Reaching Law based Intelligent Sliding Mode Controller

This paper presents design of reaching law based intelligent Sliding Mode Controller to improve the control of 2 degree of freedom serial robotic joints.Sliding Mode Control is very prominent control structure for robust control of the nonlinear systems.In this study, Radial Basis Function based intelligent sliding mode control is designed.This novel Control structure has two important components.First one is reaching laws that ensured finite-time convergence of the system trajectories to the sliding surface while reducing the chattering and second part of the control structure is an adaptive Radial Basis Function Neural Network that addresses the unknown system dynamics and external disturbances.The combined approach improve the accuracy, stability, and robust under model variations.The systems closed loop stability analysis is guaranteed by Lyapunov stability and adaptive laws are derived for online updating of neural network parameters.The proposed intelligent structure is implemented to robotic joints and simulation results demonstrate the effectiveness of the proposed control structure by achieving more accurate tracking, low control effort, and better disturbance rejection compared to conventional sliding mode control.