 Hz in the Y-direction. Finally, the accuracy of the results is verified by simulation and driving experiments. The research results can provide theoretical guidance for the design and optimization of the powertrain mount of a tracked vehicle. Moreover, it provides a new technical means of studying the vibration characteristics of a complex multibody system. The simulation results demonstrate notable directional variations in the vibration attenuation performance of the powertrain damping system. Specifically, the X-direction shows the poorest vibration attenuation, whereas the Y-direction exhibits the best damping characteristics. Full article

With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper designs a data-driven optimal bipartite containment tracking control scheme for multi-UAV systems under compound uncertainties. A novel Dynamic Iteration Regulation Strategy (DIRS) is proposed, which enables real-time adjustment of the learning iteration step according to the task-specific demands. Compared with conventional fixed-step data-driven algorithms, the DIRS provides greater flexibility and computational efficiency, allowing for better trade-offs between the performance and cost. First, the optimal bipartite containment tracking control problem is formulated, and the associated coupled Hamilton–Jacobi–Bellman (HJB) equations are established. Then, a data-driven iterative policy learning algorithm equipped with the DIRS is developed to solve the optimal control law online. The stability and convergence of the proposed control scheme are rigorously analyzed. Furthermore, the control law is approximated via the neural network framework without requiring full knowledge of the model. Finally, numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed DIRS-based optimal containment tracking scheme for multi-UAV systems, which can reduce the number of iterations by 88.27% compared to that for the conventional algorithm. Full article

Research in marine hydrographic environmental monitoring continues to deepen, necessitating a hardware platform capable of traversing air–water interfaces to collect vertical gradient parameters across oceanographic profiles. This paper proposes a deeply integrated heterogeneous monitoring platform for marine hydrological vertical profiling, addressing the functional limitations of conventional unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) in subsurface monitoring. By co-designing a hybrid aerial underwater vehicle (HAUV) with cross-domain capabilities and a USV, the system leverages USVs for long-endurance surface operations and HAUVs for high-speed vertical column monitoring. Key innovations include (1) a distributed collaborative architecture enabling “Air–Sea–Air” cyclic operations; (2) dynamic modeling of HAUV-USV interactions incorporating aerodynamic and hydrodynamic coupling; (3) an MPC-based collaborative tracking algorithm for real-time USV pursuit under marine disturbances; and (4) a vision-guided synchronous landing strategy achieving decimeter-level docking accuracy in bad conditions. Simulation experiments validate the system’s efficacy in trajectory tracking and precision landing. This work bridges the critical gap in marine vertical profile monitoring while demonstrating robust cross-domain coordination. Full article

This study focuses on optimizing track and grouser structural parameters to enhance UATV drawbar pull, particularly under soft soil conditions. A numerical soil thrust model for single-track shoes was developed based on track–soil interaction mechanics, revealing distinct mechanistic roles: track structural parameters (length/width) govern pressure–sinkage relationships at the track base, while grouser structural parameters (height, spacing, V-shaped angle) dominate shear stress–displacement dynamics on grouser shear planes. A novel DEM-MBD coupling simulation framework was established through soil parameter calibration and multi-body dynamics modeling, demonstrating that soil thrust increases with grouser height and V-shaped angle, but decreases with spacing, with grouser height exhibiting the highest sensitivity. A soil bin test validated the numerical model’s accuracy and the coupling method’s efficacy. Parametric optimization via the Whale Optimization Algorithm (WOA) achieved a 55.86% increase in drawbar pull, 40.38% reduction in ground contact pressure and 57.33% improvement in maximum gradability. These advancements substantially improve the tractive performance of UATVs in soft beach terrains. The proposed methodology provides a systematic framework for amphibious vehicle design, integrating numerical modeling, high-fidelity simulation, and experimental validation. Full article

To enhance road-testing safety for autonomous driving robotic vehicles (ADRVs), collision avoidance with sudden obstacles is essential during testing processes. This paper proposes an upper-level collision avoidance strategy integrating model predictive control (MPC) and improved artificial potential field (APF). The kinematic model of the driving robot is established, and a vehicle dynamics model considering road curvature is used as the foundation for vehicle control. The improved APF constraints are constructed. The boundary constraint uses a three-circle vehicle shape suitable for roads with arbitrary curvatures. A unified obstacle potential field constraint is designed for static/dynamic obstacles to generate collision-free trajectories. An auxiliary attractive potential field is designed to ensure stable trajectory recovery after obstacle avoidance completion. A multi-objective MPC framework coupled with artificial potential fields is designed to achieve obstacle avoidance and trajectory tracking while ensuring accuracy, comfort, and environmental constraints. Results from Carsim-Simulink and semi-physical experiments validate that the proposed strategy effectively avoids various obstacles under different road conditions while maintaining reference trajectory tracking. Full article

