The water-jet propeller is main propulsion plant of amphibious vehicle when it navigates on the surface.There are significant differences in the vehicle body attitude and the inflow conditions of propeller at different navigation depths in the land-water interface zone,which significantly affects the propulsion performance.In order to study the mutual influence of amphibious vehicle and water-jet propeller under water depth conditions,this paper takes water jet propulsion amphibious vehicle as the research object,and uses the finite volume method,the shear stress transport (SST) turbulence model,the volume of fluid (VOF) two-phase flow model and the dynamic fluid body interaction (DFBI) motion model to numerically calculate the hydrodynamic performance of the pump-vehicle-integrated amphibious vehicle at different rotational speeds of water-jet propeller under different water depth conditions.The grid uncertainty is analyzed,and the accuracy of the algorithm is verified by comparing the calculated values with the experimental results.The motion law,flow field distribution characteristics and hydrodynamic performance of the pump-vehicle-integrated amphibious vehicle under different navigation conditions were obtained through analysis.The results show that,in the deep water environment,the amphibious vehicle is in the drainage sailing state,and the body attitude of vehicle changes greatly when sailing at a low speed,which affects the stability of the axial flow of fluid in the propeller; the body sailing state of vehicle and the flow rate and head of propeller are relatively stable when sailing at a high speed.In shallow water environment,the amphibious vehicle is in subcritical state when sailing in low speed and has larger longitudinal inclination and sinking amount than those in deep water environment,and the resistance of the vehicle body is increased by 13.65% on average.When sailing at high speed,the amphibious vehicle enters into the supercritical state,the vehicle body floats up rapidly,and the sailing speed is the same as that under the deep water condition.And after the stabilisation,the amplitude of attitude change is small,and the stability of propeller performance is improved.In the shallow water environment with different depths,the vehicle body in the shallower water environment is more obviously affected by the shallow water effect and is subjected to greater resistance during low-speed navigation,while there is no obvious difference in the hydrodynamic performance of amphibious vehicle at different depths during high-speed navigation.Therefore,the amphibious vehicle should increase the output power of the propeller when passing through the shallow water area and quickly enters the supercritical state to reduce the influence of shallow water effect on it.

To evaluate the performance of tracked vehicles,a multiple road surfaces-three-dimensional driving cycle construction method based on micro-motion segments is proposed.This method aims to address the issues of various types of road surfaces,longer short-trip segments,and the numerous dimensions of influencing factors in the construction of driving cycles for tracked vehicles.The collected driving data of tracked vehicles is processed,and the three-dimensional data such as speed,angular velocity,and ground resistance coefficient are extracted.The K-means clustering method is used to categorize the driving segments into three typical road surfaces:paved road,gravel road,and undulating dirt road.The short-trip segments are divided into micro-motion segments based on their minimum weighted three-dimensional variation rate,and the feature extraction and clustering analysis are made for the short-trip segments.A three-dimensional driving cycle is constructed using the Markov transition probability of the micro-motion segments,and a corresponding comprehensive evaluation system is proposed.The total duration of the constructed driving cycle is approximately 2000 seconds,and the average feature coverage rate of three types of road surfaces is up to 94.63%.This driving cycle accurately reflects the driving characteristics of tracked vehicles and serves as an effective tool for simulation and bench testing of tracked vehicles.

The wide-area imaging becomes difficult due to the small overlapping area of multi-camera images,large parallax,and complex and changeable feature information in practical application scenarios when a multi-camera detection system is used to provide environmental perception for military vehicles such as tanks and armored vehicles.A wide-area real-time imaging algorithm based on ChArUco board calibration is proposed,and a wide-area visual enhancement system for military vehicles is designed for drivers to perform real-time imaging of the external environment in a closed compartment with limited vision.This algorithm takes advantage of the characteristic that the ChArUco board can still be accurately calibrated under occlusion conditions to achieve the accurate calibration of a small-overlapping multi-camera system.Projection transformation is carried out through calibration parameters to avoid the interference of image feature information and effectively deal with complex feature scenarios.At the same time,the parallax in the overlapping area is eliminated by the fusion of optical flow relationships,and a wide-area real-time imaging is achieved by using the projection look-up table and parallel acceleration optimization method.This algorithm is deployed on a mobile computing platform to form a complete wide-area visual enhancement system.The experimental results show that this system and algorithm can meet the wide-area real-time imaging requirements of various scenarios and improve the visual perception ability of military vehicles in complex environments.

Aiming at the problem that the stabilization system of unmanned vehicle-mounted guns is affected by the complex nonlinearity and random disturbances during moving,a composite control strategy combining active disturbance rejection adaptive control is proposed.The electromechanical coupling dynamic equations of the unmanned vehicle-mounted gun stabilization system, which take into accountconsidering the actuator dynamics and model uncertainties,are established.Based on the backstepping approach,the adaptive control is ingeniously integrated with the extended state observer (ESO),constructing the parameter adaptation laws to update the unknown parameters of the system online.A dual-channel ESO is used to estimate the matched and unmatched disturbances in real time and provide feedforward compensation.Since the parameter uncertainties of the stabilization system are mainly addressed by adaptive technology,the learning burden of ESO is further reduced,improving the tracking performance of the stabilization system during moving and avoiding the impact of high-gain feedback.The stability analysis based on the Lyapunov function indicates that the asymptotic control of the vehicle-mounted gun can be achieved when only constant disturbances are present,and even in the presence of time-varying uncertainties,the prescribed transient performance and tracking accuracy can still be ensured.Comparative co-simulations and simulation tests demonstrate the effectiveness and feasibility of the active disturbance rejection adaptive composite control strategy.

