Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12756
Title: PD-type iterative learning control for uncertain spatially interconnected systems
Authors: Zhou L.
Tao H.
Paszke, Wojciech
Stojanović, Vladimir
Yang H.
Journal: Mathematics
Issue Date: 1-Sep-2020
Abstract: © 2020 by the authors. This paper puts forward a PD-type iterative learning control algorithm for a class of discrete spatially interconnected systems with unstructured uncertainty. By lifting and changing the variable of discrete space model, the uncertain spatially interconnected systems is converted into equivalent singular system, and the general state space model is derived in view of singular system theory. Then, the state error and output error information are used to design the iterative learning control law, transforming the controlled system into an equivalent repetitive process model. Based on the stability theory of repetitive process, sufficient condition for the stability of the system along the trial is given in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed algorithm is verified by the simulation of ladder circuits.
URI: https://scidar.kg.ac.rs/handle/123456789/12756
Type: Article
DOI: 10.3390/math8091528
SCOPUS: 85091373391
Appears in Collections:Faculty of Mechanical and Civil Engineering, Kraljevo
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