Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCAS.2013.6703933
Title: Iterative learning control of linear distributed parameter systems-frequency domain design and analysis
Authors: Huang, D.
Li, X.
Xu, J.-X. 
Xu, C.
Keywords: Boundary control
Convergence
Distributed parameter system
Iterative learning control
Transfer function
Issue Date: 2013
Source: Huang, D.,Li, X.,Xu, J.-X.,Xu, C. (2013). Iterative learning control of linear distributed parameter systems-frequency domain design and analysis. International Conference on Control, Automation and Systems : 574-579. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCAS.2013.6703933
Abstract: This paper aims at iterative learning control (ILC) design and analysis for a class of linear distributed parameter systems (DPSs) that may be hyperbolic, parabolic, or elliptic, and include many important physical processes such as diffusion, vibration, heat conduction and wave propagation as special cases. Owing to the linear characteristic of systems, the system equations are first cast into a matrix form in the Laplace transform domain. Then, through determination of a fundamental matrix, the system transfer function is precisely evaluated in a closed form. The derived transfer function clearly demonstrates the input-output relationship of system, and thus facilitates the consequent ILC design and convergence analysis in the frequency domain. The proposed control design scheme is able to deal with parametric and non-parametric uncertainties and makes full use of the process repetition, while avoids any simplification or discretization for the 3D dynamics of distributed parameter systems in the time, space, and iteration domains. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed ILC scheme. © 2013 IEEE.
Source Title: International Conference on Control, Automation and Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/83875
ISBN: 9788993215052
ISSN: 15987833
DOI: 10.1109/ICCAS.2013.6703933
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