On robust modifications for repetitive learning control

In this paper, we develop two algorithms to robustify repetitive learning control (RLC), which deals with periodic tracking tasks for nonlinear dynamical systems with nonparametric uncertainties. The first robustification algorithm is to apply a projection operator to the control input signals directly. The second robustification algorithm is to add a damping term to the learning law. Both algorithms ensure the boundedness of the learning signals. The effectiveness of the proposed robust algorithms are verified through theoretical analysis and validated through a numerical example.

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