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An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint
- Source :
- Mathematical Problems in Engineering, Vol 2020 (2020)
- Publication Year :
- 2020
- Publisher :
- Hindawi Limited, 2020.
-
Abstract
- Friction is a nonlinear effect that occurs in all mechanical systems which may cause limit cycles, tracking errors, and other undesirable effects. Traditional static friction models cannot characterize all the friction situations. In recent years, neural network (NN) technique has been widely used to approximate the nonlinear function. In this paper, a new method which combines radical basis function neural network (BRFNN) with beetle antennae search (BAS) algorithm for modeling friction in a robotic joint is proposed. Velocity, load, and temperature are considered as the three factors that influence the static friction. It is shown that the proposed BAS-RBFNN possesses better performance in terms of faster convergence rate and higher accuracy.
- Subjects :
- 0209 industrial biotechnology
Article Subject
Artificial neural network
Computer science
General Mathematics
General Engineering
Robot manipulator
Basis function
02 engineering and technology
Engineering (General). Civil engineering (General)
Mechanical system
Nonlinear system
020901 industrial engineering & automation
Rate of convergence
Control theory
Search algorithm
0202 electrical engineering, electronic engineering, information engineering
QA1-939
020201 artificial intelligence & image processing
Limit (mathematics)
TA1-2040
Joint (geology)
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15635147
- Volume :
- 2020
- Database :
- OpenAIRE
- Journal :
- Mathematical Problems in Engineering
- Accession number :
- edsair.doi.dedup.....3b768039484b92aab84c3d7a2b29918f