Adaptive Sliding Mode Neural Network-Based Composite Control of Robot Manipulators for Trajectory Tracking

摘要

In this paper, an adaptive sliding mode control for a robot manipulator based on RBF neural network is proposed. A new form of fast terminal sliding mode is used in this approach. Neural networks is developed to estimate unknown terms of the robot manipulator. The tracking performance of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results verify the nonsingular fast terminal sliding mode controller can achieve faster and higher precision performance in comparison with the conventional continuous sliding mode control method.