Multilevel approximate model predictive control and its application to autonomous vehicle active steering

2013 ◽  
Author(s):  
Seung-Hi Lee ◽  
Chung Choo Chung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Approximate Model ◽  
Active Steering

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

2019 ◽  
Vol 233 (10) ◽  
pp. 1422-1437
Author(s):  
Keji Chen ◽  
Xiaofei Pei ◽  
Daoyuan Sun ◽  
Zhenfu Chen ◽  
Xuexun Guo ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Steering Control ◽  
Safe Driving ◽  
Active Steering ◽  
Active Steering Control

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

Contract-based Hierarchical Model Predictive Control and Planning for Autonomous Vehicle

2020 ◽  
Vol 53 (2) ◽  
pp. 15758-15764
Author(s):  
Mohamed Ibrahim ◽  
Markus Kögel ◽  
Christian Kallies ◽  
Rolf Findeisen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hierarchical Model ◽  
Autonomous Vehicle

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

The Restricted Newton Method for Fast Nonlinear Model Predictive Control

2019 ◽  
Author(s):  
Anson Maitland ◽  
Chi Jin ◽  
John McPhee
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Newton’S Method ◽  
Newton's Method ◽  
Gradient Descent ◽  
Autonomous Vehicle ◽  
Optimization Method ◽  
Selection Strategies ◽  
Two Parameters ◽  
Two Parameter

Abstract We introduce the Restricted Newton’s Method (RNM), a basic optimization method, to accelerate model predictive control turnaround times. RNM is a hybrid of Newton’s method (NM) and gradient descent (GD) that can be used as a building block in nonlinear programming. The two parameters of RNM are the subspace on which we restrict the Newton steps and the maximal size of the GD step. We present a convergence analysis of RNM and demonstrate how these parameters can be selected for MPC applications using simple machine learning methods. This leads to two parameter selection strategies with different convergence behaviour. Lastly, we demonstrate the utility of RNM on a sample autonomous vehicle problem with promising results.

scholarly journals Hierarchical Model Predictive Control for Autonomous Vehicle Area Coverage

2019 ◽  
Vol 52 (12) ◽  
pp. 79-84
Author(s):  
M. Ibrahim ◽  
J. Matschek ◽  
B. Morabito ◽  
R. Findeisen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hierarchical Model ◽  
Autonomous Vehicle ◽  
Area Coverage
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