Abstract
This paper considers the convergence for iterative learning control (ILC) of the non-repetitive continuous-time system. The non-repetitive system is characterized by iteration-varying uncertainties in the plant model matrices, the initial state, disturbances and the desired output during a finite time. We employed the P-type learning law to modify the control input, where the learning gain matrix varies with iteration number, then used lambda-norm and Gronwall-Bellman's Lemma to prove iteration-varying state and control input are bounded. Moreover the tracking error can converge to zero when the iteration-varying uncertainties all converge with increasing iteration. Numerical simulation illustrates the effectiveness of ILC scheme.