Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation

In this paper, an uncertainty observation-based adaptive fuzzy neural dynamic surface control (UOB-AFNDSC) is proposed to investigate the problem of high-accuracy trajectory tracking of fully actuated underwater vehicles with respect to unknown uncertainties and input saturation. The control framework of UOB-AFNDSC is constructed using the dynamic surface technique, under which an online-succinct fuzzy-neuro uncertainty observer with both projection-based parameter learning and succinct network structure learning is constructed to online identify the lumped uncertainty term including system uncertainties and external disturbances. To further suppress the effect of uncertainty reconstruction error and input saturation error, two adaptive robust terms are introduced, respectively. To theoretically analyze the stability of the overall closed-loop control system, novel error variables are introduced to ensure the uniform ultimate boundedness of all signals, and the tracking accuracy can be easily adjusted by the width parameter of novel error variables. Finally, some simulations are carried out to demonstrate the effectiveness of the proposed control scheme.