scholarly journals Path Tracking Control of Autonomous Vehicle Based on Nonlinear Tire Model

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 242
Author(s):  
Fen Lin ◽  
Minghong Sun ◽  
Jian Wu ◽  
Chengliang Qian
Keyword(s):  
Autonomous Vehicle ◽  
Path Tracking ◽  
Tire Model ◽  
Tracking Accuracy ◽  
Composite Function ◽  
Tire Force ◽  
Active Steering ◽  
Relation Model ◽  
Predictive Controller ◽  
Tire Forces

The tire forces of vehicles will fall into the non-linear region under extreme handling conditions, which cause poor path tracking performance. In this paper, a model predictive controller based on a nonlinear tire model is designed. The tire forces are characterized with nonlinear composite functions of the magic formula instead of a simple linear relation model. Taylor expansion is used to linearize the controller, the first-order difference quotient method is used for discretization, and the partial derivative of the composite function is used for matrix transformation. Constant velocity and variable velocity conditions are selected to compare the designed controller with the conventional controller in Carsim/Simulink. The results show that when the tire forces fall in the nonlinear region, two controllers have good stability, and the tracking accuracy of the controller designed in this paper is slightly better. However, after the tire forces become nonlinear, the controller with linear tire force becomes worse, the tracking accuracy is far worse than the controller with the nonlinear tire model, and the vehicle stability is also degraded. In addition, an active steering test platform based on LabVIEW-RT is established, and hardware-in-the-loop tests are carried out. The effectiveness of the designed controller is verified.

Switched model predictive controller for path tracking of autonomous vehicle considering rollover stability

2021 ◽  
pp. 1-20
Author(s):  
Ying Tian ◽  
Qiangqiang Yao ◽  
Chengqiang Wang ◽  
Shengyuan Wang ◽  
Jiaqi Liu ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Path Tracking ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Rollover Stability

scholarly journals A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

2019 ◽  
Vol 9 (22) ◽  
pp. 4739 ◽  
Author(s):  
Yao ◽  
Tian
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Tracking Error ◽  
Path Tracking ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Prediction Horizon ◽  
Tracking Model ◽  
Vehicle Path

Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

A Predictive Controller for Autonomous Vehicle Path Tracking

2009 ◽  
Vol 10 (1) ◽  
pp. 92-102 ◽  
Author(s):  
G.V. Raffo ◽  
G.K. Gomes ◽  
J.E. Normey-Rico ◽  
C.R. Kelber ◽  
L.B. Becker
Keyword(s):  
Autonomous Vehicle ◽  
Path Tracking ◽  
Predictive Controller ◽  
Vehicle Path

A Modified Dugoff Tire Model for Combined-slip Forces

2010 ◽  
Vol 38 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri
Keyword(s):  
Vehicle Dynamics ◽  
Tire Model ◽  
Magic Formula ◽  
Model Based ◽  
Proposed Model ◽  
Road Friction ◽  
On Line ◽  
Double Lane Change ◽  
Tire Forces ◽  
Tire Models

Abstract Easy-to-use tire models for vehicle dynamics have been persistently studied for such applications as control design and model-based on-line estimation. This paper proposes a modified combined-slip tire model based on Dugoff tire. The proposed model takes emphasis on less time consumption for calculation and uses a minimum set of parameters to express tire forces. Modification of Dugoff tire model is made on two aspects: one is taking different tire/road friction coefficients for different magnitudes of slip and the other is employing the concept of friction ellipse. The proposed model is evaluated by comparison with the LuGre tire model. Although there are some discrepancies between the two models, the proposed combined-slip model is generally acceptable due to its simplicity and easiness to use. Extracting parameters from the coefficients of a Magic Formula tire model based on measured tire data, the proposed model is further evaluated by conducting a double lane change maneuver, and simulation results show that the trajectory using the proposed tire model is closer to that using the Magic Formula tire model than Dugoff tire model.

Lateral Tire Forces on Wet Roads

1977 ◽  
Vol 5 (2) ◽  
pp. 75-82 ◽  
Author(s):  
A. Schallamach
Keyword(s):  
Normal Load ◽  
The Self ◽  
Experimental Results ◽  
Slip Angle ◽  
Tire Model ◽  
Side Force ◽  
Tire Forces

Abstract Expressions are derived for side force and self-aligning torque of a simple tire model on wet roads with velocity-dependent friction. The results agree qualitatively with experimental results at moderate speeds. In particular, the theory correctly predicts that the self-aligning torque can become negative under easily realizable circumstances. The slip angle at which the torque reverses sign should increase with the normal load.

scholarly journals Research on Model Predictive Control for Automobile Active Tilt Based on Active Suspension

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 671
Author(s):  
Jialing Yao ◽  
Meng Wang ◽  
Zhihong Li ◽  
Yunyi Jia
Keyword(s):  
Load Transfer ◽  
Ride Comfort ◽  
Active Suspension ◽  
Path Tracking ◽  
Roll Angle ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Model Predictive Controller ◽  
Advantages And Disadvantages ◽  
Predictive Controller

To improve the handling stability of automobiles and reduce the odds of rollover, active or semi-active suspension systems are usually used to control the roll of a vehicle. However, these kinds of control systems often take a zero-roll-angle as the control target and have a limited effect on improving the performance of the vehicle when turning. Tilt control, which actively controls the vehicle to tilt inward during a curve, greatly benefits the comprehensive performance of a vehicle when it is cornering. After analyzing the advantages and disadvantages of the tilt control strategies for narrow commuter vehicles by combining the structure and dynamic characteristics of automobiles, a direct tilt control (DTC) strategy was determined to be more suitable for automobiles. A model predictive controller for the DTC strategy was designed based on an active suspension. This allowed the reverse tilt to cause the moment generated by gravity to offset that generated by the centrifugal force, thereby significantly improving the handling stability, ride comfort, vehicle speed, and rollover prevention. The model predictive controller simultaneously tracked the desired tilt angle and yaw rate, achieving path tracking while improving the anti-rollover capability of the vehicle. Simulations of step-steering input and double-lane change maneuvers were performed. The results showed that, compared with traditional zero-roll-angle control, the proposed tilt control greatly reduced the occupant’s perceived lateral acceleration and the lateral load transfer ratio when the vehicle turned and exhibited a good path-tracking performance.

Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications

2021 ◽  
pp. 1-1
Author(s):  
Woongsun Jeon ◽  
Ankush Chakrabarty ◽  
Ali Zemouche ◽  
Rajesh Rajamani
Keyword(s):  
State Estimation ◽  
Autonomous Vehicle ◽  
Tire Model ◽  
Model Learning
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