Robust PD-type iterative learning control design for uncertain batch processes subject to nonrepetitive disturbances

This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive disturbances. By means of two-dimensional/repetitive setting, the sufficient conditions for batch-to-batch error convergence and \mathcal{H}_{\infty} disturbance attenuati...

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Bibliographic Details
Published inChinese Control Conference pp. 2266 - 2271
Main Authors Maniarski, Robert, Paszke, Wojciech, Hao, Shoulin, Tao, Hongfeng
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 25.07.2022
Subjects
batch processes
Batch production systems
Design methodology
Injection molding
iterative learning control
nonrepetitive disturbances
Process control
repetitive processes
robust control
Sufficient conditions
Uncertainty
Velocity control
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Summary:This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive disturbances. By means of two-dimensional/repetitive setting, the sufficient conditions for batch-to-batch error convergence and \mathcal{H}_{\infty} disturbance attenuation are formulated and analyzed. Subsequently, the procedure for computing the desired control law matrices is formulated in terms of solvability of linear matrix inequalities. The proposed control law is able to fulfil the imposed design specifications, i.e., they are suitable for the batch processes with time-varying uncertainties as well as non-repetitive disturbances. An illustrative example is used to validate the proposed control scheme and demonstrates a possible applicability of the developed results.
ISSN:1934-1768
DOI:10.23919/CCC55666.2022.9901544