The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

Yan Geng, Shouqin Wang and Xiaoe Ruan
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Yan Geng: Department of Applied Mathematics, School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China
Shouqin Wang: Department of Applied Mathematics, School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China
Xiaoe Ruan: Department of Applied Mathematics, School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China

Mathematics, 2022, vol. 10, issue 13, 1-19

Abstract: For multi-phase batch processes with different dimensions whose dynamics can be described as a linear discrete-time-invariant system in each phase, a data-driven optimal ILC was explored using multi-operation input and output data that subordinate a tracking performance criterion. An iterative learning identification was constructed to estimate the system Markov parameters by minimizing the evaluation criterion that consists of the residual of the real outputs from the predicted outputs and two adjacent identifications. Meanwhile, the estimated Markov parameters matrix was embedded into the learning control process in the form of an interaction. By virtue of inner product theory, the monotonic descent of the estimation error was derived, which does not restrict the weighting factor at all. Furthermore, algebraic derivation demonstrates that the tracking is strictly monotonically convergent if the estimation error falls within an appropriate domain. Numerical simulations were carried out to illustrate the validity and the effectiveness of the proposed method.

Keywords: iterative learning control; data-driven; multi-phase batch processes; iterative learning identification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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