Many underwater robotic vehicles use propeller-type thrusters driven by DC motors. The thruster system is known to be nonlinear and time-varying. Robust control of the thruster system is crucial to navigate and achieve high performance. This paper presents experimental results of five different thruster control systems based on online neural network (NN) control, off-line NN control, fuzzy control, adaptive-learning control and PID control. These control systems were initially designed or trained for the thruster system with no fin. Then each controller was implemented for the thruster system modified with different size fins. The fins changed the system hydrodynamics, especially the drag force. In this paper, each control system is briefly described and its practical advantages and disadvantages are discussed with experimental results.
['C. L. Tsukamoto', 'W. Lee', 'Junku Yuh', 'Song K. Choi', 'Jäntschi Lorentz']
Comparison study on advanced thruster control of underwater robots