Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

This paper investigates an adaptive fuzzy controller prescribed performance for uncertain robot systems with actuator saturation. To mitigate the impact of model uncertainty and unknown disturbance, an adaptive fuzzy neural network (AFNN) is designed to approximate model uncertainty, and an adaptive disturbance observer (ADO) based on the AFNN is constructed to approximate the disturbance. For multi-degree-of-freedom robotic systems, an auxiliary system is constructed to alleviate the problem of actuator saturation. Combined with the barrier Lyapunov function, an adaptive prescribed performance controller is designed to realize finite-time tracking control for robotic systems with model uncertainty, external disturbance, and actuator saturation. The superiority and practicability of the designed control method are verified by simulations and experiments.