Convergence analysis of proportional-derivative -type ILC for linear continuous constant time delay switched systems with observation noise and state uncertainties

This article is concerned with the linear continuous time delay switching system with state uncertainties and observation noise. The goal of this study is to investigate how an internal switching mechanism and the ecacy of a conventional proportional- derivative ILC method is impacted by ambient noise for linear continuous-time switching systems. The  ndings demonstrate that learning gains and the dynamics of the subsystems, rather than the time-driven switching rule, are primarily responsible for the convergence and robustness of the control method. An appropriate selection of learning gains can ensure the control algorithm's convergence and resilience given any arbitrary time-varying switching rule.

Convergence analysis of proportional-derivative -type ILC for linear continuous constant time delay switched systems with observation noise and state uncertainties Keywords:

Iterative learning control , switched system , Dynamical system , time delay , bounded state disturbance , bounded observation noise