Adaptive Robust RBF-NN Nonsingular Terminal Sliding Mode Control Scheme for Application to Snake Robot’s Head for Image Stabilization

Image stabilization is important for snake robots to be used as mobile robots. In this paper, we propose an adaptive robust RBF neural network nonsingular terminal sliding mode control reduce swinging in the robot’s head while it being driven. To avoid complex dynamic problems and interference during driving, a 2-DOF system. We designed system based on control, which ensures fast response finite time convergence. chattering, incorporated that can compensate disturbances. Additionally, included term address disadvantages of network-based control. The generates inputs error conjunction with reverse saturation function eliminate chattering. update law Lyapunov’s theory. proved stability proposed controller by investigating convergence before after operation section. Finally, verified performance through computer simulation. simulation evaluates controllers using sinusoidal reference signal similar robot movement mixed considering controller’s waste case. has excellent tracking improved chattering compared previous controller.