Observer-based data-driven iterative learning control

Abstract

By considering non-repetitive uncertainties, an observer-based data-driven iterative learning control (ObDDILC) is proposed in this article for non-linear non-affine systems in the existence of non-repeatable disturbances, random initial values, and input constraints. Aiming to addressing the non-affine and non-linear characteristics of the systems, a linear data model (LDM) is constructed in iteration domain without introducing any physical interpretation but only for the purpose of the subsequent algorithm design and analysis. The iteration-varying initial values and disturbances are incorporated as a total non-repetitive uncertainty of the LDM. Both an iterative learning observer and a parameter estimator are proposed to address the total non-repetitive uncertainties and unknown parameters of the established LDM, respectively. Then, an observer-based learning control law is developed using the estimated output to compensate the impact of the non-repetitive uncertainties on the control performance, where a saturated function is employed to deal with the input constraints. The convergence of proposed ObDDILC is proved by using contraction mapping as the basic tool. All of the algorithms are designed and analysed without dependence of any model information except I/O data. The theoretical results are tested by simulations.