On Iterative Learning Control—A Tihonov approach

Iterative Learning Control (ILC) problem is studied for single-input and single-output systems with measured noise in the measured output. An ILC algorithm is presented, which is designed based on Tihonov´s regularization theory. Since the real output of the underlined system is unknown, and the measured output is persistently corrupted by the noise, the classical iteration learning process may be ill-posed. Due to the regularization method, the presented algorithm can deal with the ill-posedness in the learning process well. Simulations illustrate that the desired trajectory can be tracked well when the learned control input act on the target system.