To address the limitations of existing traversability analysis methods,which often suffer from incomplete obstacle recognition and poor generalization in complex off-road environments,this paper proposes an obstacle recognition method based on open-vocabulary semantic segmentation.The method extracts the semantic labels of obstacles and terrain around the vehicle,enabling effective identification of previously unseen obstacles in unstructured environments.The method is validated on the datasets in real-world experiments,demonstrating its stability and comprehensive recognition capability.On this basis,a multi-layer 2.5D map is constructed by integrating semantic labels with 3D point clouds.The traversability level of a terrain is preliminarily classified according to semantic labels.And then the terrain smoothness is quantified based on ground elevation,and the geometric parameters of special environmental features (e.g.,vertical wall) are measured.Furthermore,the driving posture of vehicle is predicted by incorporating the geometric configuration of a tracked vehicle,thereby quantifying the coupling relationship among static slope stability,semantic terrain categories and geometric attributes.A cost function is then designed to jointly assess the traversal risk and cost of vehicle,ultimately generating a vehicle-centric traversability map.The effectiveness and reliability of the proposed method are verified by comparing it with similar methods,which enhances data support for decision-making,planning,and control of unmanned tracked platforms.

Trajectory tracking is a crucial functionality of the autonomous driving control system.The vehicle dynamics model has a significant impact on trajectory tracking performance,however,there is a conflict between model complexity and solving efficiency,often leading to insufficient tracking accuracy under nonlinear conditions.To address this challenge,this paper proposes a model predictive control method based on Gaussian process regression (GPR) for trajectory tracking.A simplified model is used to ensure solving efficiency,and GPR model is employed to compensate for the vehicle model,thereby enhancing the trajectory tracking performance.First,a vehicle state fusion estimation method based on the single-track dynamics model is developed to obtain the GPR compensation model.A trajectory tracking error model is developed.Based on the trajectory tracking error from vehicle dynamics model,the iterative equation for GPR error compensation within the predictive horizon is derived to dynamically compensate for model errors in the vehicle state prediction for achieving the trajectory tracking control.Finally,a real-vehicle validation platform is constructed to validate the proposed method under typical driving conditions.The proposed method is compared with other predictive control methods without GPR compensation.The results show the proposed method achieves a significant improvement in trajectory tracking accuracy.Specifically,the lateral and heading errors are reduced by 33.3% and 27.9%,respectively.Furthermore,the vehicle comfort performance is also improved,and the mean lateral acceleration and yaw rate are reduced by 17.1% and 21.7%,respectively.

Multi-axle articulated wheeled vehicles are prone to occurring “tail amplification” effects and lateral instability during trajectory tracking.The study of stable trajectory tracking for multi-axle articulated wheeled vehicles is of significant importance.For the multi-axle articulated wheeled vehicle,a 7-degree-of-freedom vehicle dynamics model is established,and a bi-level strategy trajectory tracking control method is proposed.The upper-level strategy is used to address the tractor trajectory tracking problem,while the lower-level strategy is used to optimize the trailer trajectory tracking,ensuring precise tracking for both tractor and trailer.To guarantee the real-time computation of the control strategy,the upper-level trajectory tracking strategy is solved using a finite-horizon approximate dynamic programming approach,and the online optimization problem is transformed into an offline pre-solution of parameters,thus reducing the time required for online solving.This approach significantly reduces online computation time.Co-simulation experiments with high fidelity simulation software demonstrate that the proposed method is used to improve the trajectory tracking accuracy of multi-axle articulated wheeled vehicles by 12.82%,and keep a single-step solution time below 10ms,enhancing computational efficiency by three orders of magnitude compared with the model predictive control algorithm.

The launch of Mars Ascent Vehicle (MAV) is the key technology to realize China's 2030 Mars Sample Return.To study the oblique cold launch technology of MAV,based on the theory of interior ballistics of catapults,a parallel recoilless interior ballistic scheme is designed to meet the launch conditions of Mars.Based on the discrete element method,a discrete element model of Mars soil is established.Additionally,a rigid-flexible coupling dynamics model of the launcher system is constructed.Finally,based on the launch dynamics theory,the entire launch process of the MAV under different working conditions is simulated and analyzed.The results show that the internal ballistics scheme obtained can meet the launch requirements.The recoilless design plays an important role in ensuring the stability of the launch vehicle.Discretization of soil helps to characterize the dynamic behavior of Martian soil.The larger the ground inclination angle,the worse the stability and launch accuracy.Among the four directions at the same inclination angle,the stability of the launch facing uphill is the worst.The “virtual leg” phenomenon can reduce launch stability and may even cause the launch device to topple,leading to launch failure.

The trajectory planning online applications for the formation transformation,impact time and terminal entry angle constraint of unpowered gliding vehicle clusters during the re-entry phase are studied.A real-time coordinated trajectory planning method for unpowered gliding vehicle cluster based on master-slave architecture is proposed.The trajectories of the vehicles are first decoupled into longitudinal and lateral planes,and a segmented function-based angle-of-attack velocity profile method is developed to handle the nonlinear constraints encountered during the re-entry phase.A cooperative control method based on time-varying heading angles is then proposed,which involves solving differential equations to generate a trajectory that satisfies the formation transformation and the impact time and entry angle constraints under quasi-equilibrium glide conditions.An online adjustment strategy is further introduced to accommodate the real-time updates to the target area,and the feasibility of the proposed method for online application is demonstrated.Simulated results indicate that the proposed method can achieve real-time coordinated trajectory planning across various flight scenarios,thus demonstrating its robustness,adaptability to multiple cooperative control schemes,and practical applicability to unpowered gliding vehicle clusters.

