Adaptive neural network control for a soft robotic manipulator

This paper mainly introduces the modeling based on Cosserat rod theory and focuses on the adaptive neural network controller design based on model. In dealing with the external interference with the environment and the unmodeled dynamics of the system, a neural network (NN) is introduced to compensate it, and using Backstepping method to design the adaptive controller, finally the stability of the closed-loop system and the convergence of the signal in the system is proved with Lyapunov function theory, ensuring the end of the manipulator can track the given signal. In addition, the simulation experiment of soft manipulator swing and constant angle tracking control are carried out, and the simulation shows the rationality of the proposed controller.