Trajectory Tracking Of Vehicle Research Articles

3D trajectory tracking of underactuated non-minimum phase underwater vehicles

This paper studies the 3D trajectory tracking problem for a class of torpedo-type underwater vehicles which are a typical underactuated and also non-minimum phase systems. Two spherical coordinate transformations are introduced to transform the vehicles’ tracking model into a certain three-input-three-output form which, unfortunately, is not in a strict-feedback form. To avoid possible singularity problem in the recursive control design, an exponential modification of orientation (EMO) concept is introduced and forces the vehicles to track this modified trajectory instead of directly tracking the reference one. As for the non-minimum phase problem, since the input matrix in the transformed three-input-three-output form is always invertible, this problem can be easily handled in this paper. Numerical studies are also carried out to verify the effectiveness of the proposed tracking scheme.

Observer based fixed-time integral sliding mode tracking control for underactuated AUVs via an event-triggered mechanism

In this paper, a fixed-time control scheme is presented for trajectory tracking of underactuated autonomous underwater vehicles (AUVs) subject to external disturbances. An adaptive fixed-time disturbance observer (AFDO) is developed to estimate the external disturbances in fixed time without the prior knowledge of external disturbances. An event-triggered mechanism is introduced to save the bandwidth resources and reduce the communication burden. By synthesizing the developed AFDO and the event-triggered mechanism, an AFDO based event-triggered fixed-time integral sliding mode control (AFDO-EFISMC) scheme is proposed to force the AUV to track the reference trajectory in fixed time. With the aid of the developed AFDO, the gain of discontinuous control part of the designed control scheme is reduced, and therefore the control chattering is alleviated. Stability analysis is given to show that the designed AFDO-EFISMC scheme can make the tracking errors of the AUV converge to arbitrarily small neighborhood of the origin within a fixed-time independent of the initial conditions. The effectiveness of the proposed control scheme is proven by comparative numerical simulations.

Trajectory tracking control of unmanned surface vehicle based on exponential global fast terminal sliding mode control

This article studies the trajectory tracking of an underactuated unmanned surface vehicle. The trajectory tracking controller is divided into two parts: the kinematics controller and the dynamic controller. Taking the hull coordinate system of the unmanned surface vehicle as the datum plane, the kinematics controller is designed based on the backstepping method, and the virtual control input of the speed and the heading deviation of the ship are obtained. A dynamic controller is designed through an exponential sliding surface to stabilize the error of speed and heading deviation, thereby stabilizing the position error. In the process of designing the controller, the stability of the entire closed-loop control system is analyzed using Lyapunov stability theory. Finally, the designed numerical simulation experiment is carried out. The proposed exponential sliding mode controller is compared with the global integral sliding mode controller, and it is verified that the proposed controller has better effectiveness and robustness in the trajectory tracking of the underactuated unmanned surface vehicle.

Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar

Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar

Tracking performance of the coaxial counter-paddle flight system using fractional active disturbance rejection controller

Stable aerial flight capability is crucial for amphibious vehicles to perform cross-domain motion, yet it is often influenced by unpredictable cross-domain environments and model uncertainties. A fractional-order active disturbance rejection controller based on finite-time convergent extended state observer is proposed for the anti-interference control problem in the attitude trajectory tracking of bullet-shaped trans-domain amphibious vehicle in complex environments. Firstly, a complete dynamic model of the airborne flight system of the bullet-shaped trans-domain amphibious vehicle is established. A hierarchical control scheme based on the proposed controller is designed assuming that all states of the system are available, in order to improve the control reliability and robustness of the flight system in multi-source interference environments. Then, based on a novel hybrid particle swarm search technique called the Particle Swarm Optimization with Lévy Flight and Wavelet Mutation algorithm, which combines Lévy flight and wavelet mutation, a new parameter identification and tuning algorithm is proposed for the proposed controller to obtain optimal control parameters for attitude and position. To provide a comprehensive comparison, the stability of the optimization algorithm and the robustness performance of the controller were evaluated. Finally, all controllers were tested under three different disturbance scenarios. The results show that the proposed controller can achieve better stability in flight attitude and trajectory tracking tasks compared to Proportion Integration Differentiation controller, Fractional-order Proportion Integration Differentiation controller, and Active Disturbance Rejection controller in all scenarios.