Unmanned aerial vehicle (UAV) formation can execute complex collective tasks and reduce the risk and operation difficulty of a single UAV. A distributed formation compound control method with the leader-follower structure is designed based on the prescribed-time stability theory and multi-agent consensus theory for the control of UAV swarm formation in three-dimensional scene.Firstly,a multi-UAVs kinematic model is established by analyzing the relationship between the actual input and the equivalent control.In order to enhance the robustness of UAVs against external disturbances,a prescribed-time convergent extended state observer is designed to achieve online estimation of disturbance based on active disturbance rejection control theory.Furthermore,considering that only the followers connecting to the leader can access the leader's state information,a prescribed-time convergent distributed estimator is introduced to rapidly estimate the leader's state.On this basis,a prescribed-time convergent consensus formation control algorithm is proposed combined with the outputs of the observer and the estimator,and the prescribed-time stability of closed-loop system is proved by Lyapunov theory.The simulated results validate the effectiveness of the proposed method.The research results show that the proposed control method can achieve the stable cooperative control of UAV formation within a preset time in the presence of external disturbances.

Multi-axle wheeled vehicle dynamics model is the basis for the rapid development of new vehicle equipment,the optimization of vehicle design parameters and the construction of control algorithms.The commonly used commercial software is difficult to obtain the dynamic equations and model gradient information so that it cannot be used for the dynamic optimization of vehicle global design and control parameters.And the effects of top-loading dynamics have been given less consideration in existing commercial software and theoretical modelling studies.In order to address the aforementioned issues,a vectorized modeling method is employed to construct a 24-degrees-of-freedom dynamics model of an 8×8 wheeled vehicle based on Lagrangian dynamics,which considers the influences of the longitudinal and lateral motions of unsprung mass and the reaction force of top load on the vehicle dynamics.The modeling is based on Lagrangian dynamics,and the software is developed using C++ and M languages,respectively.The proposed model is comprehensively compared with the commercial software TruckSim under a variety of working conditions,including variable acceleration,step steering,double lane change,swept sine steering.The results demonstrate that the tire force,suspension force and air resistance,as well as the longitudinal,lateral and vertical motions of the proposed simulation model exhibit high consistency with those of the commercial software with an error of less than 5%.This verifies the accuracy of the methodology.

The dynamic impacts generated during the transient shifting process of a planetary integrated transmission system for tracked vehicle have serious effect on the service performance and reliability of transmission system.For a certain planetary integrated transmission system,the torsional dynamics models of gear transmission components,clutches and brakes,etc are established,and a dynamics model of transmission system during the shifting process is constructed.The simulation and bench tests of the shifting process of the integrated transmission system are carried out.The changing trends of the simulated and test results are basically consistent,thus verifying the correctness of the dynamics model.The impact loads of typical components during the shifting process are further studied,and the influence laws of the throttle opening and the characteristics curves of oil charging and discharging,etc.on the dynamic torques of the operating components are revealed.The main conclusions are as follows.The reverse dynamic torque of disengaging operating component and the maximum impact torque of engaging operating component gradually increase with the increase in oil discharging delay.With the extension of oil discharging time,the reverse dynamic torque of disengaging operating component gradually increases,and the maximum impact torque of engaging operating component decreases first and then increases.The faster the oil charging is in the fourth pressure increasing stage,the greater the maximum impact torque of engaging operating component is,and the maximum impact value is 11% higher than the minimum impact value.With the increase in throttle opening,the maximum dynamic torque of operating component gradually increases,and the maximum dynamic torque value is 73.8% higher than the minimum dynamic torque value.

Amphibious vehicle is a mobile platform capable of operating in both terrestrial and aquatic environments,and has significant application value and development potential in both military and civilian fields.The development history of amphibious vehicles is reviewed,and the characteristics and development trends of different types of amphibious vehicles are compared and analyzed.The key technologies for the navigation of amphibious vehicles on water are expounded from three aspects:modeling and simulation,high-speed amphibious vehicle design,and navigation control.The difficulties and challenges in achieving the unmanned operation of amphibious vehicles on water are discussed based on the research progress of unmanned technology for amphibious vehicles,and the future research direction of amphibious vehicles is prospected.

For the cooperative guidance issue of high-hypersonic re-entry gliding vehicles to simultaneously hit a target at a specified angle in a complex environment,a cooperative guidance law based on meta-learning and reinforcement learning algorithms is proposed.Considering the interference caused by complex combat environment,a Markov decision model for the cooperative guidance issue is established,taking the gliding vehicles’ motion status and proportional guidance factor as the state space and action space.A reward function is designed by comprehensively considering the vehicle-target distance,remaining flight time difference,and overload situation for multiple gliding vehicles attacking a target.Based on meta-learning theory and reinforcement learning algorithm,the proximal policy optimization algorithms are combined with the gated recurrent units to learn the common features of similar cooperative guidance tasks.This approach enhances the accuracy of cooperative guidance strategies in complex interference environments to achieve the constraints on angle of attack and attack time,while also improving the adaptability of cooperative guidance strategy to different combat scenarios.Simulated results indicate that the proposed cooperative guidance law enables multiple aerial vehicles to simultaneously attack a target at a specified attack angle in complex battlefield environment and quickly adapt to new cooperative guidance tasks.The cooperative guidance law maintains good performance even when the cooperative combat scenario changes.

