Backstepping control using adaptive neural network for industrial two link robot manipulator

This paper highlights, neural network (NN) based adaptive control using backstepping control technique is proposed for robot manipulator trajectory tracking. Firstly, the vector of current is considered as the control variable for robot manipulator mechanical subsystem by using the adaptive update a...

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Main Authors: Jamil, M.U., Noor, M.N., Raza, M.Q., Rizvi, S.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31703 /
Published: Institute of Electrical and Electronics Engineers Inc. 2014
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988259092&doi=10.1109%2fINMIC.2014.7097371&partnerID=40&md5=8f12c3a26df4a1cc7e8bab9ac7002276
http://eprints.utp.edu.my/31703/
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spelling utp-eprints.317032022-03-29T03:35:18Z Backstepping control using adaptive neural network for industrial two link robot manipulator Jamil, M.U. Noor, M.N. Raza, M.Q. Rizvi, S. This paper highlights, neural network (NN) based adaptive control using backstepping control technique is proposed for robot manipulator trajectory tracking. Firstly, the vector of current is considered as the control variable for robot manipulator mechanical subsystem by using the adaptive update algorithm of NN and an enclosed control input for the desired vector of current is constructed. So that, the goal of trajectory tracking of robot manipulator is achieved. Secondly, the voltage commands are constructed in order to control the joint currents to follow the anticipated value by using the NN controller in order to manipulate the dynamics of DC motor. Simplicity of control law is achieved by using proposed control technique along with low computational cost. In addition, robot manipulator and its actuator dynamics does not require the mathematical representation of model. The weight values of NN's and robotic manipulator parameters are adaptively updated. The efficiency and usefulness of proposed scheme on 2-DOF robot manipulator is analyzed by using the running mean error. The results depicts that the proposed model out perform than the conventional PD controller in terms of enhanced robotic manipulator trajectory tracking. © 2014 IEEE. Institute of Electrical and Electronics Engineers Inc. 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988259092&doi=10.1109%2fINMIC.2014.7097371&partnerID=40&md5=8f12c3a26df4a1cc7e8bab9ac7002276 Jamil, M.U. and Noor, M.N. and Raza, M.Q. and Rizvi, S. (2014) Backstepping control using adaptive neural network for industrial two link robot manipulator. In: UNSPECIFIED. http://eprints.utp.edu.my/31703/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This paper highlights, neural network (NN) based adaptive control using backstepping control technique is proposed for robot manipulator trajectory tracking. Firstly, the vector of current is considered as the control variable for robot manipulator mechanical subsystem by using the adaptive update algorithm of NN and an enclosed control input for the desired vector of current is constructed. So that, the goal of trajectory tracking of robot manipulator is achieved. Secondly, the voltage commands are constructed in order to control the joint currents to follow the anticipated value by using the NN controller in order to manipulate the dynamics of DC motor. Simplicity of control law is achieved by using proposed control technique along with low computational cost. In addition, robot manipulator and its actuator dynamics does not require the mathematical representation of model. The weight values of NN's and robotic manipulator parameters are adaptively updated. The efficiency and usefulness of proposed scheme on 2-DOF robot manipulator is analyzed by using the running mean error. The results depicts that the proposed model out perform than the conventional PD controller in terms of enhanced robotic manipulator trajectory tracking. © 2014 IEEE.
format Conference or Workshop Item
author Jamil, M.U.
Noor, M.N.
Raza, M.Q.
Rizvi, S.
spellingShingle Jamil, M.U.
Noor, M.N.
Raza, M.Q.
Rizvi, S.
Backstepping control using adaptive neural network for industrial two link robot manipulator
author_sort Jamil, M.U.
title Backstepping control using adaptive neural network for industrial two link robot manipulator
title_short Backstepping control using adaptive neural network for industrial two link robot manipulator
title_full Backstepping control using adaptive neural network for industrial two link robot manipulator
title_fullStr Backstepping control using adaptive neural network for industrial two link robot manipulator
title_full_unstemmed Backstepping control using adaptive neural network for industrial two link robot manipulator
title_sort backstepping control using adaptive neural network for industrial two link robot manipulator
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988259092&doi=10.1109%2fINMIC.2014.7097371&partnerID=40&md5=8f12c3a26df4a1cc7e8bab9ac7002276
http://eprints.utp.edu.my/31703/
_version_ 1741197621732573184
score 11.62408

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