Iterative Learning Control of a Robotic Arm Experiment Platform with Input Constraint

This paper addresses the vibration control and the trajectory tracking control of a robotic arm system with input constraint. A hyperbolic tangent function and a saturation function are adopted to tackle the input constraint. By defining a composite energy function, a dual-loop iterative learning co...

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Bibliographic Details
Published inIEEE transactions on industrial electronics (1982) Vol. 65; no. 1; pp. 664 - 672
Main Authors Meng, Tingting, He, Wei
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Asymptotic properties
Boundary control
Convergence
Elastic properties
Feedback control
Hyperbolic functions
input constraint
Iterative learning control
iterative learning control (ILC)
Laws
Learning
Manipulators
Mathematical model
Robot arms
Robot control
robotic arm system
Tracking control
Trajectory
Trajectory control
Vibration control
Vibrations
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Summary:This paper addresses the vibration control and the trajectory tracking control of a robotic arm system with input constraint. A hyperbolic tangent function and a saturation function are adopted to tackle the input constraint. By defining a composite energy function, a dual-loop iterative learning control (ILC) law is designed by integrating a restrained learning law and a saturated feedback law. For the closed-loop system, the angle displacements are asymptotically regulated to track a prescribed constant trajectory and the elastic displacements are asymptotically suppressed to zero along the iteration axis. Simulation and experimental results are provided to illustrate the effectiveness of the designed ILC law.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2719598