  We propose novel iterative learning control algorithms to track a reference
trajectory in resource-constrained control systems. In many applications, there
are constraints on the number of control actions, delivered to the actuator
from the controller, due to the limited bandwidth of communication channels or
battery-operated sensors and actuators. We devise iterative learning techniques
that create sparse control sequences with reduced communication and actuation
instances while providing sensible reference tracking precision. Numerical
simulations are provided to demonstrate the effectiveness of the proposed
control method.
