In this paper, two adaptive iterative learning control schemes are tested experimentally on a five-degrees-of-freedom (5-DOF) robot manipulator CATALYST5. The control strategy consists of using a classical PD structure plus an additional iteratively updated term designed to cope with the unknown parameters and disturbances. The control design is very simple in the sense that the only requirement on the PD and learning gains is the positive definiteness condition. In contrast with classical iterative learning control schemes, where the number of iterative variables is generally equal to the number of control inputs, the proposed controllers use just one or two iterative variables. In this framework, the acceleration measurements and the bounds of the robot parameters are not needed.
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