Neural network approximation-based nonsingular terminal sliding mode control for trajectory tracking of robotic airships

Abstract

This paper proposes a neural network approximation-based nonsingular terminal sliding mode control (NN-NTSMC) approach to address the problem of trajectory tracking for robotic airships. First, dynamics model of an airship and control problem of trajectory tracking are formulated. Second, a nonsingular terminal sliding mode controller (NTSMC) combined with neural network (NN) approximation is designed to track the commanded trajectory. Finally, the effectiveness and robustness of the designed controller are illustrated through simulation results. Simulation results indicate that NN-NTSMC reduces chattering effectively and ensures faster convergence and better tacking precision against linear hyperplane-based sliding mode control (LSMC).