scholarly journals Obstacle Avoidance of Semi-Trailers Based on Nonlinear Model Predictive Control

2019 ◽  
Vol 10 (4) ◽  
pp. 72
Author(s):  
Guoxing Bai ◽  
Chen Liang ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Predictive Control ◽  
Minimum Distance ◽  
Safety Margin ◽  
Autonomous Driving ◽  
Nonlinear Model Predictive Control ◽  
Middle Line ◽  
Road Vehicles ◽  
Simulation Results ◽  
Core Part

Obstacle avoidance is a core part of the autonomous driving of off-road vehicles, such as semi-trailers. Due to the long length of semi-trailers, the traditional obstacle avoidance controller based on the circumcircle model can ensure that there is no collision between the semi-trailer and the obstacle, but it also greatly reduces the passable area. To solve this problem, we propose a new obstacle avoidance model. In this model, the distance between the obstacle and the middle line of semi-trailers is used as the indicator of obstacle avoidance. Based on this model, we design a new obstacle avoidance controller for semi-trailers. The simulation results show that the proposed controller can ensure that no collision occurs between the semi-trailer and the obstacle. The minimum distance between the obstacle center and the semi-trailer body trajectory is greater than the sum of the obstacle radius and the safety margin. Compared with the traditional obstacle avoidance controller based on the circumcircle model, the proposed controller greatly reduces the error between the semi-trailer and the reference path during obstacle avoidance.

Chaos Control of Atomic Force Microscope System Using Nonlinear Model Predictive Control

2016 ◽  
Vol 33 (3) ◽  
pp. 405-415 ◽  
Author(s):  
J. Keighobadi ◽  
J. Faraji ◽  
S. Rafatnia
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chaos Control ◽  
Control Method ◽  
Nonlinear Model Predictive Control ◽  
Stable Range ◽  
Operational Conditions ◽  
Simulation Results ◽  
Micro Cantilever

AbstractOwing to robust and optimal specification, model predictive control method has received wide attentions over recent years. Since in certain operational conditions, an Atomic/scanning Force Microscope (AFM) shows chaos behavior, the chaos feedback control of the AFM system is considered. According to the nonlinear model of forces interacting between the tip of micro cantilever and the substrate of AFM; the nonlinear control methods are proposed. In the paper, the chaos control of a micro cantilever AFM based on the nonlinear model predictive control (NMPC) technique is presented. Through software simulation results, the effectiveness of the designed NMPC of the AFM is assessed. The simulation results together with analytical stability proofs indicate that the proposed method is effective in keeping the system in a stable range.

scholarly journals Model Predictive Control for Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2593
Author(s):  
Trieu Minh Vu ◽  
Reza Moezzi ◽  
Jindrich Cyrus ◽  
Jaroslav Hlava
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Driving ◽  
Nonlinear Model Predictive Control ◽  
System Stability ◽  
Vehicle Speed ◽  
Model Predictive Controller ◽  
Predictive Controller

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

scholarly journals Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion

2019 ◽  
Vol 52 (8) ◽  
pp. 182-187 ◽  
Author(s):  
M. Rick ◽  
J. Clemens ◽  
L. Sommer ◽  
A. Folkers ◽  
K. Schill ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Sensor Fusion ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Driving ◽  
Nonlinear Model Predictive Control
Sign in / Sign up

Export Citation Format

Share Document