Autonomous Vehicle Path Tracking Research Articles

Ultra-fast tuning of neural network controllers with application in path tracking of autonomous vehicle

Neural network (NN) controllers have shown great potential in solving complex control or decision-making tasks. However, most of the NN controllers either rely on the availability of large datasets or require dense interactions with the environment, which hinders their application in real systems. In this paper, we introduce a model-based reinforcement learning (MBRL) algorithm, aimed at realizing ultra-fast tuning of deep NN controller from a small sample set of real-world data. The algorithm uses Gaussian processes (GPs) to model the unknown dynamics of real system and updates controller parameters through stochastic gradient descent. By using particle-based method for long-term predictions, the algorithm can easily incorporate online state estimators and filters into controller learning, which is conductive to learning from systems with partially measurable states and stochastic control delay. We apply the algorithm to calibrate a deep NN controller for the path tracking of a full-size autonomous vehicle (AV). Simulation and field test results show that the deep NN controller can be well calibrated after only one interaction with the environment and can achieve similar tracking performance to optimization-based methods such as nonlinear model prediction control (NMPC) in various test scenarios by combining with a feed-forward pure pursuit (PP) controller.

Deviation Sequence Neural Network Control for Path Tracking of Autonomous Vehicles

Despite its excellent performance in path tracking control, the model predictive control (MPC) is limited by computational complexity in practical applications. The neural network control (NNC) is another attractive solution by learning the historical driving data to approximate optimal control law, but a concern is that the NNC lacks security guarantees when encountering new scenarios that it has never been trained on. Inspired by the prediction process of MPC, the deviation sequence neural network control (DS-NNC) separates the vehicle dynamic model from the approximation process and rebuilds the input of the neural network (NN). Taking full use of the deviation sequence architecture and the real-time vehicle dynamic model, the DS-NNC is expected to enhance the adaptability and the training efficiency of NN. Finally, the effectiveness of the proposed controller is verified through simulations in Matlab/Simulink. The simulation results indicate that the proposed path tracking NN controller possesses adaptability and learning capabilities, enabling it to generate optimal control variables within a shorter computation time and handle variations in vehicle models and driving scenarios.

The fuzzy system based on vague partitions and its application to path tracking control for autonomous vehicles

As significant carriers of the application of fuzzy set theories, fuzzy systems have been widely used in many fields. However, selecting fuzzifications, fuzzy reasoning engines, and defuzzifications is subjective for Mamdani fuzzy systems, and the fuzzy rule of Takagi-Sugeno-Kang fuzzy systems is less of a linguistic interpretation. Regarding these shortcomings, this paper proposes a fuzzy system based on vague partitions processing information directly from the fuzzy rule base, in which fuzzy rules have explicit semantics. Firstly, the n-dimensional vague partition of the n-dimensional universe is defined based on 1-dimensional vague partitions and the aggregation function, and its properties are discussed. Based on these, we design the new fuzzy system, and investigate its approximation properties which is the theoretical guarantee for applying the fuzzy system. As an application, we combine the fuzzy system with PID control system to deal with autonomous vehicle path tracking control problems. A series of experiments are constructed, and experimental results indicate that the fuzzy system based on vague partitions makes the fuzzy PID control system strong robustness, and has obvious advantages compared with other traditional fuzzy systems for path tracking control problems.

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

Path Tracking of Autonomous Vehicle Based on NMPC With Pre-Steering

Path Tracking of Autonomous Vehicle Based on NMPC With Pre-Steering

Path tracking of varying-velocity 4WS autonomous vehicles under tire force friction ellipse constraints

In this paper, a control scheme is presented to solve the problem of path tracking for varying-velocity autonomous vehicles with higher robust performance while friction force constraints of vehicle tires and ground are considered to avoid appearing the steering lost situation actively in the limit of the tire friction forces by using four wheel steering. To achieve these goals, a new expression of the tire forces and a new controlled system are proposed for the three degree of freedom vehicle model. A longitudinal input-saturation controller is designed to track the desired longitudinal velocity and attenuate the influence of lateral and yaw motions. The virtual control input method is used to design the input-saturation controller and get the desired steering angles to track the desired path for the lateral and yaw motions while a new control problem with desired forces tracking is considered to autonomously avoid the limit of lateral tire friction forces and the steering lost situation appearing. To test these results, lane changing, U-corner turning and straight-line driving for a varying-velocity vehicle are simulated with external disturbances and model uncertainties. Simulation results show that the higher robustness and precise path tracking performances are obtained.

