Robust Iterative Learning Control for a Class of Linear Systems with Data Dropouts

Abstract EN

A robust iterative learning control algorithm is proposed for a class of intermittent systems with disturbances and uncertain initial conditions.

Based on the contraction mapping approach, the convergence condition for the proposed algorithm is first given, and then the bounds on control input and output trajectories can be obtained.

It is shown that these bounds depend on bounds on the initial condition errors and disturbances, and the bounds are zero in the absence of these disturbances.

A numerical example is also given to verify the theoretical result.