Accurately tracking hypersonic gliding vehicles via an LEO mega-constellation in relay tracking mode

In order to effectively defend against the threats of the hypersonic gliding vehicles(HGVs),HGVs should be tracked as early as possible,which is beyond the capability of the ground-based radars.Being benefited by the developing megaconstellations in low-Earth orbit,this paper proposes a relay tracking mode to track HGVs to overcome the above problem.The whole tracking mission is composed of several tracking intervals with the same duration.Within each tracking interval,several appropriate satellites are dispatched to track the HGV.Satellites that are planned to take part in the tracking mission are selected by a new derived observability criterion.The tracking performances of the proposed tracking mode and the other two traditional tracking modes,including the stare and track-rate modes,are compared by simulation.The results show that the relay tracking mode can track the whole trajectory of a HGV,while the stare mode can only provide a very short tracking arc.Moreover,the relay tracking mode achieve higher tracking accuracy with fewer attitude controls than the track-rate mode.

Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints

In this paper,guaranteed cost attitude tracking con-trol for uncertain quadrotor unmanned aerial vehicle(QUAV)under safety constraints is studied.First,an augmented system is constructed by the tracking error system and reference system.This transformation aims to convert the tracking control prob-lem into a stabilization control problem.Then,control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances,and they are incorporated into the reward function as penalty items.Based on the modified reward function,the problem is simplified as the optimal regulation problem of the nominal augmented system,and a new Hamilton-Jacobi-Bellman equation is developed.Finally,critic-only rein-forcement learning algorithm with a concurrent learning tech-nique is employed to solve the Hamilton-Jacobi-Bellman equa-tion and obtain the optimal controller.The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances,but also enforce safety constraints.The performance of the algorithm is evaluated by the numerical simulation.

Multiparameter Numerical Investigation of Two Types of Moving Interactions Between the Deep-Sea Mining Vehicle Track Plate and Seabed Soil:Digging and Rotating Motions

To ensure the safe performance of deep-sea mining vehicles(DSMVs),it is necessary to study the mechanical characteristics of the interaction between the seabed soil and the track plate.The rotation and digging motions of the track plate are important links in the contact between the driving mechanism of the DSMV and seabed soil.In this study,a numerical simulation is conducted using the coupled Eulerian–Lagrangian(CEL)large deformation numerical method to investigate the interaction between the track plate of the DSMV and the seabed soil under two working conditions:rotating condition and digging condition.First,a soil numerical model is established based on the elastoplastic mechanical characterization using the basic physical and mechanical properties of the seabed soil obtained by in situ sampling.Subsequently,the soil disturbance mechanism and the dynamic mechanical response of the track plate under rotating and digging conditions are obtained through the analysis of the sensitivity of the motion parameters,the grouser structure,the layered soil features and the soil heterogeneity.The results indicate that the above parameters remarkably influence the interaction between the DSMV and the seabed soil.Therefore,it is important to consider the rotating and digging motion of the DSMV in practical engineering to develop a detailed optimization design of the track plate.

Cooperative Target Tracking of Multiple Autonomous Surface Vehicles Under Switching Interaction Topologies

This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'dynamics.Accordingly,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control vectors.Moreover,a disturbance observer is presented to estimate unknown time-varying environmental disturbance.Furthermore,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target.It enables each ASV to adjust its forces and moments according to the received information from its neighbors.The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.

Adaptive saturated tracking control for solid launch vehicles in ascending based on differential inclusion stabilization

