An Iterative Learning Extended-State Observer-Based Fuzzy Fault-Tolerant Control Approach for AUVs

In this article, an iterative learning algorithm based on extended state observer (ESO) is proposed to deal with the propeller failure of an underwater vehicle. In this control scheme, the nonlinear feedback mechanism of ESO is transplanted to iterative learning processes; that is, the nonlinear function of the current output residual is used to adjust the value of the virtual fault in the next iteration. Additionally, to ensure the safety of the control torque, a saturated proportional-derivative (PD) controller is proposed. Finally, to achieve online parameter self-tuning, a fuzzy logic controller is employed in this control scheme to fuzzify the parameters of a saturated PD controller and ESO. The obtained results show the favorable speed of tracking convergence and the high precision of fault estimation.