Convergence of a State Feedback-based Iterative Learning Control Scheme for Nonlinear Systems with Random Data Dropouts

The paper investigates the almost sure convergence of a state feedback-based iterative learning control (ILC) scheme for a class of discrete-time single-input-single-output (SISO) nonlinear systems with random data dropouts. By the contraction mapping method, it is strictly proved that the tracking error of the state feedback-based ILC system converges to zero almost surely if and only if the general spectral radius condition is satisfied. An example is given to show the effectiveness of the state feedback-based ILC scheme and the correctness of the theoretical analysis.