Disturbance-Observer-Based Barrier Function Adaptive Sliding Mode Control for Path Tracking of Autonomous Agricultural Vehicles with Matched-Mismatched Disturbances

Disturbance-Observer-Based Barrier Function Adaptive Sliding Mode Control for Path Tracking of Autonomous Agricultural Vehicles with Matched-Mismatched Disturbances

Comparative Study on Coordinated Control of Path Tracking and Vehicle Stability for Autonomous Vehicles on Low-Friction Roads

This paper presents a comparative study on coordinated control of path tracking and vehicle stability for autonomous vehicles on low-friction roads. Generally, a path-tracking controller designed on high-friction roads cannot provide good performance under low-friction conditions. To cope with the problem, a coordinated control between path tracking and vehicle stability has been proposed to date. In this paper, three types of coordinated controllers are classified according to the controller structure. As an actuator, front-wheel steering, four-wheel steering, and four-wheel independent braking and driving are adopted. A common feature of these types of controllers is that front steering and yaw moment control are adopted as control inputs. To convert the yaw moment control into tire forces generated by combinations of multiple actuators, a control allocation method is applied. For each type, a controller is designed and simulated using vehicle simulation software. From the simulation results, a performance comparison among those controller types is carried out. Through comparison, it is shown that there are small differences among those types of controllers in terms of path tracking.

An improved model predictive control method for path tracking of autonomous vehicle considering longitudinal velocity

In order to increase the accuracy of the path tracking, an improved model predictive control (IMPC) is proposed for autonomous vehicle under road conditions of large curvature, which can enhance the performances of the driving stability and safety. The controller design is implemented in four steps. First, the curvature of road ahead is derived and applied to determine the longitudinal velocity. Thus, the longitudinal velocity is not assumed to be constant, which is the salient feature of the proposed control. Second, the kinematic model of vehicle is established by the Ackermann steering principle. Third, the predictive model is constructed by linearization and discretization of the kinematic model. Fourth, the longitudinal velocity and the front steering angle are imposed on hard constraints, and the constrained objective function is designed and composed of the position deviation and the control increment. Then, we can obtain the optimal results of the longitudinal velocity and the front steering angle. Furthermore, experiment and simulation on the path tracking of an autonomous vehicle are presented. The results show that the proposed control can realize excellent tracking performance under the road conditions of large curvature.

Fixed-time generalized super-twisting control for path tracking of autonomous agricultural vehicles considering wheel slipping

Efficient path tracking control for autonomous agricultural vehicles (AAVs) is the key technology for realizing agriculture 4.0 and precision agriculture, which makes smart farms more sustainable, profitable, and eco-friendly. It is well recognized that model nonlinearity, unknown disturbances, and wheel slipping are common problems in practical AAV systems in operation. To tackle these issues and improve the path tracking performance, a novel fixed-time generalized super-twisting control scheme is proposed for AAVs in this paper. First, a fixed-time integral sliding mode surface is designed for the considered vehicle by fusing the lateral offset and heading offset together. Then, an innovative auxiliary system dynamics is constructed to collaborate with the foregoing sliding mode surface, compensating for the adverse effects of wheel slipping effectively. Subsequently, based on the kinematic offset model of AAVs, a new type of generalized super-twisting controller is designed to achieve superior path tracking with robustness. Unlike existing super-twisting controllers, the generalized super-twisting continuous controller can fully eliminate chattering by replacing the previous discontinuous terms with a nonsmooth term. Further, rigorous theoretical analysis is conducted by raising a fresh Lyapunov function to verify the closed-loop control system stability. Finally, extensive field experiments have been carried out to show that the proposed control scheme significantly outperforms typical comparative control schemes.

Neural Network Approach Super-Twisting Sliding Mode Control for Path-Tracking of Autonomous Vehicles

This paper proposes a neural network approach adaptive super-twisting sliding mode control algorithm for autonomous vehicles. An adaptive and robust control algorithm in autonomous vehicles is needed to compensate for disturbance and parametric uncertainty from the variable environment and vehicle conditions. The sliding mode control (SMC) is a robust controller that compensates for robust and reasonable control performance against disturbance and parametric uncertainty. However, the inherent limitation of the sliding mode control, namely the chattering phenomenon, has a negative effect on the system. Additionally, when the disturbance exceeds the defined boundaries, the control stability is compromised. To overcome these limitations, this study incorporates the radial basis function neural network (RBFNN) and Lyapunov function to estimate disturbance and parametric uncertainty. The estimated disturbance is reflected in the super-twisting sliding mode control (STSMC) to reduce the chattering phenomenon and achieve enhanced robust performance. The performance evaluation of the proposed neural network approach control algorithm is conducted using the double lane change (DLC) scenario and rapid path-tracking (RPT) scenario, implemented in the CarMaker and Matlab/Simulink environments, respectively.

