An indirect iterative learning controller for nonlinear systems with mismatched uncertainties and matched disturbances

The article proposes an intelligent controller for output regulation of nonlinear non-autonomous continuous-time systems with mismatched uncertainties and matched disturbances. This controller is established by firstly converting the original system into an input-to-state stable one via learning-based compensation of lumped system disturbances and then applying the standard P-type iterative learning control. The learning-based disturbance compensator is basically created with the numerical backward differentiation in iterative mode. The control performance of the obtained sampled data system using the proposed controller has been verified throughout both in theory and comparison with the existing method via numerical simulations.