Error Tracking Learning Control for Nonlinearly Parameterized Systems with Input Deadzone

The trajectory tracking problem for nonlinearly parameterized uncertain systems with unknown input deadzone is investigated in this work. Lyapunov-based ILC strategy is adopted for controller design. Unlike traditional ILC methods, in this paper, the initial condition for each iteration allows to be any bounded value. By using the parameter separation technique combined with the signal replacement mechanism, a Lyapunov functional is constructed to design the ILC law and learning laws. As the iteration cycle increases, the system error can follow the predetermined desired trajectory over the entire interval, and all the signals in close-loop system are guaranteed to be bounded. Effectiveness of the proposed method is verified by theoretical results and numerical results.