Noise Tolerant Iterative Learning Control and Identification for Continuous-Time Systems With Unknown Bounded Input Disturbances

This paper considers the problems of both noise tolerant iterative learning control (ILC) and iterative identification for a class of continuous-time systems with unknown bounded input disturbance and measurement noise. To this aim, we first propose a formulation of an extended ILC scheme using sampled input∕output (I∕O) data. The proposed ILC method has distinctive features as follows. Its learning law works in a prescribed finite-dimensional parameter space and employs I∕O data of all past trials efficiently. Also, the time derivative of tracking error is not required. Then, it is presented how the uncertain parameters can be identified by using the proposed ILC algorithm and how robust it is against measurement noise through a numerical example. Furthermore, its experimental evaluation is performed to demonstrate the effectiveness of the proposed identification scheme.