A Survey of Intelligent Driving Vehicle Trajectory Tracking Based on Vehicle Dynamics

<div>Trajectory tracking control, as one of the core technologies of intelligent driving vehicles, determines the driving performance and safety of intelligent driving vehicles and has received extensive attention and research. In recent years, most of the research results of trajectory tracking control are only applicable to conventional working conditions; however, the actual operating conditions of intelligent driving vehicles are complex and variable, so the research of trajectory tracking control algorithm should be extended to the high-speed low-adhesion coefficient, large curvature, variable curvature, and other compound limit working conditions. This requires more consideration of the vehicle dynamics in the controller design. In this article, a comprehensive review of trajectory tracking control under extreme operating conditions is conducted from three levels: vehicle dynamics model, vehicle speed tracking (longitudinal motion control), and path tracking (transverse motion control), and the existing research results are analyzed and summarized to obtain the research trends and pain points and difficulties in each field. On this basis, the future outlook of trajectory tracking control is proposed, which is expected to provide some help and inspiration to the research workers in this field.</div>

Dynamic Model Based Research on Intelligent Vehicle Steering and Obstacle Avoidance

In order to improve road safety, promote the development of autonomous driving, and realize the stability of unmanned vehicles during steering and obstacle avoidance, this paper establishes a lateral dynamic model and a cyclotron curve model based on the 3-degree-freedom vehicle dynamics model. Taking no collision between cars and timely obstacle avoidance as the optimization goal, this paper discretizes the model, a prediction model is established, a soft constraint solver is designed, and a relaxation factor is added to the performance index function. Based on the model prediction and control principle, the obstacle avoidance trajectory planning and tracking of unmanned vehicles is integrated and controlled, and the solution is converted into a quadratic programming problem according to the constraints and rolling optimization characteristics. Finally Matlab/Prescan joint simulation is used to verify the steering and obstacle avoidance quality of the model. The results show that the model has excellent safety, stability and comfort in non-highspeed situations.

Correntropy based model predictive controller with multi-constraints for robust path trajectory tracking of self-driving vehicle

Self-driving vehicles must be equipped with path tracking capability to enable automatic and accurate identification of the reference path. Model Predictive Controller (MPC) is an optimal control method that has received considerable attention for path tracking, attributed to its ability to handle control problems with multiple constraints. However, if the data acquired for determining the reference path is contaminated by non-Gaussian noise and outliers, the tracking performance of MPC would degrades significantly. To this end, Correntropy-based MPC (CMPC) is proposed in this paper to address the issue. Different from the conventional MPC model, the objective of CMPC is constructed using the robust metric Maximum Correntropy Criterion (MCC) to transform the optimization problem of MPC to a non-concave problem with multiple constraints, which is then solved by the Block Coordinate Update (BCU) framework. To find the solution efficiently, the linear inequality constraints of CMPC are relaxed as a penalty term. Furthermore, an iterative algorithm based on Fenchel Conjugate (FC) and the BCU framework is proposed to solve the relaxed optimization problem. It is shown that both objective sequential convergence and iterate sequence convergence are satisfied by the proposed algorithm. Simulation results generated by CarSim show that the proposed CMPC has better performance than conventional MPC in path tracking when noise and outliers exist.

Adaptive robust control for reliable trajectory tracking of autonomous vehicle in uncertain driving environment