Cooperative learning formation control of multiple autonomous underwater vehicles with prescribed performance based on position estimation

This paper proposes a cooperative learning formation control method with finite-time prescribed performance based on position estimation for parametric path tracking of multiple autonomous underwater vehicles (AUVs) with uncertainties and external disturbances. The parametric path used in the control law permits the velocity to be specified independently while tracking the path accurately. The localized radial basis function neural networks learn the uncertainties cooperatively while tracking the period path, and the knowledge gained from learning is utilized to construct an empirically based formation control law using experience to cope with similar uncertainties rather than repeatedly using adaptive methods, which reduces the computing burden. The position of the leader is assumed to be available only for the leader's neighboring AUV, and a novel finite-time distributed observer is presented for the followers to estimate the leader's position. Based on this, the control law is derived from the prescribed performance control method using a finite-time performance function rather than exponential decaying function to enable the tracking error converges in finite time, which accelerates the learning process. The simulation results confirm the validity of the presented control protocol.

Online Learning-Informed Feedforward-Feedback Controller Synthesis for Path Tracking of Autonomous Vehicles

High-performance path tracking is a key technology for autonomous vehicles. Feedforward-feedback control architectures are suitable for accurate path tracking with adequate margins of stability. For system modelling in the feedforward component, the learning-based method has been proven to be a promising approach owing to its model-free framework. However, the offline-learned data model trained with collection data is confined by its feature space, resulting in insufficient generalization. As a solution, in this study, we introduce an online learning network – the recurrent high-order neural network (RHONN) – to characterize vehicle behaviors. The RHONN is used to feature vehicle behaviors in a timely manner with a high fidelity and flexible form. The equilibrium at the preview point on the desired path is found based on the online-identified RHONN model, and its induced steering angle is taken as the feedforward command. For the feedback steering controller, the preview point position-based control law incorporating the steady vehicle sideslip angle is adopted to enhance the stability performance. Finally, in the CarSim/Simulink environment, the performance of the designed RHONN-informed feedforward and feedback controller is validated in two typical scenarios – double-lane change and single-turn. The validation results reveal that the proposed approach offers better tracking accuracy in linear and nonlinear regions than other techniques. More notably, the average execution time (3.55 ms) is less than the sampling frequency of the controller (50 ms), which further confirms the applicability and efficiency of the proposed approach.

Development of a Particle Filter-Based Path Tracking Algorithm of Autonomous Trucks with a Single Steering and Driving Module Using a Monocular Camera

Recently, in various fields, research into the path tracking of autonomous vehicles and automated guided vehicles has been conducted to improve worker safety, convenience, and work efficiency. For path tracking of various systems applied to autonomous driving technology, it is necessary to recognize the surrounding environment, determine technology accordingly, and develop control methods. Various sensors and artificial-intelligence-based perception methods have limitations in that they must learn a large amount of data. Therefore, a particle-filter-based path tracking algorithm using a monocular camera was used for the recognition of target RGB. The path tracking errors were calculated and a linear-quadratic-regulator-based desired steering angle were derived. The autonomous trucks were steered and driven using a pulse-width-modulation-based steering and driving motor. Based on an autonomous truck with a single steering and driving module, it was verified that the path tracking could be used in three evaluation scenarios. To compare the LQR-based path tracking control performance proposed in this paper, an elliptical path tracking scenario using a conventional sliding mode control with robust control performance was performed. The results show that the RMS of the lateral preview error of the SMC was approximately 18% larger than that of the LQR-based method.

Dynamic Drifting Control for General Path Tracking of Autonomous Vehicles

Taking full advantage of tire saturation in high sideslip drifting maneuvers can substantially improve the handling limits of autonomous vehicles. However, most singular motion control methods in conventional drifting controllers have been conducted, which cannot meet general driving needs. In this paper, a hierarchical dynamic drifting controller (HDDC) is designed under both drifting maneuvers and typical cornering maneuvers to uniformly track the general path within and beyond stability limits. Specifically, the HDDC includes the path tracking layer, vehicle motion control layer, and actuator regulating layer. The first layer is designed to achieve accurate path tracking and generate the desired states, and it is algorithmically adaptable and confirmed by MPC and LQR. The second layer is proposed to integrate drifting and typical cornering control by the dynamic drifting inverse model. The third layer can achieve the steering system and wheel speed control to satisfy corresponding commands. Ultimately, the real-time performance of the controller is verified by the ECU hardware-in-the-loop platform. The results with MPC-HDDC and LQR-HDDC show that the HDDC can achieve integrated control of drifting and typical cornering maneuvers and possess high tracking accuracy.

