RBF Neural Network-Based Terminal Sliding Mode Control for Robot Manipulators

The primarily focus of this paper is to develop a novel terminal sliding mode controller based on RBF neural network. To improve the control performance, a nonlinear term is included in control operation. The neural network system is adopted for estimation of nonlinear components. The neural network reconstruction error and bound on unstructured uncertainties are compensated with the help of adaptive control. Finally, simulation is performed with given manipulator that will provide the benefit of given method.