Time optimal control for underwater Robot manipulators based on iterative learning control and time-scale transformation

For underwater robot manipulators, this paper presents a new time optimal control method without parameter estimation. The proposed method is based on iterative learning control and time-scale transformation. With this method, it is not necessary to estimate any physical parameters in order to form an input torque pattern that realizes a minimum time motion. Therefore, this method is very practical for control of the underwater robot manipulators with very complicated dynamics expressed by a lot of parameters including hydrodynamic parameters such as added-mass and drag coefficients. To realize time optimal control, we consider a class of motions whose spatial paths are fixed, but whose time trajectories (speed patterns) are changed. By using time-scale transformation, the feedforward input pattern is formed from the basic torque patterns obtained through iterative learning control. In this paper, we theoretically derive the time optical control method based on iterative learning control and time-scale transformation. The effectiveness of our proposed method is verified by several experiments and the results are presented.