Path Tracking Control of Autonomous Vehicle on Curved Road Considering Multi-Source Uncertainty

2021 ◽  
Author(s):  
Wenbin Chen ◽  
Shuen Zhao ◽  
Liang Zhang ◽  
Yandong Wang
Keyword(s):  
Tracking Control ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Curved Road

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.

Dynamic curvature path tracking control for autonomous vehicle: Experimental results

2014 ◽  
Author(s):  
Muhammad Aizzat Zakaria ◽  
Hairi Zamzuri ◽  
Rosbi Mamat ◽  
Saiful Amri Mazlan ◽  
Mohd Azizi Abd Rahman ◽  
...  
Keyword(s):  
Tracking Control ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Experimental Results

scholarly journals Study of a Path Tracking Control for Low Speed Autonomous Vehicle Based on LIDAR Information

2011 ◽  
Vol 77 (773) ◽  
pp. 114-123 ◽  
Author(s):  
Takanori YOSHIZAWA ◽  
Pongsathorn RAKSINCHAROENSAK ◽  
Masao NAGAI
Keyword(s):  
Tracking Control ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Low Speed

Autonomous vehicle path tracking control considering the stability under lane change

2021 ◽  
pp. 095965182199135
Author(s):  
Xiaolong Chen ◽  
Bing Zhou ◽  
Xiaojian Wu
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Linear Time ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Steering Angle ◽  
Adhesion Coefficient ◽  
The Road ◽  
Vehicle Path

Considering that when a vehicle travels on a low friction coefficient road with high speed, the path tracking ability declines. To keep the performance of path tracking and improve the stabilization under that situation, this article presents approaches to estimate the parameters and control the vehicle. First, the key states of the vehicle and the road adhesion coefficient are estimated by the unscented Kalman filter. This is followed by applying the linear time-varying model-based predictive controller to achieve path tracking control, and the initial tire steering angle control rate is obtained. Finally, the steering angle compensation controller is simultaneously designed by a simple receding horizon corrector algorithm to improve vehicle stability when the path is tracked on a low-adhesion coefficient or at high speed. The performance of the proposed approach is evaluated by software CarSim and MATLAB/Simulink. Simulation results show that an improvement in the performance of path tracking and stabilization can be achieved by the integrated controller under the variable road adhesion coefficient condition and high speed with 110 km/h.

scholarly journals Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 109120-109133 ◽  
Author(s):  
Kibeom Lee ◽  
Seungmin Jeon ◽  
Heegwon Kim ◽  
Dongsuk Kum
Keyword(s):  
Tracking Control ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Path Tracking ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Full State ◽  
Lqg Control

Research on autonomous vehicle path tracking control considering roll stability

2020 ◽  
Vol 235 (1) ◽  
pp. 199-210
Author(s):  
Fen Lin ◽  
Shaobo Wang ◽  
Youqun Zhao ◽  
Yizhang Cai
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Tracking Control ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Vehicle Path ◽  
Target Path

For autonomous vehicle path tracking control, the general path tracking controllers usually only consider vehicle dynamics’ constraints, without taking vehicle stability evaluation index into account. In this paper, a linear three-degree-of-freedom vehicle dynamics model is used as a predictive model. A comprehensive control method combining Model Predictive Control and Fuzzy proportional–integral–derivative control is proposed. Model Predictive Control is used to control the vehicle yaw stability and track the target path by considering the front wheel angle, sideslip angle, tire slip angles, and yaw rate during the path tracking. Fuzzy proportional–integral–derivative algorithm is adopted to maintain the vehicle roll stability by controlling the braking force of each tire. Co-simulation with CarSim and MATLAB/Simulink shows the designed controller has good tracking performance. The controller is smooth and effective and ensures handling stability in tracking the target path.

scholarly journals Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4245
Author(s):  
Keke Geng ◽  
Nikolai Alexandrovich Chulin ◽  
Ziwei Wang
Keyword(s):  
Fault Detection ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Fault Detection And Isolation ◽  
Model Predictive Control Algorithm

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

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