scholarly journals Tracking Control by the Newton-Raphson Flow: Applications to Autonomous Vehicles

2019 ◽  
Author(s):  
S. Shivam ◽  
I. Buckley ◽  
Y. Wardi ◽  
C. Seatzu ◽  
M. Egerstedt
Keyword(s):  
Autonomous Vehicles ◽  
Tracking Control ◽  
Newton Raphson

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.

Implementing Trajectory Tracking Control Algorithm for Autonomous Vehicles

2021 ◽  
Author(s):  
Xuting Duan ◽  
Qi Wang ◽  
Daxin Tian ◽  
Jianshan Zhou ◽  
Jian Wang ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Control Algorithm ◽  
Trajectory Tracking Control

Application Study of Newton-Raphson-Type Nonlinear MPC for Hydrostatic Transmissions Under Disturbance and System Uncertainty

2021 ◽  
Vol 1 (1) ◽  
pp. 21-29
Author(s):  
Ngoc Danh Dang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Unscented Kalman Filter ◽  
Industrial Applications ◽  
State Variables ◽  
Tracking Accuracy ◽  
Multiple Control ◽  
Newton Raphson ◽  
Hydrostatic Transmissions

Model predictive control is well established and has a huge practical relevance in many industrial applications, especially for chemical or thermal plants. This paper presents the design and the implementation of a nonlinear model predictive control aiming at an accurate tracking control of desired output trajectories under disturbances and uncertainties for a nonlinear hydrostatic transmission system with multiple control inputs, which represents a fast mechatronic system. The benefit of this solution is that it can be easily adapted to either velocity tracking control or torque tracking control -- which is not the case with alternative model-based approaches. The control design is based on a numerical optimization within a moving horizon using the Newton-Raphson method in combination with the optimization-over-some variables technique. The unmeasurable system state variables as well as the system disturbances are reconstructed by an unscented Kalman filter which is well suited for nonlineaer systems subject to process and measurement noise. The proposed control scheme is investigated by simulations and experimentally validated on a test rig at the Chair of Mechatronics, University of Rostock. The results indicates the robustness of the proposed control structure by a high tracking accuracy despite system disturbances and uncertainties.

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