Theoretical and experimental studies on iterative learning control for underwater robots

On underwater robot manipulators, high speed and high precision are basic requirements in order to improve efficiency of operations. To satisfy these requirements, feedforward control inputs are crucial. For making feedforward inputs, one method is to estimate all parameters of the robot dynamics, including hydrodynamic terms such as added-mass, drag force and buoyancy. However, the parameter estimation of hydrodynamic coefficients is not suitable for forming the feedforward control inputs of underwater robot manipulators, because it is difficult to model and estimate the hydrodynamic terms. TO overcome such a difficulty, we apply iterative learning control to underwater robots. In this paper, we theoretically and experimentally investigate the performance of iterative learning control for underwater robot manipulators. The effectiveness of iterative learning control is demonstrated through several experimental results.