Adaptive iterative learning control for robot manipulators

In this paper, using a Lyapunov-like function, we derive an adaptive iterative learning control (ILC) scheme for the trajectory tracking problem of rigid robot manipulators, with unknown parameters, performing repetitive tasks. The control scheme is nothing else but a PD controller plus an 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 ILC schemes where the number of iterative variables is generally equal to the number of control inputs, the proposed controller uses just two iterative variables. In this framework, the acceleration measurements and the bounds of the robot parameters are not needed. Furthermore, we show that it is possible to use a single iterative variable in the control scheme at the expense of the knowledge of some bounds of the system parameters.

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