Active Front Steering LTV MPC for Varying Friction Conditions

2014 ◽  
Author(s):  
Nicola De Val ◽  
Andrea Fuso ◽  
Francesco Braghin ◽  
Edoardo Sabbioni
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Time ◽  
Path Following ◽  
Vehicle Speed ◽  
Optimal Parameters ◽  
Active Front Steering ◽  
Road Friction ◽  
Nonlinear Vehicle Model ◽  
Road Friction Coefficient

The developed Active Front Steering (AFS) Linear Time Variant (LTV) Model Predictive Control (MPC) is a linear model predictive control based on linearization of the nonlinear vehicle model. A sensitivity analysis of the parameters of the controller is carried out on a simple path following test. Once the optimal parameters are found, both in terms of trajectory following and real-time performances, the LTV-MPC is used for determining the requirements for the necessary sensors (in terms of minimum obstacle distance detection) as a function of the vehicle speed. Then, the same analysis is carried out considering wet road conditions (i.e. the tyre-road friction coefficient is different from that accounted for by the controller).

scholarly journals Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Fitri Yakub ◽  
Aminudin Abu ◽  
Shamsul Sarip ◽  
Yasuchika Mori
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Path Following ◽  
Change Scenario ◽  
Direct Yaw Moment Control ◽  
Path Following Control ◽  
Road Friction ◽  
Yaw Moment Control ◽  
Friction Surfaces

We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving

2020 ◽  
Author(s):  
Irfan Khan ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Autonomous Driving ◽  
Maximum Speed ◽  
Reference Trajectory ◽  
Vehicle Speed ◽  
The Road ◽  
Speed Regulation ◽  
Road Geometry

Abstract This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.

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