Hierarchical control for path tracking of autonomous vehicles

2012 ◽  
Author(s):  
Changfang Chen ◽  
Yingmin Jia ◽  
Junping Du ◽  
Jun Zhang
Keyword(s):  
Autonomous Vehicles ◽  
Hierarchical Control ◽  
Path Tracking

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

2021 ◽  
pp. 095440702110205
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Coordinated Control ◽  
Steering System ◽  
Front Wheel ◽  
Path Tracking ◽  
Hierarchical Architecture ◽  
Automatic Steering ◽  
The Stability ◽  
Control Layer

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

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.

An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle

2019 ◽  
Vol 233 (12) ◽  
pp. 3246-3262 ◽  
Author(s):  
Bing Zhang ◽  
Changfu Zong ◽  
Guoying Chen ◽  
Guiyuan Li
Keyword(s):  
Electric Vehicle ◽  
Model Prediction ◽  
Hierarchical Control ◽  
Path Tracking ◽  
Vehicle Dynamic ◽  
Independent Control ◽  
Prediction Horizon ◽  
Adaptive Prediction ◽  
Model Prediction Control ◽  
Prediction Control

An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switching prediction horizon and for robustness during path tracking, the numerical problem inherent in the adaptive-prediction-horizon model prediction control is analysed and solved by introducing exponentially decreasing weight. Subsequently, the desired motion for path tracking with the four-wheel independent control electric vehicle is realized with a hierarchical control structure. Simulation results finally illustrate the effectiveness of the proposed method.

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