Path Tracking Control Of Vehicles Research Articles

Research on dynamic constraint mechanism and motion control of intelligent vehicles based on preview distance optimization under complex road conditions

Aiming at the coupling and conflict issues between intelligent vehicle dynamics characteristics and path tracking system under complex road conditions, this paper investigates the dynamics constraint mechanism and motion control of intelligent vehicles, and proposes an intelligent vehicle lateral–vertical cooperative control method based on optimized preview distance. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicle. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the “human-vehicle-road” system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, hardware-in-the-loop platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 m, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions.

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.

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.

Vehicle Path Tracking Control Using Pure Pursuit With MPC-Based Look-Ahead Distance Optimization

Vehicle Path Tracking Control Using Pure Pursuit With MPC-Based Look-Ahead Distance Optimization

Finite-Time Autonomous Vehicle Path Tracking Control Based on State Constraint

Finite-Time Autonomous Vehicle Path Tracking Control Based on State Constraint

Intelligent vehicle path tracking control strategy considering data-driven dynamic stable region constraints

The path tracking controller can easily reduce the tracking error, but often exceed the limitations of vehicle stability. In this paper, an intelligent vehicle path tracking control strategy considering data-driven dynamic stable region constraints is proposed. Firstly, based on the two-degree-of-freedom (DOF) vehicle model and nonlinear tire model, the vehicle sideslip angle-sideslip angular velocity ([Formula: see text]) phase plane is established. Then, the stable region dataset is made considering the influence of vehicle speed, adhesion coefficient, and front wheel angle. To get the vehicle driving stable region, a back propagation neural network (BP-NN) regression model is trained offline. Subsequently, a path tracking control strategy based on adaptive-model predictive control (MPC) is designed, which considers the vehicle dynamic stable region constraints with the BP-NN predicting online. Finally, model-in-the-loop (MIL) and driving simulator is designed to test the control strategy, which indicates that it has a better performance compared with the linear quadratic regulator (LQR) path tracking controller.

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle.

Relaxed Actor-Critic With Convergence Guarantees for Continuous-Time Optimal Control of Nonlinear Systems

This paper presents the Relaxed Continuous-Time Actor-critic (RCTAC) algorithm, a method for finding the nearly optimal policy for nonlinear continuous-time (CT) systems with known dynamics and infinite horizon, such as the path-tracking control of vehicles. RCTAC has several advantages over existing adaptive dynamic programming algorithms for CT systems. It does not require the ``admissibility" of the initialized policy or the input-affine nature of controlled systems for convergence. Instead, given any initial policy, RCTAC can converge to an admissible, and subsequently nearly optimal policy for a general nonlinear system with a saturated controller. RCTAC consists of two phases: a warm-up phase and a generalized policy iteration phase. The warm-up phase minimizes the square of the Hamiltonian to achieve admissibility, while the generalized policy iteration phase relaxes the update termination conditions for faster convergence. The convergence and optimality of the algorithm are proven through Lyapunov analysis, and its effectiveness is demonstrated through simulations and real-world path-tracking tasks.

Continuous-Time Receding-Horizon Estimation via Primal-Dual Dynamics and Stability Analysis

Receding-Horizon Estimation (RHE) is an optimal filterling approach which uses past series of the plant's measured output and input and finds estimated states based on linear programming or quadratic programming. It is known that RHE can estimate the plant state to which the Kalman filter cannot be applied due to modeling errors. This paper considers the new computational form of RHE based on the principal dual gradient algorithm. The proposed form is expressed by the dynamical system, so we can consider the computational stability based on the dynamical system theory. This paper discusses the continuous-time representation of the RHE algorithm (continuous-time RHE) and filter characteristics to improve the convergence performance of the estimation. On the basis of the small gain theorem, the dynamics of RHE is analyzed. Also, the characteristics of continuous-time RHE is demonstrated via a vehicle path tracking control problem.

