Iterative Learning Control (ILC) is an open-loop control approach of tracking control for systems that work in a repetitive mode. Examples of systems that operate in a repetitive manner include robot arm manipulators, chemical batch processes and reliability testing rigs. In each of these tasks the system is required to perform the same action over and over again with high precision on a finite time interval.

Repetition allows the system to sequentially improve tracking accuracy, in effect learning the required input needed to track the reference as closely as possible. The learning process uses information from previous repetitions to improve the control signal, ultimately enabling a suitable control action to be found iteratively. The internal model principle yields conditions under which perfect tracking can be achieved but the design of the control algorithm still leaves many decisions to be made to suit the application. A typical, simple control law is of the form:

. is crucial to achieving design objectives (i.e. trading off fast convergence and robust performance) and ranges from simple scalar gains to sophisticated optimization computations.

In many cases a low-pass filter is added to the input to improve performance. The control law then takes the form

is a low-pass filtering matrix. This removes high-frequency disturbances which may otherwise be amplified during the learning process.

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