External disturbances, parameter perturbance, data delay and control lag provoke significant model mismatches. If not properly compensated, they can greatly deteriorate the control performance of autonomous vehicle, such as reduction of tracking accuracy or even loss of stability in extreme. However, existing approaches barely consider these uncertainties together. In the light of this, an adaptive strategy is presented for trajectory tracking control of autonomous vehicle to simultaneously cope with aforementioned factors. First of all, given the dynamic or kinematic characteristics among path, vehicle and steering actuator, an integrated dynamic model is constructed. To handle the control lag of the steering actuator, a first-order model is utilized to approximate the dynamics of the steering subsystem, which is then integrated into the vehicle dynamics to reformulate the tracking model as a lag-free one. Then, the hierarchical robust tracking controller is proposed to acquire reliable control commands. To prevent the system breakdown in the presence of data delay, the delay-dependent criterion is designed via linear parameter varying technique and integral inequality approach. Moreover, the controllers also consider both the H∞ index and the guaranteed cost one to guarantee the effectiveness and robustness of tracking commands. Subsequently, to enhance the adaptability of algorithm, a feedback gains scheduling mechanism is proposed to adaptively tune tracking commands among different robust gains leveraging the phase plane approach. Finally, several comparative cases are conducted in the hardware-in-the-loop platform to verify that proposed strategy has better capability on trajectory tracking in uncertain driving conditions.

Adaptive Asymmetric Time-Varying Integral Barrier Lyapunov Control-Based Trajectory Tracking of Autonomous Vehicles

This paper investigates the lane following and changing maneuvers of autonomous vehicles in the presence of unknown disturbances, taking into account the dynamic system states and input constraints. The integrated longitudinal-lateral and yaw rate dynamics of the vehicle are simultaneously considered to improve the tracking accuracy and system stability when navigating under critical conditions. Then, an adaptive asymmetric time-varying integral barrier Lyapunov control and dynamic surface control scheme are developed to design the active front steering controller, longitudinal controller, and direct yaw moment control controller, which is capable of constraining the system states and control signals within the predefined boundary. In addition, the radius basis function neural network (RBFNN) is employed to estimate the lumped disturbances caused by the parametric uncertainties, external disturbances, and unmodeled dynamics, and the command filter system is used to avoid the explosion of terms phenomenon. Due to the fast and accurate torque response characteristics of the in-wheel motors, the optimization-based method is then implemented to effectively allocate the driving/braking torque to each in-wheel motor so as to improve vehicle performance. The stability of the closed-loop system is comprehensively demonstrated by means of the Lyapunov theory. Finally, the quantitative and qualitative comparisons in different diving scenarios using the Carim-Simulink joint environment are carried out to illustrate the effectiveness and validation of the proposed method.

Coupled lateral and longitudinal trajectory tracking control with a modified vehicle kinematics model for autonomous vehicles

Abstract The paper proposes a coupled lateral and longitudinal trajectory tracking control algorithm based on model predictive control (MPC) to enhance the precision and stability of trajectory tracking of autonomous vehicles. The coupled MPC controller is developed based on a modified vehicle kinematics model, which simultaneously takes the steering angle and the longitudinal speed as control variables. The modified vehicle model and the coupled MPC controller are verified by CarSim and MATLAB/Simulink co-simulation experiments. Simulation results demonstrate that, at the cost of 12 percent increase in average computational time, the proposed coupled MPC controller leads to an approximate 48 percent reduction in tracking error compared to the benchmarking coupled lateral and longitudinal MPC controller based on the classic kinematics model.

Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing

AbstractIn this study, a practical adaptive control scheme is proposed for the trajectory tracking of an unmanned surface vehicle via the characteristic modelling approach. Therefore, accurate tracking control can be achieved in the presence of unknown time‐varying model parameters and environmental disturbances. The control scheme comprises a trajectory guidance module based on the virtual target approach and a tracking control module designed by characteristic modelling theory. Firstly, the ideal control commands of the yaw speed and surge speed are generated using the position errors between the vehicle and the virtual target. Then, a second‐order characteristic model for the heading and surge speed channel is developed. The parameters of the model are updated by a real‐time parameter identification algorithm. Based on this model, an integrated adaptive control law is designed which consists of golden‐section control, feed‐forward control and integral control. Finally, the development processes of the vehicle platform and the control algorithms are described, and the results of simulation and field experiments are presented and discussed.

