An iterative learning controller for reduction of repeatable runout in hard disk drives

In this study we consider the iterative learning control (ILC) framework to design a reference signal that reduces the periodic component of disturbances in a feedback measurement containing both repeatable and non-repeatable components. Reduction of periodic disturbances is useful in alleviating undesirable repeatable tracking errors in applications such as the two-stage servo track writing process for disk drives. A general problem description is given for a linear discrete time system and convergence robustness results for the learning system are derived. A learning filter is designed with the use of an FIR model approximation for the inverse of the closed-loop sensitivity for fast nominal convergence while robustness to modeling errors and non-repeatable disturbances is achieved through additional filtering. The ILC algorithm is applied to a disk drive system where experimental results demonstrate the effectiveness of the design method in reducing periodic measurement disturbances.