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

As the bottom layer of the autonomous vehicle, path tracking control is a crucial element that provides accurate control command to the X-by-wire chassis and guarantees the vehicle safety. To overcome the deterioration of control performance for autonomous vehicle path-tracking controllers caused by modeling errors and parameter perturbation, an adaptive robust control framework is proposed in this paper. Firstly, the 2-DOF vehicle dynamic model is established and the non-singular fast terminal sliding mode control algorithm is adopted to formulate the control law. The unmeasured model disturbance and parameter perturbation is regarded as the system uncertainty. To enhance the control accuracy, the radial basis forward neural network is introduced to estimate such uncertainty in real time. Then, the dynamic model of an active front steering system is established. The model reference control algorithm is applied for the steering torque control considering model uncertainty brought by the dissipation of manufacturing and mechanical wear. Finally, the Simulink–CarSim co-simulation platform is used and the proposed control framework is validated in two test scenarios. The simulation results demonstrate the proposed adaptive robust control algorithm has satisfactory control performance and good robustness against the system uncertainty.

NMPC-Based Path Tracking Control Strategy for Autonomous Vehicles With Stable Limit Handling

A novel nonlinear model predictive control (NMPC) strategy for path tracking of autonomous vehicles with stable limit handling is proposed in this article. To improve the path tracking performance under complex driving maneuvers, a tire lateral force model, which treats road adhesion coefficient and vertical load as variable parameters, is discussed first. It has the advantage that the dynamics model can be applied under different driving conditions. In addition, the stability properties of the dynamics system with the front tire lateral force as the virtual control input are analyzed. The global asymptotical stability with zero input and local input-to-state stability with nonzero input of the dynamics system are proved respectively. To perform path tracking within the handling limits, both constraints of front tire lateral force and rear tire slip angle are included in the NMPC controller. The constraint of front tire lateral force is considered as peak limits related to load transfer and adhesion coefficient. And for the rear tire slip angle, a combining constraint scheme is proposed, where the indirect constraint is implemented by a penalty term that predicts the saturation of rear tire lateral force, while the direct constraint is implemented by a dead-zone exponential penalty function. The Continuation/Generalized Minimum Residual (C/GMRES) algorithm is applied to improve the computation efficiency of the NMPC. The performance of the controller is evaluated in simulations and hardware-in-the-loop (HIL) tests, and the results show that the controller is able to track the reference path with high precision in real-time.

Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow

This paper presents a nonlinear model predictive control with terminal cost (NMPC–WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles. The path tracking issue is formulated as an optimal control problem. In order to improve the squeezing phenomenon of traditional NMPC, a discrete-time nonlinear model predictive controller with terminal cost is then designed, in which the state error of last step is augmented. The cost function of NMPC–WTC consists of two parts: (1) the traditional NMPC cost function responding to tracking errors and controller output, and (2) the augmented terminal cost. The algorithm was implemented on CasADi numerical optimization framework, which is free, open-source and developed for nonlinear optimization. The open-loop and closed-loop simulation results are then presented to demonstrate the improved performance in tracking accuracy and stability compared to traditional model predictive controller.

Robust path following control for autonomous vehicle considering actuator fault

The development of drive-by-wire chassis technology increases the flexibility of vehicle dynamic control. However, more actuators have higher potential failure risk and lead model mismatch, which brings a severe challenge for the control precision and system robustness. Regarding to the potential failure risk of the steering actuator, this research presents a feedback gain scheduling H∞ controller for autonomous vehicle path tracking. Firstly, the linear parameter-varying (LPV) model is established considering the speed variation and potential actuator fault. Then the state feedback control algorithm is proposed to maintain the tracking performance and system robustness. The control gain matrix is optimized via linear matrix inequation (LMI). Finally, the closed loop system is proved to be asymptotic stable by Lyapunov function and the simulation results demonstrate that the proposed controller has satisfactory tracking performance under unpredictable actuator fault degree, which provides good robustness and better reliability for autonomous vehicle in complex conditions.