Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

Abstract

The finite time tracking control of n‐link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite‐time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite‐time command filtered control achieves finite‐time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.