A multi-dimensional enhanced particle swarm optimization algorithm (MDEPSO) is proposed to address the problem of insufficient global search capability and susceptibility to local optima in the 3D trajectory planning process of unmanned aerial vehicles using classical particle swarm optimization algorithms.This algorithm first introduces improvement factors to dynamically adjust inertia weights in various stages of particle optimization,enhancing population adaptability and overcoming local optima; Secondly,relying on dynamic constraint equations to enhance learning factors promotes more efficient information sharing between particles and improves the algorithm’s self-learning ability; Subsequently,the advantages of orderly integration of chaos initialization and elite reverse learning evolution strategies were utilized to re plan the particle swarm evolution process,enhance the balance and diversity of particles in the iterative process,and improve the convergence accuracy of the algorithm.In the experiment,through horizontal comparison of test functions and vertical application in complex 3D task scenarios,the multi-dimensional enhanced particle swarm optimization algorithm showed an improvement in the UAV trajectory planning ability compared to the classical particle swarm algorithm in the new multi-dimensional objective function indicators.It demonstrated good effectiveness and competitiveness among the five comparison algorithms.

During the vertical movement underwater,vehicles are highly susceptible to the marine environment,leading to unstable motion postures. Uncontrolled and unpowered launching has become inadequate in deep water and strong disturbance conditions. PID control,dynamic fluid body interaction,and dynamic nesting/sliding mesh techniques were employed to construct an integrated numerical calculation method that couples fluid dynamics,motion,and control in this paper. The motion process of underwater controlled-propulsion vehicles,analyzing the evolution of tail cavitation and ballistic characteristics under different tail jet pressures(6 MPa,10 MPa,12 MPa)were numerically simulated. The results show that during the merging process of supersonic jets and tail cavitation,the tail gas mass breaks due to interface instability and nozzle swaying,with the break occurring earlier as nozzle pressure increases. Inside the tail spray cavitation,a complex wave system structure exists. At higher nozzle pressures,the jet is under-expanded,resulting in multiple high and low-pressure areas formed by expansion and compression within the gas bubble. Over time,a Mach disk perpendicular to the axis appears near the nozzle exit. Additionally,the larger the nozzle pressure,the greater the disturbance torque caused by the tail spray flow. Increasing the nozzle pressure from 6 MPa to 12 MPa reduces the maximum deviation angle during motion by 18.5%,decreases the overshoot from 32% to 22%,resulting in less attitude fluctuation and better ballistic stability.

The near-space hypersonic gliding vehicle (HGV) poses a significant threat to existing defense systems due to its ultra-high velocity,extreme maneuverability,and superior penetration capabilities.To address the challenges in tracking and predicting HGV trajectories during interception,this paper presents an intelligent trajectory prediction method based on aerodynamic acceleration estimation.The maneuver patterns and aerodynamic variation laws of HGV are systematically analyzed according to the HGV motion model.On this basis,three critical parameters,i.e.,aerodynamic lift acceleration,drag acceleration and bank angle control,are identified as trajectory prediction variables for replacing the unknown terms in the HGV motion model.A dynamics tracking model based on aerodynamic acceleration estimation is developed to use the radar measurement data and the unscented Kalman filter (UKF) for real-time tracking and estimation of these parameters.These estimated parameters are then used as inputs to train a long short-term memory (LSTM) network,which captures the temporal relationships and variation patterns in the prediction parameters.The trained LSTM network is used to iteratively forecasts future aerodynamic accelerations,which are integrated with the numerical solutions of motion equations to extrapolate HGV trajectories.Numerical simulations confirm that the proposed method achieves high prediction accuracy and robust stability in predicting the trajectories of non-cooperative HGVs.

To optimize the fuel economy of the series hybrid tracked vehicle and reduce the offline training time of neural network,an energy management strategy (EMS) based on double-deep Q_learning network (DDQN) algorithm with Munchausen gradient optimization and prioritized experience replay (Munchausen-PER-DDQN) is proposed.The required power is calculated by a vehicle model which involves the engine-generator set,the battery pack and drive motor,and then the peoposed strategy is used to optimally control the throttle opening of engine based on power demand.The Munchausen gradient optimization algorithm adds log-policy to the reward to ease the learning of sub-optimal actions,and the prioritized experience replay algorithm assigns higher selection possibility to certain experience for those who have more influence on the training of the algorithm,Tthe energy management strategy based on Munchausen-PER-DDQN algorithm shows a better performance of fuel economy and training time of neural network.The simulated result shows that,compared with TD3-PER algorithm,the Munchausen-PER-DDQN algorithm achieves 35.3% improvement in neural network training time and 4.6% improvement in the fuel economy.

The launching mechanism of submarine-launched missiles under polar underwater environments is studied. The Arbitrary Lagrangian-Eulerian (ALE) method and the smoothed particle hydrodynamics (SPH) method are used to establish the fluid and infinite whole ice models, respectively, based on finite element software, and a plastic compressive tension material model with equation of state is used to reproduce the sensitivity of mechanical properties of ice to stain rate. Consequently, a fluid-structure interaction model of underwater vehicle ice-breaking is built. Based on the validated key numerical methods, the load and collision force characteristics of underwater vehicle under different ice thicknesses and vehicle speed conditions are investigated by simulation. and the stress distributions of vehicle and ice in the process of ice-breaking are analyzed. The result shows that the load and interaction time imposed on the vehicle increase with the increase in ice thickness, and with the increase in vehicle speed, the loads imposed on the vehicle increase, and the interaction time decreases.

