Adaptive iterative learning control for discrete-time nonlinear systems with unknown control direction

An adaptive iterative learning control method is proposed for a class of discrete-time systems with random initial condition and iteration-varying desired trajectory. The discrete Nussbaum gain is incorporated into the control design to deal with the lack of a prior knowledge of the control direction. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error would converge to zero asymptotically along the iterative learning axis. The effectiveness of the proposed control law is verified through numerical simulation.