Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot

Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot

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-...

Full description

Bibliographic Details
Main Authors: Jian Dong, Bin He, Chenghong Zhang, Gang Li
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2019/5705126
id doaj-416d04c839274187a695f4e8a92260b1
record_format Article
spelling doaj-416d04c839274187a695f4e8a92260b12020-11-25T00:42:01ZengHindawi-WileyComplexity1076-27871099-05262019-01-01201910.1155/2019/57051265705126Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile RobotJian Dong0Bin He1Chenghong Zhang2Gang Li3College of Electronics and Information Engineering, Tongji University, Shanghai, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai, ChinaA 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.http://dx.doi.org/10.1155/2019/5705126
collection DOAJ
language English
format Article
sources DOAJ
author Jian Dong
Bin He
Chenghong Zhang
Gang Li
spellingShingle Jian Dong
Bin He
Chenghong Zhang
Gang Li
Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot
Complexity
author_facet Jian Dong
Bin He
Chenghong Zhang
Gang Li
author_sort Jian Dong
title Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot
title_short Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot
title_full Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot
title_fullStr Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot
title_full_unstemmed Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot
title_sort open-closed-loop pd iterative learning control with a variable forgetting factor for a two-wheeled self-balancing mobile robot
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2019-01-01
description 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.
url http://dx.doi.org/10.1155/2019/5705126
work_keys_str_mv AT jiandong openclosedlooppditerativelearningcontrolwithavariableforgettingfactorforatwowheeledselfbalancingmobilerobot
AT binhe openclosedlooppditerativelearningcontrolwithavariableforgettingfactorforatwowheeledselfbalancingmobilerobot
AT chenghongzhang openclosedlooppditerativelearningcontrolwithavariableforgettingfactorforatwowheeledselfbalancingmobilerobot
AT gangli openclosedlooppditerativelearningcontrolwithavariableforgettingfactorforatwowheeledselfbalancingmobilerobot
_version_ 1725284281289277440

Similar Items