In order to ensure the safety of unmanned underwater vehicle (UUV) when performing the complex tasks such as coastline patrol, collision avoidance of large vessels, and traversing dense islands and reefs, an elliptical modeling obstacle avoidance method based on control barrier function (CBF) is proposed. A control barrier function containing the heading angle constraints is designed on the basis of elliptical modeling obstacles, a quadratic programming (QP) problem with constraints is constructed, and a closed-form obstacle avoidance guidance law is proposed by combining with the guidance law in obstacle-free environment. The simulated results verify the effectiveness of the proposed method, which globally satisfies the safety and stability requirements. The proposed method has practical application value for the safe navigation of unmanned underwater vehicle in complex marine environment.

Aiming at the strong nonlinear problem of the interaction between underwater-launched projectile and ice, the key dimensionless parameters affecting the ice-breaking of projectile are derived by similarity theory. The scaled model test is carried out for the high-speed penetration of projectile through the ice layer. Based on the Gaussian fitting function, an ice load prediction formula is proposed. A fluid-solid coupling model of ice-breaking is established. The ice-breaking phenomenon, the ice load and the motion characteristics of projectile are analyzed by changing the nose shape of projectile, the kinetic energy of projectile and the thickness of ice layer. The results show that the volume of projectile nose cavitation decreases during ice-breaking, the volume of shoulder cavitation gradually increases, and the asymmetry of shoulder cavitation increases with the increase in ice thickness. When the initial speed of projectile is 40m/s, the extreme values of the ice loads on projectiles with hemispherical, spherical conical and pointed conical noses are 35700kN, 33200kN and 18600kN, respectively. The speed loss rate of projectile with pointed conical nose is the lowest, and its ice breaking effect is the best. Under the condition that the ice thickness is 180mm and the ejection pressure is 3MPa and 5MPa, respectively, the speeds of projectile after icebreaking are reduced from 13.1m/s and 17.8m/s to 9.5m/s and 13.4m/s. The greater the ice-breaking speed of projectile is, the lower the speed loss rate is, and the greater the kinetic energy loss is.The extreme value of ice load and the speed loss rate of projectile increase with the increase in ice thickness, and the effect of the initial speed of projectile on the load characteristics and motion characteristics weakens with the decrease in ice thickness.

The influence of the free surface on the shape of supercavitation and the hydrodynamic characteristics of underwater vehicle is investigated. The motion process of a supercavitating vehicle near the free surface is numerically simulated using an adaptive mesh method and the volume of fluid method, and the effects of the free surface on the shape of supercavitation, and the lift, resistance and torque of the vehicle are analyzed. The research findings show that the presence of the free surface causes the tail of supercavitation to shift away from the free surface, resulting in the length of supercavitation being significantly shorter than that in an infinite water domain. The velocity of vehicle has a significant impact on its lift near the free surface. When the velocity of vehicle is less than 50m/s, its lift is negative; when the velocity of vehicle exceeds 60m/s, its lift becomes positive. When the vehicle is fully enveloped by the cavitation, only the cavitator head is wetted. A zero-torque point always appears near the front (x=1D, where D is the diameter of the cavitator) of the vehicle. When the interface of supercavitation tail intersects with the vehicle, it causes a change in the position of the zero-torque point. When the attack angle of the vehicle is positive, the torque is less affected by the water depth. However, when the attack angle is negative, the torque is significantly influenced by the water depth.

A motion parameter estimation method based on acoustic measurement is proposed for identifying the kinetic characteristics of high dynamic underwater vehicle. In the proposed method, the position and velocity data series are obtained by point-by-point solution, and then the data jitters caused by random errors are removed by functional reconstruction and coefficient identification, so a smooth and continuous parameter sequence is obtained. In simulation, the high dynamic underwater vehicle moves from the depth of several tens of meters to surface, and the motion parameter is measured by the seabed four-receiver array. The simulated results show that the estimated parameter sequence has higher accuracy in vertical direction than the point-by-point solved one, and the root mean square errors (RMSE) of position and velocity are reduced by 27.8% and 47.2%, respectively. Calculations based on large samples indicate that the estimated parameter sequence approaches the true value, and the deviations of position and velocity are 0.042m and 0.056m/s, respectively. The water-tank test results show that the estimated parameter sequence generated by the high dynamic physical model is well consistent with the inertial sensor data, and the RMSEs of position and velocity in vertical direction are 0.024m and 0.141m/s, respectively. The proposed method can provide an accurate, smooth and continuous parameter sequence for the high dynamic underwater vehicle in performance testing and has certain engineering application value in sea trials.