The large-range uncertainties of specific impulse,mass flow per second,aerodynamic coefficients and atmospheric density during rapid turning in solid launch vehicles(SLVs) ascending leads to the deviation of the actual trajectory from the reference one.One of the traditional trajectory tracking methods is to observe the uncertainties by Extended State Observer(ESO) and then modify the control commands.However,ESO cannot accurately estimate the uncertainties when the uncertainty ranges are large,which reduces the guidance accuracy.This paper introduces differential inclusion(DI) and designs a controller to solve the large-range parameter uncertainties problem.When above uncertainties have large ranges,it can be combined with the ascent dynamic equation and described as a DI system in the mathematical form of a set.If the DI system is stabilized,all the subsets are stabilized.Different from the traditional controllers,the parameters of the designed controller are calculated by the uncertain boundaries.Therefore,the controller can solve the problem of large-range parameter uncertainties of in ascending.Firstly,the ascent deviation system is obtained by linearization along the reference trajectory.The trajectory tracking system with engine parameters and aerodynamic uncertainties is described as an ascent DI system with respect to state deviation,which is called DI system.A DI adaptive saturation tracking controller(DIAST) is proposed to stabilize the DI system.Secondly,an improved barrier Lyapunov function(named time-varying tangent-log barrier Lyapunov function) is proposed to constrain the state deviations.Compared with traditional barrier Lyapunov function,it can dynamically adjust the boundary of deviation convergence,which improve the convergence rate and accuracy of altitude,velocity and LTIA deviation.In addition,the correction amplitudes of angle of attack(AOA) and angle of sideslip(AOS) need to be limited in order to guarantee that the overload constraint is not violated during actual flight.In this paper,a fixed time adaptive saturation compensation auxiliary system is designed to shorten the saturation time and accelerate the convergence rate,which eliminates the adverse effects caused by the saturation.Finally,it is proved that the state deviations are ultimately uniformly bounded under the action of DIAST controller.Simulation results show that the DI ascent tracking system is stabilized within the given uncertainty boundary values.The feasible bounds of uncertainty is broadened compared with Integrated Guidance and Control algorithm.Compared with Robust Gain-Scheduling Control method,the robustness to the engine parameters are greatly improved and the control variable is smoother.

Observer-Based Path Tracking Controller Design for Autonomous Ground Vehicles With Input Saturation

This paper investigates the problem of path tracking control for autonomous ground vehicles(AGVs),where the input saturation,system nonlinearities and uncertainties are considered.Firstly,the nonlinear path tracking system is formulated as a linear parameter varying(LPV)model where the variation of vehicle velocity is taken into account.Secondly,considering the noise effects on the measurement of lateral offset and heading angle,an observer-based control strategy is proposed,and by analyzing the frequency domain characteristics of the derivative of desired heading angle,a finite frequency H_∞index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking error.Thirdly,sufficient conditions are derived to guarantee robust H_∞performance of the path tracking system,and the calculation of observer and controller gains is converted into solving a convex optimization problem.Finally,simulation examples verify the advantages of the control method proposed in this paper.

Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments,such as low intelligence and poor comfort perfor-mance in the driving process.The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions.In this paper,in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are pro-posed.Especially,the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition,and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve.By using low-order interpolation curve splicing,the planning computation is reduced,and the real-time performance of planning is improved,com-pared with one-segment curve fitting method.Furthermore,the comfort performance of the planned path is reflected intuitively by the curvature information of the path.Finally,the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim.The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

Vehicle recognition and tracking based on simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm

Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value.

Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway management.However,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall accuracy.Deep learning is considered to be an efficient method for object detection in vision-based systems.In this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation technique.The model consists of six steps.In the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the images.These pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in images.Each detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple vehicles.Based on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching technique.Further,we implemented a Kalman filter to track multiple vehicles.In the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking algorithm.The experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art systems.The proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.

Multi-Target Tracking of Person Based on Deep Learning

To improve the tracking accuracy of persons in the surveillance video,we proposed an algorithm for multi-target tracking persons based on deep learning.In this paper,we used You Only Look Once v5(YOLOv5)to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric(DeepSORT)to do cascade matching and Intersection Over Union(IOU)matching of person targets between different frames.To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification(ReID)network in the process of cascade matching,we introduced Spatial Relation-aware Global Attention(RGA-S)and Channel Relation-aware Global Attention(RGA-C)attention mechanisms into the network structure.The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03.To enhance the discrimination performance of the network,we proposed a new loss function design method,which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network,we introduced a Label-Smoothing regularization method to the cross-entropy loss.To facilitate the model’s convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training,this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay.The experimental results on CUHK03 show that the mean Average Precision(mAP)of the improved ReID network is 76.5%.The Top 1 is 42.5%,the Top 5 is 65.4%,and the Top 10 is 74.3%in Cumulative Matching Characteristics(CMC);Compared with the original algorithm,the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%,the tracking precision is improved by 3.8%.The number of identity switching is reduced by 25%.The algorithm effectively alleviates the IDSwitch problem,improves the tracking accuracy of persons,and has a high practical value.

Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.

Multi-constrained model predictive control for autonomous ground vehicle trajectory tracking

A multi-constrained model predictive control （ MPC ） algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computa- tion, an active steering linear error model is applied in the MPC controller. Then, a control incre- ment constraint and a relaxing factor are taken into account in the objective function to ensure the smoothness of the trajectory, using a softening constraints technique. In addition, the controller can obtain optimal control sequences which satisfy both the actual kinematic constraints and the actuator constraints. The circular trajectory tracking performance of the proposed method is compared with that of another MPC controller. To verify the trajectory tracking capabilities of the designed control- ler at different desired speed, the simulation experiments are carried out at the speed of 3m/s, 5m/ s and 10m/s. The results demonstrate the MPC controller has a good speed adaptability.