To address the adverse effects of Tuned Inertia Dampers (TIDs) on track slab vibrations while controlling high-frequency rail vibrations, a hybrid Finite Element-Statistical Energy Analysis (FE-SEA) method is developed for modeling the vehicle-track-bridge coupled system. Short-wavelength track irregularities are introduced as high-frequency excitation, and the accuracy and efficiency of this method are validated by comparison with the traditional finite element method (FEM). A vibration control model for track-bridge structures incorporating TIDs is designed, and the effects of the TID’s inertance, stiffness, and damping coefficients on the vertical acceleration responses of the rail and track slab are investigated in detail. The study reveals that although TIDs effectively reduce rail vibrations, they may induce adverse effects on track slab vibrations. Using the vibration acceleration amplitudes of both the rail and track slab as dual control objectives, a multi-objective optimization model is established, and the TID’s optimal parameters are determined using a multi-objective genetic algorithm. The results show that the optimized TID parameters reduce rail acceleration amplitudes by 16.43% and improve the control efficiency by 12.45%, while also addressing the negative effects on track slab vibration. The track slab’s vibration acceleration is reduced by 5.47%, and the vertical displacement and acceleration of the vehicle body are reduced by 14.22% and 47.5%, respectively, thereby enhancing passenger comfort. This study provides new insights and theoretical guidance for vibration control analysis in vehicle-track-bridge coupled systems. Full article

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

This paper presents a flight control framework based on neural network Model Predictive Control (NN-MPC) to tackle the challenges of acceleration command tracking for supersonic vehicles (SVs) in complex flight environments, addressing the shortcomings of traditional methods in managing nonlinearity, random disturbances, and real-time performance requirements. Initially, a dynamic model is developed through a comprehensive analysis of the vehicle’s dynamic characteristics, incorporating strong cross-coupling effects and disturbance influences. Subsequently, a predictive mechanism is employed to forecast future states and generate virtual control commands, effectively resolving the issue of sluggish responses under rapidly changing commands. Furthermore, the approximation capability of neural networks is leveraged to optimize the control strategy in real time, ensuring that rudder deflection commands adapt to disturbance variations, thus overcoming the robustness limitations inherent in fixed-parameter control approaches. Within the proposed framework, the ultimate uniform bounded stability of the control system is rigorously established using the Lyapunov method. Simulation results demonstrate that the method exhibits exceptional performance under conditions of system state uncertainty and unknown external disturbances, confirming its effectiveness and reliability. Full article

LCL (inductor–capacitor–inductor) filters are widely used in grid-connected inverters, particularly in electric vehicle (EV) battery-to-grid systems, for harmonic suppression but introduce resonance issues that compromise stability. This study presents a novel sensorless active damping strategy based on μ-synthesis control for EV batteries connected to the grid via LCL filters, eliminating the need for additional current sensors while preserving harmonic attenuation. A comprehensive state–space and process noise model enables accurate capacitor current estimation using only grid current and point-of-common-coupling (PCC) voltage measurements. The proposed method maintains robust performance under ±60% LCL parameter variations and integrates a proportional-resonant (PR) current controller for resonance suppression. Hardware-in-the-loop (HIL) validation demonstrates enhanced stability in dynamic grid conditions, with total harmonic distortion (THD) below 5% (IEEE 1547-compliant) and current tracking error < 0.06 A. Full article

Unmanned surface vehicles (USVs) course control research constitutes a vital branch of ship motion control studies and serves as a key technology for the development of marine critical equipment. Aiming at the problems of model uncertainties, external marine disturbances, performance optimization, and actuator constraints encountered by the autopilot system, this paper proposes a composite disturbance cancellation optimized control method based on fuzzy reinforcement learning. Firstly, a coupling design of the finite-time disturbance observer and fuzzy logic system is conducted to estimate and reject the composite disturbance composed of internal model uncertainty and ocean disturbances. Secondly, a modified backstepping control technique is employed to design the autopilot controller and construct the error system. Based on the designed performance index function, the fuzzy reinforcement learning is utilized to propose an optimized compensation term for the error system. Meanwhile, to address the actuator saturation issue, an auxiliary system is introduced to modify the error surface, reducing the impact of saturation on the system. Finally, the stability of the autopilot system is proved using the Lyapunov stability theory. Simulation studies conducted on the ocean-going training ship “Yulong” demonstrate the effectiveness of the proposed algorithm. Under the strong and weak ocean conditions designed, this algorithm can ensure that the tracking error converges within 7 s. Full article

To address the diverse requirements of accuracy and robustness in integrated navigation for unmanned aerial vehicles, an interactive robust filter algorithm that integrates the interactive multiple model concept and leverages the complementary applicability of the strong tracking filter and the smooth variable structure filter is proposed. The algorithm operates as follows: the strong tracking filter, along with the smooth variable structure filter, operates side by side with distinct models. During the filter process, the likelihood function is utilized to update the filter probabilities and determine the weights for each one of the filters. Input interaction, coupled with output fusion, is then carried out. The results of the experiments validate that the presented interactive filter algorithm significantly reduces estimation errors. When confronted with complex, dynamic noise environments and system uncertainties, it retains high-precision state estimation while demonstrating markedly improved robustness. The proposed interactive robust filter algorithm is compared against the strong tracking filter, smooth variable structure filter, and strong tracking smooth filter. Taking the strong tracking smooth filter, which has the highest accuracy among the three, as the reference baseline, the presented interactive robust filter algorithm achieves over 16% improvement in velocity accuracy and over 40% improvement in position accuracy. Full article