Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/95852
Title: Data-driven optimal terminal iterative learning control
Authors: Chi, Ronghu
Wang, Danwei
Hou, Zhongsheng
Jin, Shangtai
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Chi, R., Wang, D., Hou, Z., & Jin, S. (2012). Data-driven optimal terminal iterative learning control. Journal of Process Control, 22(10), 2026-2037.
Series/Report no.: Journal of process control
Abstract: This paper presents a data-driven optimal terminal iterative learning control (TILC) approach for linear and nonlinear discrete-time systems. The iterative learning control law is updated from only terminal output tracking error instead of entire output trajectory tracking error. The only required knowledge of a controlled system is that the Markov matrices of linear systems or the partial derivatives of nonlinear systems with respect to control inputs are bounded. Rigorous analysis and convergence proof are developed with sufficient conditions for the terminal ILC design and the results are developed for both linear and nonlinear discrete-time systems. Simulation results illustrate the applicability and effectiveness of the proposed approach.
URI: https://hdl.handle.net/10356/95852
http://hdl.handle.net/10220/11300
ISSN: 0959-1524
DOI: 10.1016/j.jprocont.2012.08.001
Rights: © 2012 Elsevier Ltd.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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