Robust networked ILC for switched nonlinear discrete systems with non-repetitive uncertainties and random data dropouts

Abstract

In this article, a robust networked iterative learning control (ILC) method is presented for switched nonlinear discrete-time systems (NDTS) subject to non-repetitive uncertainties and random data dropouts. In the proposed robust networked ILC scheme, the switching law, iterative initial state, and disturbances, all of which vary with iterations, are well addressed. Corresponding to the actuator side and the measurement side of the networked switched NDTS, the random data dropouts occurred are compensated by the input signals at last iteration and the reference outputs, respectively. As a result, it is theoretically proved that under the non-repetitive uncertainties of the switched NDTS, the mathematical expectation of ILC tracking error remains bounded during the ILC process. While the non-repetitive uncertainties are progressively convergent in iteration domain, a precise tracking to the reference trajectory in mathematical expectation sense can be achieved. The effectiveness of the proposed networked ILC design is validated by a numerical example.