Model predictive control–based cooperative lane change strategy for improving traffic flow

Abstract A comparative study of model predictive control (MPC) schemes and robust A state feedback control (RSC) method for trajectory tracking, is proposed in this paper. Both MPC-based and RSC-based tracking controllers are designed on the basis of a 3-DOF vehicle model, including longitudinal, lateral and yaw motions. The main objective of this paper is to compare both controllers’ performance in tracking expected trajectory under different scenarios. Therefore, three cases, namely, verification test, double lane change test and curve test, were built in Carsim-Simulink joint platform. The simulation results indicate that MPC controller performed better in terms of accuracy and responding time under well driving conditions. However, in the test of double lane change manoeuvre where the road adhesion was set as 0.2, the maximum velocity RSC can execute was 14m/s, while that for MPC was 10m/s. In addition, in the curve test, the maximum velocity MPC can carry out was only 9m/s and that for RSC was 12m/s. In conclusion, RSC was robust and stable when the driving conditions was worse, while MPC was prone to be unstable.

Download Full-text

Model Predictive Control for Freeway Networks Based on Multi-Class Traffic Flow and Emission Models

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2016.2573306 ◽

2017 ◽

Vol 18 (2) ◽

pp. 306-320 ◽

Cited By ~ 16

Author(s):

Shuai Liu ◽

Hans Hellendoorn ◽

Bart De Schutter

Keyword(s):

Model Predictive Control ◽

Traffic Flow ◽

Predictive Control ◽

Emission Models ◽

Freeway Networks

Download Full-text

The Trajectory Planning of the Lane Change Assist Based on the Model Predictive Control with Multi-Objective

10.4271/2017-01-2004 ◽

2017 ◽

Cited By ~ 6

Author(s):

Yangyang Wang ◽

Rong Feng ◽

Ding Pan ◽

Zhiguang Liu ◽

Nan Wu ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Trajectory Planning ◽

Lane Change ◽

Multi Objective

Download Full-text

Conflict-free trajectory planning based on the model predictive control theory

Archives of Transport ◽

10.5604/08669546.1203205 ◽

2016 ◽

Vol 37 (1) ◽

pp. 77-85 ◽

Cited By ~ 1

Author(s):

Han Yun-xiang ◽

Huang Xiao-qiong

Keyword(s):

Model Predictive Control ◽

Traffic Flow ◽

Predictive Control ◽

Discrete Event ◽

Discrete Event System ◽

Air Traffic ◽

Service Rate ◽

Modeling Framework ◽

Event System ◽

Air Traffic Flow

Model Predictive Control (MPC) is a model-based control method based on a receding horizon approach and online optimization. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. This paper proposes a max-plus general modeling framework adapted to the robust optimal control of air traffic flow in the airspace. It is shown that the problem can be posed as the control of queues with safety separation-dependent service rate. We extend MPC to a class of discrete-event system that can be described by models that are linear in the max-plus algebra with noise or modeling errors. Regarding the single aircraft as a batch, the relationships between input variables, state variables and output variable are established. We discuss some key properties of the system model and indicate how these properties can be used to analyze the behavior of air traffic flow. The model predictive control design problems are defined for this type of discrete event system to help prepare the airspace for various robust regulation needs and we give some extensions of the air traffic max-plus linear systems.

Download Full-text