Boundary Tracking Control for Autonomous Vehicles with Rigidly Mounted Range Sensors

Curved path tracking control is one of the most important functions of autonomous vehicles. First, small turning radius circular bends considering bend quadrant and travel direction restrictions are planned by polar coordinate equations. Second, an estimator of a vehicle state parameter and road adhesion coefficient based on an extended Kalman filter is designed. To improve the convenience and accuracy of the estimator, the combined slip theory, trigonometric function group fitting, and cubic spline interpolation are used to estimate the longitudinal and lateral forces of the tire model (215/55 R17). Third, to minimize the lateral displacement and yaw angle tracking errors of a four-wheel steering (4WS) vehicle, the front-wheel steering angle of the 4WS vehicle is corrected by a model predictive control (MPC) feed-back controller. Finally, CarSim® simulation results show that the 4WS autonomous vehicle based on the MPC feed-back controller can not only significantly improve the curved path tracking performance but also effectively reduce the probability of drifting or rushing out of the runway at high speeds and on low-adhesion roads.

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MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2019.2907696 ◽

2019 ◽

Vol 68 (6) ◽

pp. 5246-5259 ◽

Cited By ~ 31

Author(s):

Chuan Hu ◽

Zhenfeng Wang ◽

Hamid Taghavifar ◽

Jing Na ◽

Yechen Qin ◽

...

Keyword(s):

Autonomous Vehicles ◽

Tracking Control ◽

Input Saturation ◽

Path Tracking

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Path-tracking control for autonomous vehicles using double-hidden-layer output feedback neural network fast nonsingular terminal sliding mode

Neural Computing and Applications ◽

10.1007/s00521-021-06101-8 ◽

2021 ◽

Author(s):

Zhe Sun ◽

Jiayang Zou ◽

Defeng He ◽

Wei Zhu

Keyword(s):

Neural Network ◽

Output Feedback ◽

Autonomous Vehicles ◽

Tracking Control ◽

Sliding Mode ◽

Path Tracking ◽

Terminal Sliding Mode ◽

Nonsingular Terminal Sliding Mode ◽

Feedback Neural Network ◽

Hidden Layer

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Robust Path Tracking Control for Autonomous Vehicle Based on a Novel Fault Tolerant Adaptive Model Predictive Control Algorithm

Applied Sciences ◽

10.3390/app10186249 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6249

Author(s):

Keke Geng ◽

Shuaipeng Liu

Keyword(s):

Predictive Control ◽

Autonomous Vehicles ◽

Tracking Control ◽

Control Algorithm ◽

Fault Tolerant ◽

Path Tracking ◽

Adaptive Model ◽

Chi Square ◽

Model Predictive Control Algorithm ◽

Adaptive Model Predictive Control

Autonomous vehicles are expected to completely change the development model of the transportation industry and bring great convenience to our lives. Autonomous vehicles need to constantly obtain the motion status information with on-board sensors in order to formulate reasonable motion control strategies. Therefore, abnormal sensor readings or vehicle sensor failures can cause devastating consequences and can lead to fatal vehicle accidents. Hence, research on the fault tolerant control method is critical for autonomous vehicles. In this paper, we develop a robust fault tolerant path tracking control algorithm through combining the adaptive model predictive control algorithm for lateral path tracking control, improved weight assignment method for multi-sensor data fusion and fault isolation, and novel federal Kalman filtering approach with two states chi-square detector and residual chi-square detector for detection and identification of sensor fault in autonomous vehicles. Our numerical simulation and experiment demonstrate that the developed approach can detect fault signals and identify their sources with high accuracy and sensitivity. In the double line change path tracking control experiment, when the sensors failure occurs, the proposed method shows better robustness and effectiveness than the traditional methods. It is foreseeable that this research will contribute to the development of safer and more intelligent autonomous driving system, which in turn will promote the industrial development of intelligent transportation system.

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