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.
Highlights
One of the most prominent intelligent computation systems is the neural network, which has an innate learning capability and can estimate any nonlinear continuing function with precision
Sliding mode control necessitates information on the top bound of model unpredictability and external disruptions, and there is always chattering present in practical applications
The RBF neural network approximates the unknown nonlinearities in the system and the weight values are adjusted online based on adaptive laws to control the nonlinear system and to have satisfactory tracking of trajectory
Summary
One of the most prominent intelligent computation systems is the neural network, which has an innate learning capability and can estimate any nonlinear continuing function with precision. Many adaptive neural network control strategy have been created to design robust tracking control of robot manipulators with high nonlinearity using the universal estimation property of many layer neural networks. Sliding mode control (SMC) is one of the most powerful robust non-linear control technique for robot manipulators tracking control in the presence of parametric unpredictability and external disruption [5,6,9]. Designing a robust controller for robot manipulator trajectory tracking is a very challenging task.[6]. In this work a sliding mode with adaptive control based on neural network is designed for trajectory tracking of robot manipulator with modelling uncertainties. The RBF neural network approximates the unknown nonlinearities in the system and the weight values are adjusted online based on adaptive laws to control the nonlinear system and to have satisfactory tracking of trajectory.
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