Disturbance Rejection for Underwater robotic vehicle based on adaptive fuzzy with nonlinear PID controller

In this paper, an adaptive fuzzy with a nonlinear PID (AFNLPID) controller is suggested to solve and eliminate the effect of the disturbances regularly caused by the ocean currents and the unknown uncertainties of the underwater robotic vehicle (URV) dynamics. The main principle of the AFNLPID controller is to estimate the unknown dynamics parameters of the URV and estimate the unknown disturbances. Two types of disturbances are presented that are deepwater wave disturbances and near-surface shallow water wave disturbances. The outstanding properties of the AFNLPID were evaluated by comparing the designed controller with other existing works that are adaptive fuzzy PID (AFPID, AFPID2) controllers, conventional PID controllers, and nonlinear fractional-order PID (NLFOPID) controllers. At the end, the collected results show that the AFNLPID controller improved the efficiency of the URV for deepwater disturbances by 42.0663%, 37.0490%, 24.4449%, 64.7356%, and 51.7643% for AFPID, AFPID2, AFNLPID2, PID, and NLFOPID controllers, respectively. At the same time, the AFNLPID improved the efficiency of the URV for near-surface wave disturbances by 45.0911%, 32.2492%, 20.3839%, 56.5498%, and 51.3964% for AFPID, AFPID2, AFNLPID2, PID, and NLFOPID controllers, respectively. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes an adaptive fuzzy with a nonlinear PID (AFNLPID) controller to address and eliminate disturbances caused by ocean currents and unknown uncertainties in underwater robotic vehicle (URV) dynamics. The AFNLPID controller estimates the unknown dynamics parameters of the URV and unknown disturbances. Two types of disturbances, deepwater wave disturbances and near-surface shallow water wave disturbances, are presented. The performance of the AFNLPID controller is evaluated by comparing it with other controllers, such as adaptive fuzzy PID (AFPID, AFPID2) controllers, conventional PID controllers, and nonlinear fractional-order PID (NLFOPID) controllers.