Intelligent vehicle path tracking control based on LQR and curvature feedforward

Intelligent vehicle path tracking control based on LQR and curvature feedforward

Robust sliding mode prediction path tracking control for intelligent vehicle

Because of the characteristics of nonlinearity, strong coupling, fast time-varying, and uncertainty of the intelligent vehicle movement, the accurate path tracking control of the intelligent vehicle becomes a challenge. Considering a variety of complex constraints from the control system, this article combines sliding mode control and model prediction control to improve the intelligent vehicle real-time path tracking performance and proposes a robust sliding mode predictive controller for intelligent vehicle path tracking control. The sliding mode predictive controller uses the sliding mode surface in sliding mode control to construct the prediction model and corrects the sliding mode control output through continuous feedback correction and rolling optimization and then obtains the optimal control input which makes sure optimal performance control under system various constraints. The sliding mode predictive controller can simultaneously consider the tracking performance and system complex constraints, which effectively improves the dynamic performance and robustness of the control system. Related simulation results show that sliding mode predictive controller not only solves the problem of system constraints but also improves the accuracy of intelligent vehicle path tracking and the robustness of the system.

Research on long-wheelbase vehicle path tracking control based on optimal heading angle

Path tracking control technology is a hot topic in the research of intelligent vehicles. It is an important link to realize intelligent transportation to solve the problem of path tracking control for long-wheelbase vehicles. Aiming at the problem that the vehicle body of long wheelbase is prone to collision with the surrounding environment due to the influence of the vehicle axle length in the process of turning, the overall deviation model of vehicle wheelbase and reference path is creatively proposed, and the optimal heading angle of long-wheelbase vehicle in the process of turning is determined by solving the maximum value of the overall deviation model. The MPC path tracking controller is established based on the deviation between the heading angle and the actual vehicle. Eventually established more than a typical working condition in MATLAB and TRUCKSIM joint simulation, to verify the proposed algorithm not only has realized the long-wheelbase vehicle path tracking control and front wheel position closer to the reference curve, effective in reducing long-wheelbase vehicle steering wheel swept area, in the process of reducing long-wheelbase vehicle steering and road environment in the process of collision probability. It improves the trafficability of long-wheelbase vehicles in narrow areas.

Robust Adaptive Path-Tracking Control of Autonomous Ground Vehicles With Considerations of Steering System Backlash

Backlash phenomena have haunted control engineers for ages. Concerning the ground vehicle path-tracking control system design, the steering system's backlash attribute is often overlooked. If not properly compensated, the backlash nonlinearity tends to provoke unfavorable consequences including erroneous positioning of the road-wheel steering angle, response chattering, limit-cycle-like oscillations, and delays. This paper pioneers to employ an adaptive inverse controller to offset the dynamics of the steering system's backlash. Overall, the path-following objective is accomplished via a novel trio-loop control architecture, which decomposes the tracking objectives into kinematic, vehicular lateral dynamics, and steering backlash compensation sub-levels. All adaptive control laws are robustified by means of sigma modification. Hardware-in-the-loop experimental results are presented to demonstrate the superiorities of the proposed solution.

Rendering bounded error in adaptive robust path tracking control for autonomous vehicles

For the sake of safety, the vehicle path tracking control should not only ensure the stability of the path tracking error containing the lateral offset and the orientation error but also guarantee that both the transient and steady states of the lateral offset are within a specified safe boundary. However, the time-varying uncertainties of a vehicle system make the control design a tough task. This paper develops an adaptive robust control (ARC) which guarantees both the tracking stability and the bounded error property for autonomous vehicles. First, to handle the bounded error requirement, a barrier function based state transformation which converts the constrained lateral offset into an unconstrained state is proposed. Then, the path tracking control task is cast into an equality constraint of the system state. On this basis, a novel adaptive robust constraint-following controller is developed to make the transformed system follow the proposed equality constraint. Through Lyapunov minimax analysis, it is proved that the resulting control guarantees the approximate constraint-following performance and the bounded error property despite the presence of system uncertainties. Finally, the main theoretical results are verified through CarSim-Simulink co-simulation results.

Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability

The antidisturbance control problem of autonomous vehicle path tracking considering lateral stability is studied in this paper. This paper proposes an improved active disturbance rejection control (IADRC) control method including an improved extended state observer (IESO) and an error compensator based on LQR, where a new continuous nonlinear function is proposed in the IESO instead of the classical piecewise function. Based on the IADRC, an autonomous vehicle path-tracking controller considering lateral stability is designed. Using the output wheel steering angle and external yaw moment, the IESO estimates the disturbance value and compensates for the disturbance in the feedback to meet the goal of antidisturbance control. Based on the concept of control allocation (CA), the control distributor is designed to distribute the external yaw moment to the four wheels in a reasonable and optimal way to achieve differential braking. Finally, the control scheme is evaluated in the form of CarSim/Simulink cosimulation; the results show that the proposed autonomous vehicle path-tracking control scheme has better path-tracking effect and higher antidisturbance robustness.

