An intelligent predictive control approach to path tracking problem of autonomous mobile robot

Author(s):  
Xinxin Yang ◽  
Kezhong He ◽  
Muhe Guo ◽  
Bo Zhang
2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881263 ◽  
Author(s):  
Paul Quillen ◽  
Kamesh Subbarao

This article puts forth a framework using model-based techniques for path planning and guidance for an autonomous mobile robot in a constrained environment. The path plan is synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort.” Then, an improved nonlinear model predictive control approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the planned path leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Finally, the performance of the resulting framework using both nonlinear guidance techniques is verified in simulation where the environment is constrained by the presence of static obstacles.


2000 ◽  
Vol 33 (4) ◽  
pp. 559-564
Author(s):  
Julio E. Normey-Rico ◽  
Ismael Alcalá ◽  
Juan Gómez-Ortega ◽  
Eduardo F. Camacho

2004 ◽  
Vol 77 (16) ◽  
pp. 1438-1445 ◽  
Author(s):  
B. Kim ◽  
D. Necsulescu * ◽  
J. Sasiadek

2014 ◽  
Vol 47 (3) ◽  
pp. 4849-4854 ◽  
Author(s):  
Mitra Bahadorian ◽  
Ray Eaton ◽  
Tim Hesketh ◽  
Borislav Savkovic

Author(s):  
Qifan Tan ◽  
Lingling Shi ◽  
Jay Katupitiya

This paper presents a novel approach for a force-controlled over-actuated two-wheel-steer four-wheel-drive vehicle to achieve accurate path tracking through control allocation. The control algorithm is developed by combining embedded integrator model predictive control and sequential quadratic programming–based control allocation. Based on the offset model presented in this paper, the model predictive control can generate the virtual accelerations. Using these virtual accelerations in control allocation, the actual control inputs are obtained. The proposed method is then used in a simulation to demonstrate its successful implementation in path tracking.


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