Iterative learning control of nonlinear systems with Hilfer fractional derivative for enhancing precision in robotic manipulator

Abstract

$P$ type for a class of Hilfer-type fractional-order nonlinear systems (HFONS) is presented in this study. In order to guarantee the robust convergence of tracking errors under the suggested ILC scheme, sufficient conditions are created by applying the generalized Gronwall-Bellman lemma in conjunction with $(1-\varpi, \Lambda)$-norm estimates. The system can better withstand initial deviations thanks to the method's consideration of both input learning and initial state learning. A numerical example with a robotic manipulator is used to validate the theoretical conclusions, and the outcomes validate the convergence of the suggested methodology.