Research on automatic driving control strategy based on improved model predictive control

As a new technology direction in the automotive industry in recent years, automatic driving technology has played an important role in reducing traffic accidents, promoting energy conservation and emission reduction, and improving the efficiency of high-speed and urban roads. As one of the key technologies of autonomous vehicles, the performance of control algorithm greatly affects the smoothness of vehicle driving and the accuracy of track tracking. In order to consider the lateral control and longitudinal control accuracy at the same time, and ensure that the vehicle travels according to the given trajectory, this paper first establishes the vehicle kinematics model, and improves the traditional model predictive control algorithm, which realizes the high dynamic control of the desired trajectory while ensuring the vehicle stability and comfort. Finally, based on the built real vehicle test platform for automatic driving, the acceleration and deceleration test and the high-speed serpentine test are designed. By analyzing the test data, it can be seen that the control algorithm proposed in this paper can effectively achieve the vehicle trajectory tracking, and the control accuracy is improved.

Three-Dimensional Trajectory Tracking of AUV Based on Nonsingular Terminal Sliding Mode and Active Disturbance Rejection Decoupling Control

This paper presents a nonsingular terminal sliding mode and active disturbance rejection decoupling control (NTSM-ADRDC) scheme for the three-dimensional (3D) trajectory tracking of autonomous underwater vehicles (AUV). Firstly, the AUV model is decoupled into five independent single input–single output (SISO) channels using ADRDC technology. Secondly, the NTSM-ADRDC controller is designed. The linear extended state observer (LESO) is used to observe the AUV state variables, and estimate the total disturbance of the system. In addition, to improve the system error convergence rate, the combination of exponential reaching rate and NTSM constitutes a nonlinear states error feedback control law for the controller. Finally, the stability of the proposed control law is proved using the Lyapunov theory. The simulation results demonstrate the effectiveness and robustness of the designed NTSM-ADRDC trajectory tracking approach.

Predefined-Time Fault-Tolerant Trajectory Tracking Control for Autonomous Underwater Vehicles Considering Actuator Saturation

This paper presents the design of two predefined-time active fault-tolerant controllers for the trajectory tracking of autonomous underwater vehicles (AUVs) which can address actuator faults without causing actuator saturation. The first controller offers improved steady-state trajectory tracking precision, while the second ensures a nonsingular property. Firstly, a predefined-time sliding mode controller is formulated based on a predefined-time disturbance observer by integrating a novel predefined-time auxiliary system to prevent the control input from exceeding the actuator’s physical limitations. Subsequently, a non-singular backstepping controller is introduced to circumvent potential singularities in the sliding mode controller, guaranteeing that the trajectory tracking error is uniformly ultimately bounded (UUB) within the predefined time. Additionally, theoretical analysis and simulation results are presented to illustrate the advantages of the proposed method.

A Double-Layer Model Predictive Control Approach for Collision-Free Lane Tracking of On-Road Autonomous Vehicles

This paper proposes a double-layer model predictive control (MPC) algorithm for the integrated path planning and trajectory tracking of autonomous vehicles on roads. The upper module is responsible for generating collision-free lane trajectories, while the lower module is responsible for tracking this trajectory. A simplified vehicle model based on the friction cone is proposed to reduce the computation time for trajectory planning in the upper layer module. To achieve dynamic and accurate collision avoidance, a polygonal distance-based dynamic obstacle avoidance method is proposed. A vertical load calculation method for the tires is introduced to design the anti-rollover constraint in the lower layer module. Numerical simulations, with static and dynamic obstacle scenarios, are conducted on the MATLAB platform and compared with two state-of-the-art MPC algorithms. The results demonstrate that the proposed algorithm outperforms the other two algorithms regarding computation time and collision avoidance efficiency.

