Paper Info
Title
Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot
Abstract
A novel iterative learning control (ILC) algorithm for a two-wheeled self-balancing mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties is presented to resolve the trajectory tracking problem in this research. A kinematics model and dynamic model of a two-wheeled self-balancing mobile robot are deduced in this paper, and the combination of an open-closed-loop PD-ILC law and a variable forgetting factor is presented. The open-closed-loop PD-ILC algorithm adopts current and past learning items to drive the state variables and input variables, and the output variables converge to the bounded scope of their desired values. In addition, introducing a variable forgetting factor can enhance the robustness and stability of ILC. Numerous simulation and experimental data demonstrate that the proposed control scheme has better feasibility and effectiveness than the traditional control algorithm.
Year
DOI
Venue
2019
10.1155/2019/5705126
COMPLEXITY
DocType
Volume
ISSN
Journal
2019.0
1076-2787
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Jian Dong1911.04
Bin He2176.47
Zhang Chenghong3227.07
Gang Li442179.69