Iterative learning control for path tracking of service robot in perspective dynamic system with uncertainties

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...

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Bibliographic Details
Published in:International journal of advanced robotic systems 2020-11, Vol.17 (6)
Main Authors: Yugang, Wang, Fengyu, Zhou, Yang, Zhao, Ming, Li, Lei, Yin
Format: Article
Language:English
Subjects:
Algorithms
Dynamical systems
Machine learning
Path tracking
Robot dynamics
Robots
Service robots
Tracking control
Uncertainty
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Summary: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.
ISSN:1729-8806
1729-8814
DOI:10.1177/1729881420968528