In order to effectively identify the visual and auditory channel workloads of operators during the operation of a special vehicle, a machine learning-based workload recognition model is constructed from the electroencephalogram (EEG) signals acquired in a simulated driving environment. A total of 30 participants were recruited for experiment, and the visual and auditory workload states were induced by increasing the scenario complexity and administering an auditory N-back task. The experimental results show that, the power spectral densities in δ, θ, and α bands in the frontal lobe, δ and θ bands in the temporal lobe, θ band in the occipital lobe, and all four frequency bands in the parietal lobe under the auditory workload condition are significantly higher than those under the visual workload condition. Moreover, the brain network has a stronger connectivity at θ and β bands under the auditory workload condition exhibites. Notably, the θ-band power spectral density (PSD) emerges as the most effective feature for the identification of visual and auditory workload channels, enabling the random forest algorithm to achieve a maximum classification accuracy of 95.68%. Shapley additive explanations (SHAP) analysis indicates that the frontal lobe contributes most significantly to the classification outcomes. These findings demonstrate the effectiveness of EEG-based indicators in identifying the visual and auditory channel workloads, providing a theoretical foundation for the development of adaptive interaction systems.

In order to improve the obstacles avoidance ability of intelligent vehicles,an trajectory planning and control method of intelligent vehicles is proposed based on velocity obstacle model.The proposed method is used to establish a velocity obstacle model for intelligent vehicles by combining the velocity obstacle method and obstacle expansion method,The motion uncertainty of dynamic obstacles in the velocity space is transformed into the positional uncertainty,and the safety margin is adaptively adjusted by obstacle size and relative velocity.To balance trajectory tracking accuracy and driving stability,a fuzzy model predictive controller (FMPC) is designed based on the equation of state for vehicle,the fuzzy control principle and the model predictive control principle.A simulation model is established to verify the effectiveness of the proposed method.The simulated results show that the proposed method can be used to avoid the multiple random static and dynamic obstacles,and the reference trajectory can be quickly and smoothly tracked after obstacles avoidance.Based on the analysis of obstacles avoidance stability,it is concluded that the target speed is 100km/h,the maximum lateral speed is 4.01km/h,the maximum yaw rate is 20.8°/s,and the maximum centroid side slip angle is 2.32°,which meet the requirements of driving stability.The proposed method effectively improves the obstacle avoidance ability and driving stability of intelligent vehicles.

Due to the characteristics of hypersonic glide vehicles (HGVs) being unpowered and uncontrollable in axial overload,a significant positional error of HGV often exists at the transition between the gliding phase and the terminal phase,thereby substantially impacting the precision of coordinated strikes during the terminal phase.To address this issue,a formation control method for HGVs is proposed,which considers the capability of adjusting the position in launch direction.Fixed-time consensus controllers are devised for the second-order multi-agent system,serving as the foundation for an underactuated formation control framework.The underactuated control characteristics of HGV formation is analyzed.An adjustment strategy for HGV position in launch direction is formulated,and an analytical relationship between launch-direction position adjustments and additional lateral velocity is established.After enabling the capability for launch-direction adjustment,a three-dimensional artificial potential field is established,and a collision avoidance control strategy for HGV formation is proposed.Theoretical analysis and numerical simulations demonstrate that the proposed method can support the formation and maintenance of a group of HGVs in scenarios such as dispersion,contraction,collective steering,and high and low flying of formation.

At present,the traffic management relying on manpower is characterized by inaccurate statistics and delayed feedback.A vehicle detection algorithm based on the improved YOLOv7-tiny algorithm suitable for deploying on edge terminal devices is proposed to better protect people’s lives and property.A deep powerful residual (DP_Res)convolutional block isconstructed to perform the lightweight improvements on the efficient layer aggregation network-tiny (ELAN-T) module of backbone network.By reducing branches,the lightweight improvement on the ELAN-T module of the feature fusion network is made to reduce the number of parameters and computational load of the network,and the structure of the feature fusion network is reconstructed;The efficient channel attention mechanism and the EIOU bounding box loss function are introduced to improve the accuracy of the algorithm.The experiment is conducted on the preprocessed UA-DETRAC dataset,and the parameters of the improved algorithm are reduced by 15.1% compared to those of the original YOLOv7-tiny,with a reduction in computation of 5.3% and an increase in mAP@0.5 of 5.3 percentage points.The experimental results show that the improved algorithm not only achieves lightweight,but also improves the detection accuracy,making it suitable for deployment on edge terminal devices to complete the task of detecting vehicles on the road.

The high-power diesel generator set for vehicles is the power supply unit of heavy-duty series hybrid electric vehicles.Due to the coupling effect of the torque impact of multi-cylinder diesel engine crankshaft and the electromagnetic torque pulsation of generator,the system torsional vibration phenomenon is prominent,and the dynamic quality is poor.A dynamic model of engine-generator set considering the electromechanical coupling effect is established,and the influence of electromechanical coupling effect on the inherent characteristics of the system is analyzed.The law of variation of torsional vibration characteristics with the parameters,such as electromagnetic stiffness,rotor eccentricity and torsional damper stiffness,is revealed.The dynamic response characteristics of the system under the combined excitation of engine and motor are analyzed,and the order of the main response is clarified.A torsional vibration control overall framework based on dual loop decoupling is proposed to solve the problem of excessive low-frequency torsional vibration response.An independent modal space optimal controller for the start stop process and an adaptive filtering compensating controller for steady-state operating conditions are designed for the suppression of engine main harmonic disturbances,and are verified through simulation.The results show that the proposed torsional vibration active control algorithm can achieve the suppression of main harmonic torsional vibration in the full speed domain of the engine.

