Neuro-fuzzy control of underwater vehicle-manipulator systems

This paper presents an intelligent controller for underwater vehicle-manipulator systems (UVMS) based on the neuro-fuzzy approach. The controller is composed of fuzzy PD control with membership function tuning by linguistic hedge. A neural network compensator approximates the dynamics of the UVMS in decentralized form. The new controller has the advantages of simplicity of implementation due to decentralized design, precision, and robustness to payload variations and hydrodynamic disturbances. It has significantly low energy consumption compared to both the conventional PD and conventional fuzzy control methods. The effectiveness of the proposed controller is illustrated by results of simulations for a six degrees of freedom autonomous underwater vehicle with a three degrees of freedom on-board manipulator.