Neural Network Based Robust Controller for Trajectory Tracking of Underwater Vehicles

A robust neural network controller （NNC） is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A twolayer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded （UUB） stabilities of tracking errors and NN weights are guaran- teed through the proposed controller. An on-line NN weights tuning algorithm is also propesed. Good performances of the tracking control system are illustrated bv the results of numerical simulations.

Tracking control for air-breathing hypersonic cruise vehicle based on tangent linearization approach

This paper is focused on developing a tracking controller for a hypersonic cruise vehicle using tangent linearization approach.The design of flight control systems for air-breathing hypersonic vehicles is a highly challenging task due to the unique characteristics of the vehicle dynamics.Motivated by recent results on tangent linearization control,the tracking control problem for the hypersonic cruise vehicle is reduced to that of a feedback stabilizing controller design for a linear time-varying system which can be accomplished by a standard design method of frozen-time control.Through a proper model transformation,it can be proven that the tracking error of the designed closed-loop system decays exponentially.Simulation studies are conducted for trimmed cruise conditions of 110000 ft and Mach 15 where the responses of the vehicle to step changes in altitude and velocity are evaluated.The effectiveness of the controller is demonstrated by simulation results.

Local Path Planning and Tracking Control of Vehicle Collision Avoidance System

Automotive collision avoidance technology can effectively avoid the accidents caused by dangerous traffic conditions or driver's manipulation errors.Moreover,it can promote the development of autonomous driving for intelligent vehicle in intelligent transportation.We present a collision avoidance system,which is composed of an evasive trajectory planner and a path following controller.Considering the stability of the vehicle in the conflict-free process,the evasive trajectory planner is designed by polynomial parametric method and optimized by genetic algorithm.The path following controller is proposed to make the car drive along the designed path by controlling the vehicle's lateral movement.Simulation results show that the vehicle with the proposed controller has good stability in the collision process,and it can ensure the vehicle driving in accordance with the planned trajectory at different speeds.The research results can provide a certain basis for the research and development of automotive collision avoidance technology.

Modeling and Trajectory Tracking Control for Flapping-Wing Micro Aerial Vehicles

This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles(FWMAVs)in the longitudinal plane.First of all,the kinematics and dynamics of the FWMAV are established,wherein the aerodynamic force and torque generated by flapping wings and the tail wing are explicitly formulated with respect to the flapping frequency of the wings and the degree of tail wing inclination.To achieve autonomous tracking,an adaptive control scheme is proposed under the hierarchical framework.Specifically,a bounded position controller with hyperbolic tangent functions is designed to produce the desired aerodynamic force,and a pitch command is extracted from the designed position controller.Next,an adaptive attitude controller is designed to track the extracted pitch command,where a radial basis function neural network is introduced to approximate the unknown aerodynamic perturbation torque.Finally,the flapping frequency of the wings and the degree of tail wing inclination are calculated from the designed position and attitude controllers,respectively.In terms of Lyapunov's direct method,it is shown that the tracking errors are bounded and ultimately converge to a small neighborhood around the origin.Simulations are carried out to verify the effectiveness of the proposed control scheme.

Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of model predictive control(MPC)schemes and robust Hstate feedback control(RSC)method for trajectory tracking is proposed in this paper.The main objective of this paper is to compare MPC and RSC controllers’performance in tracking predefined trajectory under different scenarios.MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire,which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode.RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison.Then,three test cases are built in CarSim-Simulink joint platform.Specifically,the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions.Besides,the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability.Furthermore,an extreme curve test is built where the road adhesion changes suddenly,in order to test the performance of both controllers under extreme conditions.Finally,the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

Reference tracking control for flexible air-breathing hypersonic vehicle with actuator delay and uncertainty

This paper considers the problem of reference tracking control for the flexible air-breathing hypersonic flight vehicle with actuator delay and uncertainty.By constructing the Lyapunov functional including the lower and upper bounds of the time-varying delay,the non-fragile controller is designed such that the resulting closed-loop system is asymptotically stable and satisfies a prescribed performance cost index.The simulation results are given to show the effectiveness of the proposed control method,which is validated by excellent output reference altitude and velocity tracking performance.