Fault Tolerant Nonrepetitive Trajectory Tracking for MIMO Output Constrained Nonlinear Systems Using Iterative Learning Control

Most works on iterative learning control (ILC) assume identical reference trajectories for the system state over the iteration domain. This fundamental assumption may not always hold in practice, where the desired trajectories or control objectives may be iteration dependent. In this paper, we relax this fundamental assumption, by introducing a new way of modifying the reference trajectories. The concept of modifier functions has been introduced for the first time in the ILC literature. This proposed approach is also a unified framework that can handle other common types of initial conditions in ILC. Multi-input multi-output nonlinear systems are considered, which can be subject to the actuator faults. Time and iteration dependent constraint requirements on the system output can be effectively handled. Backstepping design and composite energy function approach are used in the analysis. We show that in the closed loop analysis, the proposed control scheme can guarantee uniform convergence on the full state tracking error over the iteration domain, beyond a small initial time interval in each iteration, while the constraint requirements on the system output are never violated. In the end two simulation examples are shown to illustrate the efficacy of the proposed ILC algorithm.