Sampled-data iterative learning control for singular systems

Sampled-data iterative learning control (SILC) for singular systems is addressed for the first time. With the introduction of the constrained relative degree, an SILC algorithm combined with a feedback control law is proposed for singular systems. Convergence of the algorithm is proved in sup-norm, while the conventional convergence analysis is in λ-norm. The final tracking error uniformly converges to a small residual set whose level of magnitude depends on the system dynamics and the sampling-period. Inequalities to estimate the level the existing results of SILC, convergence is guaranteed not only at the sampling instants but on the entire operation interval, thus the inter-sample behavior guaranteed, which is more practical for real implementation.