Trajectory Tracking Of Vehicle Research Articles

An Integrated Motion Planning Scheme for Safe Autonomous Vehicles in Highly Dynamic Environments

This study proposes a new integrated approach to the motion control of autonomous vehicles, which differs from the conventional method of treating planning and tracking tasks as separate or hierarchical components. By means of the proposed approach we can reduce the side effects on the performance of autonomous vehicles under challenging driving circumstances. To this end, our approach processes both of the aforementioned tasks asynchronously and simultaneously utilizes a multi-threaded architecture to enhance control performance. Meanwhile, the behavior planning feature is integrated into the path-tracking module. Then, a linear parameter-varying model predictive control is deployed for trajectory tracking of autonomous vehicles and compared with the linear model predictive control method. Finally, the control performance of the proposed approach was evaluated through simulation trials on urban roads with placed obstacles. The outcomes revealed that the suggested framework satisfies the processing rate and high-precision criteria, while safely avoiding obstacles, indicating that it is a promising control strategy for real-world applications.

Adaptive fixed-time backstepping control for three-dimensional trajectory tracking of underactuated autonomous underwater vehicles

An adaptive fixed-time backstepping control is proposed to achieve the three-dimensional trajectory tracking control of an underactuated autonomous underwater vehicle (AUV) in the presence of model uncertainty and external disturbances. In this paper, the dynamics of the AUV in terms of five degrees of freedom (DOFs) are discussed. Considering it is an underactuated AUV, a virtual velocity guidance law is derived using the backstepping method. For velocity convergence, an adaptive fixed-time control is derived without model parameters, with adaptive adjusting law tackling system unknows. Theoretical analyses demonstrate that the tracking error converges to a small bounded field within a fixed time in the proposed control scheme. The effectiveness and superiority of the proposed method are verified by simulation results.

Isolating Trajectory Tracking From Motion Control: A Model Predictive Control and Robust Control Framework for Unmanned Ground Vehicles

This letter studies the trajectory tracking and motion control problems of unmanned ground vehicles (UGVs). A model predictive control and robust control (MPC-RC) framework for UGVs is proposed to improve tracking accuracy, yaw stability and robustness in a modular fashion without introducing complexity into controller. The trajectory tracking problem and three-dimensional phase trajectory planning with high stability of a vehicle motion can be performed in the model predictive control design simultaneously. Also, combining the advantages of linear matrix inequality, sliding mode control, and back-stepping control law, three robust motion controllers can track the generated three-dimensional phase trajectory steadily so that the UGV motion stability is guaranteed. The robust performance is guaranteed through considering model uncertainties and terra-aerodynamic disturbances in robust controllers. Sufficient conditions for closed-loop stability under the diverse robust factors are provided by the Lyapunov method analytically, which ensures the series system's feasibility. The results of simulations on MATLAB-Carsim platform demonstrate that the proposed controller can significantly enhance tracking accuracy, motion stability, and robustness compared to the existing methods, which guarantees the feasibility and capability of driving in a nonlinear extreme scenario.

Cooperative Strategy of Trajectory Tracking and Stability Control for 4WID Autonomous Vehicles Under Extreme Conditions

Trajectory tracking and stability control are two essential functions of autonomous vehicles, and there is inevitable mutual interference between them, especially under extreme conditions. Towards addressing this challenge, this paper proposes a novel cooperative strategy of trajectory tracking and stability control for four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed based on the model predictive control (MPC) theory, in which the tire cornering stiffness is modified in real-time leverages the lateral force estimated by the square-root cubature Kalman filter (SCKF). Then, the vehicle stability controller is designed based on the sliding mode control (SMC), in which the tire force saturation constraint and the relative weight of yaw rate and sideslip angle are considered. Furthermore, a weight adaptive criterion with normalized stability index is defined to develop the cooperative strategy of trajectory tracking and stability control. The feasibility and adaptability of the proposed method to different extreme conditions are verified by hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. Finally, an experimental case is presented to demonstrate the realizability of the proposed method.

