A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator
Monisha Pathak1, Mrinal Buragohain2

1Monisha Pathak*, Department of Instrumentation Engineering, Jorhat Engineering College, Jorhat, Assam, India.
2Dr. Mrinal Buragohain, Department of Electrical Engineering, Jorhat Engineering College, Jorhat, Assam, India.
Manuscript received on November 10, 2021. | Revised Manuscript received on November 15, 2021. | Manuscript published on December 30, 2021. | PP: 12-16 | Volume-11 Issue-2, December 2021. | Retrieval Number: 100.1/ijeat.B32171211221 | DOI: 10.35940/ijeat.B3217.1211221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system’s hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system’s output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.
Keywords: Neural Network, Sliding Mode Control, Robot manipulator, Trajectory Tracking.