Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities

Jamil, Muhammad U., Kongprawechnon, Waree and Raza, Muhammad Q. (2016) Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities. Aircraft Engineering and Aerospace Technology, 88 1: 137-150. doi:10.1108/AEAT-09-2014-0150


Author Jamil, Muhammad U.
Kongprawechnon, Waree
Raza, Muhammad Q.
Title Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities
Journal name Aircraft Engineering and Aerospace Technology
ISSN 0002-2667
1758-4213
Publication date 2016-01-04
Sub-type Article (original research)
DOI 10.1108/AEAT-09-2014-0150
Open Access Status Not Open Access
Volume 88
Issue 1
Start page 137
End page 150
Total pages 14
Place of publication Bingley, United Kingdom
Publisher Emerald Group
Collection year 2017
Language eng
Formatted abstract
Purpose: The purpose of the proposed research methodology is to control the trajectory tracking of EDRM and also to cancel out the effect of no-smooth nonlinearities, which affect the system performance badly.

Design/methodology/approach: Robust adaptive neural network (RANN)-based backstepping control design methodology is presented in this paper. The proposed design methodology improves the trajectory tracking and running mean error.

Findings: The running mean error results show that the convergence of the proposed RANN-based backstepping technique is very fast as compare to the conventional PD control and due to this proposed control technique, the EDRM follows its desired trajectory perfectly.

Practical implications: The EDRM trajectory tracking performance increases which leads to a better working position of EDRM.

Originality/value: The originality of this research article is 93 per cent.
Keyword Backstepping
Electrically driven robot manipulator (EDRM)
Linear in parameter (LIP)
Robust adaptive neural network (RANN)
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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