The recovery device carried outside the vehicle is susceptible to the interference from flow noise and has an effect on the acoustic stealthiness of underwater vehicle during the acoustic guidance.Regarding the aforementioned problems,this paper numerically simulates the flow noise of underwater vehicle based on Lighthill acoustic analogy theory and large eddy simulation (LES),compares the sound pressure level spectra and directivities of different shaped docking stations,and analyzes the sound pressure levels at different locations in the basin.The results show that the outboard recovery device is the main source of the radiated noise,which is about 50 dB higher than the maximum sound pressure level of the non-attached underwater vehicle,and the high sound pressure level is mainly concentrated between the middle and low frequencoes.For the recovery dock with the same profile areas,the maximum sound pressure level of horn-shaped guide housing is 1.62dB lower than that of the rectangular guide housing.The results can provide theoretical reference for reducing the flow noise during the process of underwater vehicle recovery and improving the recovery efficiency and acoustic stealth of underwater vehicle.

Stable and high-precision localization is a prerequisite for realizing the cooperative autonomous navigation of unmanned ground vehicle (UGV).LiDAR simultaneous localization and mapping (SLAM) often fails to achieve the precise localization in scenarios lacking geometric features,such as corridors,tunnels,and deserts.Therefore,a leapfrog cooperative LiDAR SLAM degradation correction method is proposed for UGVs.This method is used to estimate the normal vector of each feature point in the current frame,and a LiDAR SLAM degradation detection algorithm is devised.When the degradation of environment is detected,the ranging information about two unmanned vehicles is utilized to correct the degradation in LiDAR SLAM.Finally,the locating results are further optimized in the pose graph.Testing on two self-built UGV platforms reveals that the proposed method achieves better mapping performance compared to the current famous LiDAR SLAM methods,demonstrating its significant capability to enhance the locating performance of LiDAR SLAM in degraded scenarios.

The control strategy of traditional tank bidirectional stabilizers is difficult to effectively deal with the coupling,nonlinearity and uncertainty in the new generation of all-electric bidirectional stabilizers,while the model-based nonlinear control can make full use of a priori information from the dynamic model of the system to enhance the control effect.Based on this,an electromechanical coupled dynamics model of an all-electric bidirectional stabilizer taking the actuator dynamics into account is established,and a nonlinear sliding mode control method based on neural network compensation is proposed.The sliding mode surface and the improved sliding mode robust control law based on hyperbolic tangent function are introduced to construct the nonlinear sliding mode control function,which can effectively eliminate the system oscillations and improve the system stability.Meanwhile,the multilayer neural network technique is deeply fused to accurately estimate the uncertainty in the system and make compensation for the feedforward,thereby avoiding high-gain feedback.It is rigorously demonstrated by the stability theory based on Lyapunov functions that the proposed control strategy can achieve the asymptotic stability performance of tank all-electric bidirectional stabilizer with continuous control inputs.A co-simulation environment and a semi-physical experimental platform are built.The superiority of the proposed control strategy is verified through a large number of comparative experiments.

Lubricating oil plays a pivotal role in engines due to carrying a wealth of information about the engine state,and is crucial for characterizing the faults of engine.The engine of an armored vehicle is studied,and a fault diagnosis method for the engine is proposed,which leverages the kernel linear discriminant analysis (KLDA) and an improved dung beetle optimization (DBO) algorithm to optimize a back propagation (BP) neural network.The dimensionality reduction of the acquired lubricating oil data is performed through KLDA,and the dimensionality reduced data is taken as input for the BP neural network.The DBO algorithm is then enhanced by integrating optimal Latin hypercube sampling method,weighting factors and Levy flight strategy in order to further optimize the key parameters of neural network.A fault diagnosis model is established to predict the faults in test data effectively.Experimental results affirm the proposed method’s efficacy in rapidly and accurately predicting the faults,providing a scientific basis for the maintenance and repair of engines in armored vehicles.

Addressing the uncertainty of dynamic model parameters in the terminal guidance phase of hypersonic gliding vehicles and the slow convergence speed of traditional reinforcement learning algorithm,an adaptive guidance algorithm based on reinforcement learning is proposed.The terminal guidance problem for hypersonic gliding vehicles under nominal conditions is converted into an optimal control problem,which is solved using the sequential convex optimization algorithm to generate a dataset of state-control pairs.The dataset is fitted through supervised learning to obtain a corresponding guidance model.The disturbances such as aerodynamic parameter deviation,uncertainty in control response delay coefficient,and state measurement noise are introduced,and the guidance model is further optimized based on the reinforcement learning framework through numerous interactions between the vehicle and the current environment.Numerically simulated results indicate that the proposed guidance method exhibits better robustness and accuracy compared to the supervised learning guidance method.

The high-speed electromechanical transmission(EMT) system for vehicle is characterized by a highly compact structure and high-speed components,leading to significant electromechanical coupling effects and susceptibility to resonance.This paper analyzes the electromechanical coupling inherent vibration characteristics of a series EMT system.An inherent vibration model of the system is derived,its natural frequencies and mode shapes are studied,and six typical vibration modes of the system are summarized based on the characteristics of each mode shape.The influences of high-speed operating conditions and electromechanical coupling effects on the system’s natural frequencies and mode shapes are investigated.The resonance speeds of the system within a wide operating speed range are studied using Campbell diagrams and modal energy method.The results indicate that the electromechanical coupling effects alter the low-frequency vibration characteristics of the system,and the high-speed operating conditions can significantly change the certain natural frequencies of the system.Both factors should be fully considered in dynamics modeling.The research of resonance speed provides a reference for the dynamic regulation and optimization of system.

