2017
DOI: 10.1002/asjc.1602
|View full text |Cite
|
Sign up to set email alerts
|

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L2‐Gain Approach

Abstract: In this study, a new backstepping control scheme is proposed to deal with the high accuracy flexible joint servo system's position control. Based on the introduction of non‐consecutive friction, the cascade dynamics equations of flexible joint are established. The macroscopic controller is designed using a backstepping design technique to suppress the flexibility and external disturbance based on the L2 property. To identify the non‐consecutive function, the wavelet neural networks (WNN) are utilized in the mi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
19
0

Year Published

2018
2018
2019
2019

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 16 publications
(19 citation statements)
references
References 39 publications
0
19
0
Order By: Relevance
“…Nowadays, artificial neural networks are attracting growing interest. They are applied in various fields without requiring detailed information on the system operation and they establish the relationship between input and output variables by analyzing the training database . They provide a soft computing algorithm for complex nonlinear problems and can avoid the fail of the conventional techniques to track variations under fast varying conditions .…”
Section: Direct Grid Connected Photovoltaic Systemmentioning
confidence: 99%
“…Nowadays, artificial neural networks are attracting growing interest. They are applied in various fields without requiring detailed information on the system operation and they establish the relationship between input and output variables by analyzing the training database . They provide a soft computing algorithm for complex nonlinear problems and can avoid the fail of the conventional techniques to track variations under fast varying conditions .…”
Section: Direct Grid Connected Photovoltaic Systemmentioning
confidence: 99%
“…Recently, computational intelligence related approaches have been widely employed for control, such as fuzzy systems [16][17][18][19][20][21], neural networks [22][23][24][25], neurofuzzy systems [26,27], or radial basis function neural networks (in short, RBFN or RBFNN) [28][29][30]. Some researchers have tried to employ these adaptive networks in sliding mode controllers.…”
Section: Introductionmentioning
confidence: 99%
“…Back-stepping is a powerful tool to design controllers for nonlinear systems with certain specific structures [1][2][3][4][5]. For example, back-stepping design has been widely applied in actuator and manipulator control [5][6][7], and in the field of hypersonic flight control [8][9][10].…”
Section: Introductionmentioning
confidence: 99%