Spatial path tracking using iterative learning control

Author(s):  
Yiyang Chen ◽  
Bing Chu ◽  
Christopher T. Freeman
Author(s):  
Shuhua Su ◽  
Gang Chen

In order to achieve stable steering and path tracking, a lateral robust iterative learning control method for unmanned driving robot vehicle is proposed. Combining the nonlinear tire dynamic model with the vehicle dynamic model, the nonlinear vehicle dynamic model is constructed. The structure of steering manipulator of unmanned driving robot vehicle is analyzed, and the kinematics model and dynamics model of steering manipulator of unmanned driving robot vehicle are established. The structure of vehicle steering system is analyzed, and the dynamic model of vehicle steering system is established. Vehicle steering angle model is established by taking vehicle path tracking error and vehicle yaw angle error as input. Combining with the typical iterative learning control law, the robust term is added to the control law, and a robust iterative learning controller for steering manipulator system of unmanned driving robot vehicle is designed. The proposed controller’s stability and astringency are proved. The effectiveness of the proposed method is verified by comparing it with other control methods and human driver simulation tests.


2005 ◽  
Vol 22 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Min K. Kang ◽  
Jin S. Lee ◽  
Kyoung L. Han

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096852
Author(s):  
Wang Yugang ◽  
Zhou Fengyu ◽  
Zhao Yang ◽  
Li Ming ◽  
Yin Lei

A novel iterative learning control (ILC) for perspective dynamic system (PDS) is designed and illustrated in detail in this article to overcome the uncertainties in path tracking of mobile service robots. PDS, which transmits the motion information of mobile service robots to image planes (such as a camera), provides a good control theoretical framework to estimate the robot motion problem. The proposed ILC algorithm is applied in accordance with the observed motion information to increase the robustness of the system in path tracking. The convergence of the presented learning algorithm is derived as the number of iterations tends to infinity under a specified condition. Simulation results show that the designed framework performs efficiently and satisfies the requirements of trajectory precision for path tracking of mobile service robots.


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