Neural network optimization algorithm based non-singular fast terminal sliding-mode control for an uncertain autonomous ground vehicle subjected to disturbances

As computer computing capabilities increase, optimization algorithms are becoming more useful for solving engineering problems. Up to now, several metaheuristic algorithms have been exploited in control engineering. However, this effort remains weak in addressing the autonomous ground vehicles (AGVs) trajectory tracking problem. This research presents a novel optimal approach merging the robust non-singular fast terminal sliding-mode control method (NFTSMC) and the neural network optimization algorithm (NNA) for automatic lane changing. First, a reference double lane-change path (DLC) is designed, and the robust non-singular fast terminal sliding-mode steering controller is developed, according to Lyapunov stability theory, to suppress the lateral deviation and ensure the yaw stability. Then, the control strategy is optimized by the NNA algorithm to adjust the steering controller optimally while avoiding local optimums. A comparison, under the same conditions, with the particle swarm optimization algorithm (PSO) revealed the superiority of the control law resulting from the NNA-based optimization. Furthermore, the proposed approach shows its excellent tracking performance versus the integrated backstepping sliding-mode controller (IBSMC) and the adaptive sliding-mode control (ASMC) under severe conditions typical of real-world lane changes.

Adaptive practical prescribed-time fault-tolerant control for autonomous underwater vehicles trajectory tracking

This article investigates the practical prescribed-time trajectory tracking control problem for AUVs with parametric uncertainties under tracking error constraints and velocity constraints. A settling time regulator is combined to construct the performance function, with which we build a new adaptive fault-tolerant control scheme upon the transferred error, ensuring that the tracking error converges to an adjustably small residual set within the prescribed time with a continuous control action. Furthermore, with the specific velocity transformation techniques, the problem of velocity constraints is transformed into the boundedness of new variables, thus the feasibility conditions are eliminated, and the requirements of constrained boundary functions are relaxed. Theoretical analysis shows that the proposed control method is an effective way to achieve pre-given tracking precision within the prescribed time, while avoiding the issue of explosion of complexity. The prescribed time is independent of system initial conditions and other parameters, and the tracking error will converge asymptotically. Simulation results further demonstrate the effectiveness of the proposed approach.

Fuzzy predictive Stanley lateral controller with adaptive prediction horizon

The challenge of trajectory tracking of autonomous vehicles (AVs) is a critical aspect that must be effectively addressed. Recent studies are concerned with maintaining the yaw stability to guarantee the customers’ comfort throughout the journey. Most of the geometrical controllers solve this task by dividing it into consecutive point stabilization problems, limiting the controllers’ ability to handle sudden trajectory changes. One research presented a predictive Stanley lateral controller that uses a discrete prediction model to mimic human behavior by anticipating the vehicle’s future states. That controller is limited in its use, as the parameters must be manually tuned for every change in the maneuver or vehicle velocity. This article presents a novel approach for trajectory tracking in autonomous vehicles, by introducing a fuzzy supervisory controller that automatically adapts to changes in the vehicle’s velocity and maneuver by estimating the prediction horizon’s length and providing different weights for each controller. The proposed method overcomes the limitations of traditional controllers that require manual tuning of parameters, making it ready for real-world experiments. This is the main contribution of the research in this paper. The suggested technique demonstrated an advantage over the Basic Stanley controller and the manually tuned predictive Stanley controller in terms of the total lateral error and the model predictive control (MPC) in terms of computational time. The performance is determined by performing various simulations on V-Rep and hardware-in-the-loop (HIL) experiments on an E-CAR golf bus. A broad selection of velocities is used to validate the behavior of the vehicle while working on different maneuvers (double lane change, hook road, S road, and curved road).

State constrained control strategy for unmanned surface vehicle trajectory tracking based on improved barrier Lyapunov function

This paper addresses the state-constrained control of the symmetric underactuated unmanned surface vehicles (USVs) under unknown dynamics and time-varying interference based on improved Lyapunov function. Inspired by the prescribed performance control (PPC), the improved logarithmic barrier Lyapunov function (BLF) is proposed with a reasonably defined performance function in advance. The tracking errors of position and yaw direction are constrained within the set range. Meanwhile, the symmetric barrier Lyapunov function (SBLF) and dynamic surface control (DSC) are introduced to simplify the controller design and indirectly restrict the yaw velocity. On this basis, the finite-time disturbance observer (FDO) is proposed to estimate the unknown disturbance. The finite-time auxiliary dynamic system is introduced to deal with the input saturation. In addition, the neural network minimum learning parameter (MLP) can reduce the computational complexity while figuring out the unknown dynamics. Finally, numerical simulation verifies the effectiveness of the control system.