scholarly journals A Model Predictive Controller With Switched Tracking Error for Autonomous Vehicle Path Tracking

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 53103-53114 ◽  
Author(s):  
Chuanyang Sun ◽  
Xin Zhang ◽  
Quan Zhou ◽  
Ying Tian
Keyword(s):  
Autonomous Vehicle ◽  
Tracking Error ◽  
Path Tracking ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Vehicle Path

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

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 Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 128233-128249
Author(s):  
Mohammad Rokonuzzaman ◽  
Navid Mohajer ◽  
Saeid Nahavandi ◽  
Shady Mohamed
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Vehicle Path

Effect of Short-Term Weather Predictions on Model Predictive Trajectory Tracking Performance of Unmanned Surface Vessels

2020 ◽  
Author(s):  
Benjamin Armentor ◽  
Joseph Stevens ◽  
Nathan Madsen ◽  
Andrew Durand ◽  
Joshua Vaughan
Keyword(s):  
Trajectory Tracking ◽  
Wind Waves ◽  
Tracking Error ◽  
Tracking Performance ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Surface Vessels ◽  
Waves And Currents ◽  
Predictive Controller ◽  
Disturbance Model

Abstract For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.

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