Compensation‐based data‐driven ILC with input and output package dropouts

Abstract

SummaryThis paper explores the problem of random data loss at both input and output sides and proposes a compensation‐based data‐driven iterative learning control (cDDILC) to refrain from deteriorating of the control performance due to the data loss. A linear data model is first established to describe the input‐output dynamics of a repetitive control system in the iteration domain. The linear data model, which only virtually exists in the computer without any physical backgrounds, is employed as a predictive model to estimate and compensate the lost output data. Meanwhile, the lost input data is replaced by the corresponding input of the same time instant in the latest previous iterations. Then, a cDDILC is proposed by introducing two Bernoulli random variables to describe the stochastic data loss at both input and output sides. The proposed cDDILC method is data driven and independent of a precise plant model. Although the design and analysis of the cDDILC start from a MIMO linear repetitive system, one can easily extend the results to a MIMO nonlinear nonaffine one. Theoretical analysis and simulations confirm the efficiency of the proposed cDDILC method.