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.
|Number of pages||11|
|Journal||IEEE Transactions on Cybernetics|
|State||Published - Aug 2019|
Bibliographical noteFunding Information:
The author would like to thank the Reviewers from the IEEE TRANSACTIONS ON CYBERNETICS for the helpful comments and constructive suggestions during the review process. The author also would like to thank Prof. Xu's help and guidance, in research, life, and beyond.
© 2018 IEEE.
- Actuator fault
- iterative learning control (ILC)
- modifier functions
- nonrepetitive trajectory tracking
- system output constraint
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering