Paper Info
Title
Iterative learning control of robot based on artificial bee colony algorithm.
Abstract
For the repetitive motion control, inaccurate model, and other issues of industrial robots, this article presents a novel control method that the proportion differentiation-type iterative learning parameters are self-tuning based on artificial bee colony algorithm. Considering the influence of the numerical value of iterative learning parameters on the control system, especially in the early iteration, the control effect is not satisfactory. Thus, the artificial bee colony algorithm is introduced in this article. Using bee colony as search unit, the parameters in iterative learning are optimized through the exchange of information and the survival of fittest between them. And then the optimized results are returned to iterative learning control algorithm. Finally, the digital simulation of a two-degrees-of-freedom manipulator and the experimental verification of a cable-driven robot with its first two joints are carried out. The results show that the iterative learning control based on the artificial bee colony algorithm has faster convergence and better control effect than the iterative learning control with fixed parameters.
Year
DOI
Venue
2019
10.1177/0959651818824202
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
Keywords
DocType
Volume
Artificial bee colony algorithm,iterative learning control,robot,parameter optimization,system simulation
Journal
233.0
Issue
ISSN
Citations 
9
0959-6518
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wanqiang Xi100.68
Yaoyao Wang2219.30
Bai Chen32014.41
Hong-tao Wu42214.32