Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation
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 July 24, 2021. | Revised Manuscript received on August 19, 2021. | Manuscript published on August 30, 2021. | PP: 120-123 | Volume-10 Issue-6, August 2021 | Retrieval Number: 100.1/ijeat.F30050810621 | DOI: 10.35940/ijeat.F3005.0810621
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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: This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory tracking controller without any modelling information. The ASMCNN is designed to have robust trajectory tracking of Robot Manipulator, which combines Neural Network Estimation with Adaptive Sliding Mode Control. The RBF model is utilised to construct a Lyapunov function-based adaptive control approach. Simulation of the tracking control of a 2dof Robotic Manipulator in the presence of unpredictability and external disruption demonstrates the usefulness of the planned ASMCNN.
Keywords: Sliding Mode Control, Robot manipulator, Trajectory Tracking, Neural Network.