Realtime wide-area vehicle trajectory tracking using millimeter-wave radar sensors and the open TJRD TS dataset

• A system based on millimeter wave radar is introduced for tracking vehicles. • Trajectory splicing is applied for tracking vehicles in wide-area level. • Validation results show a reliable and stable performance of the system. • The system performs well in detecting speeding fractions through pilot project. • TJRD TS Platform is introduced in sharing wide-area trajectory dataset (open). This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking results were first validated for single vehicle trajectory tracking using the Real-Time Kinematic positioning technology based on the Beidou satellite navigation systems. The validation showed that the vehicle positions were captured with a mean lateral offset of −0.284 m and a mean longitudinal offset of −0.352 m. The mean estimated speeds were found to have a difference of only −0.048 km/h from the ground truths. The trajectory tracking results were also validated through multi-object tracking using video data from an unmanned aerial vehicle (UAV). Compared with the UAV video footage, the millimeter wave-based system was found to correctly capture around 92% of the total number of vehicles. For the correctly captured vehicles, their positions were found to be within 0.99 m (about a quarter of the width of a regular traffic lane) of the ground truth. In this paper, we also would like to share openly the entire validated datasets which has been recently published online as the TJRD TS platform. Additionally, a demonstration in using the dataset to detect aggressive driving behaviors, such as speeding was presented. It is expected that the open dataset will help enable researchers and practitioners to further explore the behaviors of road users, track the dynamics of safety risks and congestion formation, and evaluate incident impacts in a more microscopic but comprehensive way.

Analysis and design of control systems via parameter‐based approach

Analysis and design of control systems via parameter‐based approach

Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve

This paper proposes a clothoid-curve-based trajectory tracking control method for autonomous vehicles to solve the problem of tracking errors caused by the discontinuous curvature of the control curve calculated by the pure pursuit tracking algorithm. Firstly, based on the Ackerman steering model, the motion model is constructed for vehicle trajectory tracking, Then, the position of the vehicle after the communication delay of the control system is predicted as the starting point of the clothoid control curve, and the optimization interval of the curve end point is determined. The clothoid control curves are calculated, and their parameters are verified by the vehicle motion and safety constraints, so as to obtain the optimal clothoid control curve satisfying the constraints. Finally, considering the servo system response delay time of the steering system, the steering angle target control value is obtained by previewing the curvature of the clothoid control curve. The field experiment is conducted on the test road, which consists of straight, right-angle turns and lane-change elements under three sets of speed limitations, and the test results show that the proposed clothoid-curve-based trajectory tracking control method greatly improved the tracking accuracy compared with the pure pursuit method; in particular, the yaw deviation is improved by more than 50%.

Adaptive Control Algorithm for Trajectory Tracking of Underactuated Unmanned Surface Vehicle (UUSV)

The ability to track the trajectory or path on the sea surface remains a key measurement in the control system of an unmanned surface vehicle (USV). In this research, the designed algorithm defines the path and minimizes the disturbances to zero. Underactuated USVs are ships or boats, which operate on the water surface without a crew and the underactuated system has low actuators than its degree of freedom (DOF). The adaptive control strategy is applied to the unbounded system as the ocean due to its high performance, while the robust control system attains high performance in the bounded system. To consider the trajectory tracking of UUSV and provide an optimal control strategy for a ship in the presence of external disturbances, the study presents a controller-based on model reference adaptive control with an integrator (MRACI), which guarantees the stability of a closed-loop system. The vehicle experiences variations in the system response due to external disturbance. This abovementioned scheme reduces the variations to nearly equal to zero making the vehicle stable. First, use computer-based simulation to verify the proposed controller under two different scenarios. Then, simulation results show that the designed scheme lowers the errors and performs well.

Intelligent electric vehicle trajectory tracking control algorithm based on weight coefficient adaptive optimal control

The track tracking effect of intelligent vehicle directly affects the safety of vehicle and passengers. In the process of intelligent vehicle track tracking, the track tracking accuracy is related to many factors such as track curvature, road friction coefficient, and longitudinal speed change. In this paper, a new lateral and longitudinal coupling control algorithm is proposed. Based on the vehicle dynamics model and the optimal control theory, combined with the fuzzy control theory, the Fuzzy Linear Quadratic Regulator (FLQR) lateral optimal controller is designed, and feed-forward control and predictive controller are added. According to the real-time tracking lateral error and fuzzy control algorithm fed back by the system, the weight coefficient of the lateral displacement deviation in the cost function is dynamically adjusted; considering the coupling effect of lateral and longitudinal controllers of vehicle trajectory tracking control, a model predictive control (MPC) longitudinal speed controller is designed based on MPC theory, considering acceleration constraint and acceleration variation constraint, and taking lateral stability as evaluation index. A joint simulation platform is built based on CarSim and Simulink. The simulation results show that the designed lateral and longitudinal coupling controller of FLQR + MPC has better track tracking accuracy and can improve the driving stability of the vehicle; finally, the tracking effect of the designed algorithm is verified by real vehicle experiments. The maximum error of the designed controller algorithm in real vehicle tracking is 0.56 m, and the tracking effect is good.

