LAUSR

Funding information National Natural Science Foundation of China, Grant/Award Number: 61673045 and 61304085; Beijing Natural Science Foundation, Grant/Award Number: 4152040 Summary This paper proposes robust iterative learning control schemes for continuous-time nonlinear systems with various nonparametric uncertainties under nonuniform trial length circumstances. The nonuniform trial length is described by a random variable, which causes a random data missing problem while designing and analyzing algorithms for the precise tracking problem. Three common types of nonparametric uncertainties are taken into account: norm-bounded uncertainty, variation-norm-bounded uncertainty, and norm-bounded uncertainty with unknown coefficients. A novel composite energy function is introduced with the help of a newly defined virtual tracking error for the asymptotical convergence of the proposed schemes. Extensions to multiple-input–multiple-output cases are also elaborated. Illustrative simulations are provided to verify the theoretical results.