Self-learning path-tracking control of autonomous vehicles using kernel-based approximate dynamic programming

2008 ◽  
Author(s):  
Xin Xu ◽  
Hongyu Zhang ◽  
Bin Dai ◽  
Han-gen He
Keyword(s):  
Dynamic Programming ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Approximate Dynamic Programming ◽  
Path Tracking ◽  
Learning Path ◽  
Self Learning

scholarly journals Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Runqiao Liu ◽  
Minxiang Wei ◽  
Nan Sang ◽  
Jianwei Wei
Keyword(s):  
Autonomous Vehicles ◽  
Tracking Control ◽  
Spline Interpolation ◽  
Lateral Displacement ◽  
Autonomous Vehicle ◽  
State Parameter ◽  
Front Wheel ◽  
Path Tracking ◽  
Adhesion Coefficient ◽  
Polar Coordinate

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.

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

2019 ◽  
Vol 68 (6) ◽  
pp. 5246-5259 ◽  
Author(s):  
Chuan Hu ◽  
Zhenfeng Wang ◽  
Hamid Taghavifar ◽  
Jing Na ◽  
Yechen Qin ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Tracking Control ◽  
Input Saturation ◽  
Path Tracking

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

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.

Approximate Dynamic Programming in the Sensor-Based Navigation of the Wheeled Mobile Robot

2015 ◽  
Vol 220-221 ◽  
pp. 60-66 ◽  
Author(s):  
Zenon Hendzel ◽  
Marcin Szuster
Keyword(s):  
Dynamic Programming ◽  
Control System ◽  
Path Planning ◽  
Mobile Robot ◽  
Tracking Control ◽  
Approximate Dynamic Programming ◽  
Wheeled Mobile Robot ◽  
Discrete Control ◽  
Heuristic Dynamic Programming ◽  
Control Layer

The article presents a new approach to the sensor-based navigation of wheeled mobile robot Pioneer 2-DX in the unknown 2-D environment with static obstacles. The navigation task has been developed using a discrete hierarchical control system with a path planning layer and a tracking control layer designed using approximate dynamic programming algorithms. The navigator realises a behavioural control approach to the path planning process using the adaptive coordination of two simple behaviours: “goal-seeking” and “obstacle avoiding”. The main part of the navigator is the Action-Dependant Heuristic Dynamic Programming structure realised in a form of the actor and critic neural networks. To avoid the time consuming trial and error learning, additional proportional controllers generating signals that prompt the direction of the sub-optimal control law seeking process at the beginning of the NNs adaptation process are arranged in the navigator. The tracking control layer is composed of a PD controller, the Dual Heuristic Dynamic Programming algorithm and a supervisory term. It generates control signal for DC motors of the robot. The performance of the proposed discrete control system was verified by a series of experiments conducted using wheeled mobile robot Pioneer 2-DX equipped with one laser and eight ultrasonic range finders that provide object detection.

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