Research on trajectory tracking and body attitude control of autonomous ground vehicle based on differential steering.

The differential steering can be used not only as the backup system of steer-by-wire, but also as the only steering system. Because the differential steering is realized through the differential moment between the coaxial left and right driving wheels, the sharp reduction of the load on the inner driving wheel will directly lead to the failure of the differential steering when the four-wheel independent drive electric vehicle approaches the rollover. Therefore, this paper not only realizes the trajectory tracking of autonomous ground vehicle through the differential steering, but also puts forward the body attitude control to improve the handling stability. Firstly, the dynamic and kinematic models of differential steering autonomous ground vehicle (DSAGV) and its roll model are established, and the linear three-degree of freedom vehicle model is selected as the reference model to generate the ideal body roll angle. Secondly, a model predictive controller (MPC) is designed to control the DSAGV to track the given reference trajectory, and obtain the required differential moment and the resulting front-wheel steering angle. Then, a sliding mode controller (SMC) is adopted to control the DSAGV to track the ideal body roll angle, and obtain the required roll moment. The simulation results show that the proposed MPC and SMC can not only make the DSAGV realize the trajectory tracking, but also achieve the body attitude control.

Trajectory Tracking of Autonomous Vehicle: A Differential Flatness Approach With Disturbance-Observer-Based Control

In this article, the problem of trajectory tracking control is studied for autonomous vehicle with consideration of the nonlinearity and coupling characteristics. To achieve accurately trajectory tracking in longitudinal and lateral motions simultaneously, a flatness disturbance-observer-based control scheme is developed to address the kinematic coupling issue of the vehicle, which can avoid extra modeling errors introduced by linearization. The main idea is that, derived from a suitable selection of flat outputs, the complex kinodynamic vehicle model is transformed into three fully-actuated subsystems with same structure, which will provide convenience for controller design. Then, in order to compensate the lumped disturbances caused by modeling and transformation, a disturbance-observer-based control strategy is designed via backstepping. Trajectory tracking control is accomplished through longitudinal, lateral and yaw motions simultaneously. The stability of the closed-loop dynamics is guaranteed in the framework of Lyapunov and input-to-state stability theories. Finally, the CarSim simulation tests demonstrate that the proposed integrated control method can successfully promote the trajectory tracking performance in different manoeuvres.

Adaptive reinforcement learning fault-tolerant control for AUVs With thruster faults based on the integral extended state observer

In this paper, a reinforcement learning (RL) fault-tolerant control (FTC) method is proposed for trajectory tracking of autonomous underwater vehicles (AUVs) with thruster faults. To deal with the thruster fault, unknown disturbance and model uncertainty, a new integral extended state observer (IESO) for fault diagnosis observation is proposed, which uses a conventional ESO to estimate the total system uncertainty, and introduces an integral mechanism to mitigate the effect of estimation error further. Thus, the problem that the estimation error caused by the traditional ESO leads to the decline of the fault-tolerant capability of the FTC system is solved. Then, to solve the problem of integral saturation due to the introduction of the integral term, the integral term is limited after the thruster fault of the AUV. Furthermore, based on the actor–critic structure of RL, a PD-like feedback controller is designed to realize the FTC of AUV in the face of thruster fault by using the total uncertainty of the IESO scheme. And the input saturation of the thruster is considered, and an auxiliary variable system is used to handle the control truncation between saturated and unsaturated inputs. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the proposed method is verified to have good fault tolerance and robustness by simulation and underwater experiments.