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using CarSim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

Nonlinear Tire Model Approximation Using Machine Learning for Efficient Model Predictive Control

Model Predictive Controller (MPC) is widely used as a technique for path tracking control since it allows for dealing with system constraints and future forecasts. However, the performance of MPC is directly affected by the adopted model. A complex dynamic model can guarantee accuracy in path tracking but may not be suitable in computational terms. On the other hand, a simplified model may not capture essential nonlinear aspects. Thus, to cope with these problems, this paper deals with data-driven tire modeling to improve autonomous ground vehicle path tracking control. The main contribution of the present work is to show that neural tires can capture the nonlinearities present in the interaction between lateral and longitudinal vehicle dynamics, with a reduced computational cost for predictive controllers. Simulated and experimental tire data are approximate to design data-driven tire models using radial basis function and multilayer perceptron neural networks. Then, based on ground vehicles with neural tires, model predictive controllers are designed to regulate wheel torque and steering angle inputs. Comparative tests were conducted to compare the proposed data-driven MPC approach with the classical nonlinear MPC controller. The results show that the neural tires approximate nonlinear tire models and experimental data with arbitrary precision in terms of accuracy and error-based metrics. The proposed methodology was successfully applied to perform trajectory and velocity tracking of ground vehicles. In addition, MPC with a neural tire model as prediction inference reduces the computational effort compared to traditional approaches.

Path tracking control of an autonomous vehicle with model-free adaptive dynamic programming and RBF neural network disturbance compensation

The performance of the model-based controller is always affected by the uncertainty and nonlinearity of the model parameters in the vehicle path tracking process. To address this issue, a novel path tracking controller based on model-free adaptive dynamic programming (ADP) is proposed for autonomous vehicles in this paper. To be specific, the proposed controller obtains information from the online state and front-wheel angle input data which are repeatedly used to calculate the controller gain iteratively. So, this controller features not requiring accurate knowledge of vehicle model parameters for controller development. Meanwhile, the path tracking performance of the autonomous vehicle will be inevitably disturbed by unknown nonlinear external disturbance. To approximate this disturbance, the learning characteristics of Radial Basis Function Neural Network (RBFNN) are applied to generate compensation for the front-wheel angle. Afterward, the weight updating law of RBFNN is derived by Lyapunov function to ensure the stability and convergence of the whole system. Finally, Hardware in the loop (HIL) test results demonstrate that the proposed ADP-RBF controller can improve the comprehensive performance of the vehicle path tracking control system and achieve the balance between path tracking accuracy and minimum sideslip angle.

Cooperative-game-theoretic optimal robust path tracking control for autonomous vehicles

Modeling uncertainties are a major concern in vehicle path tracking control. As a practical engineering system, the uncertainties in vehicle lateral dynamics can be time-varying while bounded and have certain distributions wherein. The fuzzy set theory can effectively describe system uncertainties in terms of boundary and distribution. Contrary to fuzzy logic-based approaches, this article puts forward an explicit multiparameter optimal robust control law to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop path tracking dynamical system. Then, the tracking performance as well as the control cost is quantified as cost functions using fuzzy set theories. Finally, an optimization problem is established in the content of cooperative game to seek the optimal values for the tunable parameters. Simulations are conducted using CarSim and Simulink under double lane change and serpentine driving conditions. The results show that the proposed robust optimal control exhibits superior tracking performance.

Design and Optimization of Robust Path Tracking Control for Autonomous Vehicles With Fuzzy Uncertainty

Uncertainty is a major concern in vehicle path tracking control design. The coefficients of the uncertainty bound are unknown. They are assumed to lie within prescribed fuzzy sets. First, based on the path tracking kinematic model, this paper innovatively formulates the vehicle path tracking task as a constraint-following problem. Second, we put forward a deterministic adaptive robust control law with a tunable parameter to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop system. Third, an optimal scheme for the tunable parameter is proposed based on the fuzzy uncertainty. The resulting optimal robust control (ORC) minimizes a comprehensive fuzzy performance index that involves the fuzzy system performance and the control cost. The results of the CarSim-Simulink co-simulation and the Hardware-in-loop (HIL) experiment together show that the proposed optimal robust control exhibits a superior path tracking performance.