AILC for nonlinearly parameterized systems with unknown distributed time-varying delays and unknown control direction

This paper addresses a new adaptive iterative learning control (AILC) approach for a class of nonlinear parameterized systems with unknown distributed time-varying delays and unknown control direction. By using the parameter separation technique combined with the signal replacement mechanism, a novel adaptive control strategy is designed to ensure the tracking error converging to zero on a finite time-interval in the mean-square sense. Simultaneously, Nussbaum-type function is used to detect the unknown control direction. The stability of the tracking error is also proven by constructing a Lyapunov-Krasovskii-like composite energy function (CEF). Two simulations example are presented to illustrate the effectiveness of the proposed control algorithms.