Design of autonomous vehicle trajectory tracking controller based on Neural Network Predictive Control

When the linear Model Predictive Control (MPC) algorithm is used for trajectory tracking control of autonomous vehicles in high-speed turning conditions, the tracking accuracy is usually decreased due to the low fidelity of the predictive model. To deal with this problem, a trajectory tracking controller based on Neural Network Predictive Control (NNPC) is designed in this paper. The data collected from driving simulation experiments is used for the Back Propagation Neural Network (BPNN) training process to obtain the vehicle state predictive models. To reduce the iteration times of the rolling optimization module of NNPC, Particle Swarm Optimization (PSO) with Fitness Allocating Inertia Weights (PSO-FAIW) algorithm is proposed, which can allocate inertia weights in light of the distance between each particle’s fitness and the best fitness of the swarm. Finally, the performance of the controller designed in this paper is verified based on MATLAB /Simulink. The simulation results demonstrate that the controller proposed in this paper can give consideration to tracking accuracy and real-time performance.

An at-scale tailless flapping wing hummingbird robot: II. Flight control in hovering and trajectory tracking

Flight control such as stable hovering and trajectory tracking of tailless flapping-wing micro aerial vehicles is a challenging task. Given the constraint on actuation capability, flight control authority is limited beyond sufficient lift generation. In addition, the highly nonlinear and inherently unstable vehicle dynamics, unsteady aerodynamics, wing motion caused body oscillations, and mechanism asymmetries and imperfections due to fabrication process, all pose challenges to flight control. In this work, we propose a systematic onboard control method to address such challenges. In particular, with a systematic comparative study, a nonlinear flight controller incorporating parameter adaptation and robust control demonstrates the preferred performances. Such a controller is designed to address time-varying system uncertainty in flapping flight. The proposed controller is validated on a 12-g at-scale tailless hummingbird robot equipped with two actuators. Maneuver experiments have been successfully performed by the proposed hummingbird robot, including stable hovering, waypoint and trajectory tracking, and stabilization under severe wing asymmetries.

Research on Path Planning and Trajectory Tracking of Autonomous Vehicle

Research on Path Planning and Trajectory Tracking of Autonomous Vehicle

Predefined-time Neural Sliding Mode Control based Trajectory Tracking of Autonomous Surface Vehicle

Predefined-time Neural Sliding Mode Control based Trajectory Tracking of Autonomous Surface Vehicle

Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional‐integral‐difference)‐ based control method are proposed. A lateral tracking controller is designed by using MPC with rule‐based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability.

Design of Particle Swarm Optimization Adaptive Sliding Mode Controller Based on an Extended State Observer for the Longitudinal Motion of a Supercavitating Vehicle with Input Saturation

The aim of this paper is to present a novel sliding mode control scheme for the supercavitating vehicle trajectory tracking problem that subjects to external disturbances and actuator saturation with two symmetric elevators and a cavitator as actuators by analytical methods and computer simulations. Firstly, the nonlinear and highly coupled dynamic and kinematic models of a supercavitating vehicle are presented in a comprehensive way by taking the cavity memory effect and time‐variant planing force into consideration. The PSO algorithm is employed to optimize the control parameters for achieving better and more practical tracking performance by minimizing the objective function. A second‐order extended state observer (ESO) is utilized to estimate the unknown external and state‐dependent disturbances and compensates for control inputs. In addition, an antiwindup compensator is adopted to cope with actuator saturation. Finally, the proposed control scheme is employed for complex trajectory tracking of a supercavitating vehicle under various conditions by conducting comparative numerical simulations. Rigorous theoretical analysis and simulation results indicate that the proposed control scheme can achieve satisfactory tracking performance and have a good capability of robustness.

Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking

This paper develops a multi-stage estimation algorithm for vehicle trajectory tracking applications. Previously designed nonlinear observers for vehicle trajectory tracking lack either the ability to handle variable velocity or have a high sensitivity to sensor noise. To overcome these shortcomings, the original model of the non-ego vehicle is translated into three separate models for speed, orientation, and position. Three stable observers are designed for these models which are all shown to be stable and robust to uncertainties. The new estimation algorithm outperforms previous high-gain and LMI-based nonlinear observers. The developed observer is useful for collision prediction and avoidance applications.

Analyses of autonomous underwater vehicle trajectory tracking control algorithms

Analyses of autonomous underwater vehicle trajectory tracking control algorithms