What is Iterative Learning Control and when is it applied?

ILC improves performance over repeated operations of the same task by learning from previous attempts. Control signal for trial k+1 uses error from trial k: u_{k+1}(t) = u_k(t) + L*e_k(t). The learning operator L is designed for convergence and robustness. ILC is effective when: task repeats identically, initial conditions reset, and error can be measured. Applications include robotic pick-and-place, batch processes, CNC machining, and printing. ILC complements feedback - feedback handles disturbances, ILC handles repetitive errors.