As the potential applications and strategic value of unmanned ground vehicles(UGVs)in complex operational environments become increasingly prominent,the safety of their autonomous actions is of paramount importance.This paper proposes a system safety analysis method for UGV,which combines the system-theoretic process analysis(STPA)method and the Bow-Tie model.Focusing on the safety of teleoperated UGVs,the STPA method is utilized to identify the unsafe control actions(UCAs)within the UGV system and their associated latent risks.Subsequently,the Bow-Tie model is utilized to analyze the event chain from loss causation scenarios to potential accident consequences,thereby delineating the risk propagation and diffusion pathways.Ultimately,the active and passive safety stratified control measures are determined based on the Bow-Tie analysis,and the system safety management is realized through an autonomous safety controller.

Reforming the current manned tracked vehicles into the unmanned tracked vehicles is one effective way to develop the unmanned tracked vehicle.To this end,a data acquisition system is designed to collect the vehicle state and driver’s operation data.Based on the collected data,the driver’s steering behaviors during the tracked vehicle’s operation are clustered and analyzed based on the Gaussian mixture model(GMM),and a steering control behavior model is established.Based on different steering categories from GMM,a prediction model for the driver’s steering operation is trained by taking the running speed and steering angle deviation of vehicle as model inputs and the turning angle of hydraulic motor swinging arm as the predictive truth value of steering control.The proposed predsiction model is used for the statistical modeling and prediction of driver steering behavior in the in real cross-country environment.Experimental results show that the proposed steering control model can predict the driver’s steering behavior accurately.

Skid-steering wheeled vehicle has a wide range of applications. Understanding the impact of drive system on the vehicle driving and steering performance is the basis for forward design. In response to the design and analysis requirements of hydraulically-driven skid-steering wheeled vehicle, a coupling simulation model of hydraulic drive system and vehicle dynamics system is established based on the principle of the hydraulically-driven skid-steering wheeled vehicle systems. Combined with the specific vehicle parameters, the characteristics of the vehicle driving and steering conditions are simulated and analyzed to obtain the dynamic response characteristics of hydraulic driving system during vehicle driving. The simulated results of the model are verified through the actual vehicle. The results show that time-varying parameters such as road resistance, vehicle speed and driver operation have a great impact on the pressure of hydraulic driving system, which is strongly related to the dynamic steering resistance. The coupled simulation model can be used for the coupled simulation of vehicle motion state and hydraulic driving system state and the coupled analysis of hydraulic driving system characteristics and vehicle dynamics characteristics.

Unmanned surface vehicles (USVs) have high mobility,strong concealment,and extensive operational range,making them highly suitable for performing a wide array of tasks such as reconnaissance,anti-submarine warfare,search and rescue.Environmental perception technology,crucial for the operation of USVs,has attracted considerable attention.This paper conducts a survey on the development status of environmental perception technology for USVs at abroad,and define and analyzed the challenges in USV environmental perception through specific case studies.The current state of research on USV environmental perception technology is analyzed from the perspectives of both unimodal and multimodal perception,considering the sensory equipment utilized by USVs.Finally,the unresolved challenges in USV environmental perception technology are summarized,and its future development is prospected.

Time series matching technology is widely used in vehicle handling consistency evaluation.An evaluation method of vehicle throttle control action consistency based on segmented dynamic time warping (DTW) is proposed for the motion consistency evaluation of a coach vehicle in the process of cooperative driving.On the basis of the inconsistent number of sample data points in the cooperative control test of coach vehicle and the slope constraint of dynamic bending path, the traditional DTW rectangular search area is transformed into a parallelogram search area to reduce the area of the search area by changing the slope of search path, thus greatly reduce the calculation amount.Four groups of typical throttle action curves are selected to carry out 50 rounds of iterative experiments for verification, and the DTW distance matrix between the action curves of real vehicles A and B is calculated by segmented DTW method.A minimum deviation leveling method is used to combine the cluster objects for the actions of vehicles A and B, so as to complete the consistency evaluation of throttle action data.The experimental results show that the average matching accuracy of the improved DTW algorithm in each throttle action can reach 89.2%, which is about 3.2% higher than that of the single DTW algorithm, and the average matching time is about 92.45s, which is about 12.6% lower, thus verifying the feasibility and superiority of the segmented DTW algorithm in the consistency evaluation of throttle action.

Taking the external disturbances caused by the uneven road surfaces of marching tank gun control system (TGCS) and the unbalanced moment of tank gun elevation motion during TGCS operationinto consideration,a two-axis coupled two-degrees-of-freedom (2-DOF) dynamics model of the marching TGCS is established using the second kind of Lagrange method. In terms of the actual force and motion state of TGCS,an electromechanical coupling dynamics model is established for the marching TGCS, which is equipped with permanent magnet synchronous motors (PMSM) in the azimuthal and elevational direction drive system and the gear reduction gearboxes and roller screws in the transmission mechanism. A stable tracking controller for marching tank with finite-time convergence characteristicsis designed to quickly reject the effects of internal and external disturbances of the marching TGCS on the stable tracking of target. This design is based on a nonsingular fast terminal sliding mode (NFTSM) control law and a linear extended state observer (LESO). The stability precision and arrival time of TGCS for marching tank are calculated based on the stable tracking process for targets. The computational results are then compared with those obtained by utilizing traditional control methods under various operating conditions. The findings demonstrate that the proposed tracking controller exhibits rapid response speed,strong robustness against disturbances,and high tracking accuracy,thereby validating the effectiveness of the controller design.