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 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.

scholarly journals Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

2019 ◽  
Vol 9 (7) ◽  
pp. 1372 ◽  
Author(s):  
Guoxing Bai ◽  
Li Liu ◽  
Yu Meng ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Longitudinal Velocity ◽  
Path Tracking ◽  
Nonlinear Model Predictive Control ◽  
Maximum Displacement ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Mining Vehicles

Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.

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

Model predictive controller–based spatiotemporal path tracking method for transhumeral prostheses

2019 ◽  
Vol 15 (3) ◽  
Author(s):  
Kanishka Madusanka Dannangoda Gamage ◽  
R.A. Ruwan C. Gopura ◽  
Y.W. Ranjith Amarasinghe ◽  
George K.I. Mann
Keyword(s):  
Path Tracking ◽  
Model Predictive Controller ◽  
Tracking Method ◽  
Predictive Controller ◽  
Path Tracking Method

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.

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