Brief paper: Novel iterative learning controls for linear discrete-time systems based on a performance index over iterations

Authors:
Shengyue Yang;Zhihua Qu;Xiaoping Fan;Xiaohong Nian
Affiliations:
School of Information Science and Engineering, Central South University, Changsha, 410075, China;School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA;School of Information Science and Engineering, Central South University, Changsha, 410075, China;School of Information Science and Engineering, Central South University, Changsha, 410075, China
Venue:
Automatica (Journal of IFAC)
Year:
2008

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Abstract

An optimal iterative learning control (ILC) is proposed to optimize an accumulative quadratic performance index in the iteration domain for the nominal dynamics of linear discrete-time systems. Properties of stability, convergence, robustness, and optimality are investigated and demonstrated. In the case that the system under consideration contains uncertain dynamics, the proposed ILC design can be applied to yield a guaranteed-cost ILC whose solution can be found using the linear matrix inequality (LMI) technique. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed learning controls.