Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities

• Published in: IEEE transactions on industrial electronics (1982) Vol. 64; no. 8; pp. 6785 - 6795
• Main Authors: Cui, Rongxin, Chen, Lepeng, Yang, Chenguang, Chen, Mou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithms; Adaptive control; Angular velocity; Disturbances; Extended state observer (ESO); Fluid dynamics; Fluid flow; Hydrodynamics; integral sliding mode controller (ISMC); Integrals; MIMO (control systems); Observers; Robot control; Robots; Sliding mode control; Stability; State observers; Trajectory tracking; Uncertainty; Uncertainty analysis; underwater robot; Underwater robots; underwater vehicle; Unmanned underwater vehicles; Upper bounds; Vehicle dynamics
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2017.2694410

This paper develops a novel integral sliding mode controller (ISMC) for a general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured velocities, unknown disturbances, and uncertain hydrodynamics of the robot have been successfully solved in the control design. An adaptive MIMO-ESO is designed not only to estimate the unmeasurable linear and angular velocities, but also to estimate the unknown external disturbances. An ISMC is then designed using Lyapunov synthesis, and an adaptive gain update algorithm is introduced to estimate the upper bound of the uncertainties. Rigorous theoretical analysis is performed to show that the proposed control method is able to achieve asymptotical tracking performance for the underwater robot. Experimental studies are also carried out to validate the effectiveness of the proposed control, and to show that the proposed approach performs better than a conventional potential difference (PD) control approach.