A Neural Terminal Sliding Mode Control for Tracking Control of Robotic Manipulators in Uncertain Dynamical Environments.

This article investigates a control algorithm for trajectory tracking control of robot manipulators in uncertain dynamical environments. To deal with chattering behavior that always still exists in the conventional sliding mode control, to remove the requirement about a dynamic model of the uncertain robot system, and prior knowledge of the unknown function such as its upper boundary, the proposed solution is to develop a control method that combines the advantages of both terminal sliding mode control and radial basis function neural network. Furthermore, the proposed controller’s fast stabilization and convergence are also significantly improved by using a novel adaptive fast reaching control law. Hence, the proposed controller’s performance expectations are always guaranteed such as high tracking accuracy, fast stabilization, chattering reduction, fast convergence, and robustness to uncertain dynamical environments. Especially, the proposed controller can operate without the robot’s dynamic model. Both theoretical investigations based on Lyapunov stability theory and computer simulation using MATLAB/Simulink are presented to confirm the effectiveness of the proposed control solution.