Repetition allows the system to improve tracking accuracy from repetition to repetition, in effect learning the required input needed to track the reference exactly. The learning process uses information from previous repetitions to improve the control signal ultimately enabling a suitable control action can 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:
