A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

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 learning with minimal parameter (LMP) technique robotic manipulator tracking. decreases online adaptive parameters number to only one, lowering computational costs boosting real-time performance. RBFNN analyses system's hidden non-linearities, its weight value are updated using laws control nonlinear output track specific trajectory. model used create Lyapunov function-based law. effectiveness designed NNNSMAC demonstrated by simulation results 2 dof Robotic Manipulator. chattering effect has been significantly reduced.