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

Content Provider	IEEE Xplore Digital Library
Author	Jamil, M.U. Noor, M.N. Raza, M.Q. Rizvi, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Faisalabad, Faisalabad, Pakistan (Jamil, M.U.; Noor, M.N.; Raza, M.Q.) \|\| Dept. of Electr. & Electron. Eng., Univ. Teknol. PETRONAS, Tronoh, Malaysia (Rizvi, S.)
Abstract	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.
Starting Page	389
Ending Page	394
File Size	1996650
Page Count	6
File Format	PDF
ISBN	9781479957545
e-ISBN	9781479957552
DOI	10.1109/INMIC.2014.7097371
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-08
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manipulators Control systems Voltage control Artificial neural networks Nonlinear systems Backstepping
Content Type	Text
Resource Type	Article

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