Data-driven optimization framework for nonlinear model predictive control
type: Item
instance of: scholarly article
title: Data-driven optimization framework for nonlinear model predictive control
zbMATH Open document ID: 1426.93093
DOI: 10.1155/2017/9402684
author: Shiliang Zhang
author: Hui Cao
author: Lixin Jia
author: Zonglin Ye
author: Xiali Hei
author: Yanbin Zhang
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: The structure of the optimization procedure may affect the control quality of nonlinear model predictive control (MPC). In this paper, a data-driven optimization framework for nonlinear MPC is proposed, where the linguistic model is employed as the prediction model. The linguistic model consists of a series of fuzzy rules, whose antecedents are the membership functions of the input variables and the consequences are the predicted output represented by linear combinations of the input variables. The linear properties of the consequences lead to a quadratic optimization framework without online linearisation, which has analytical solution in the calculation of control sequence. Both the parameters in the antecedents and the consequences are calculated by a hybrid-learning algorithm based on plant data, and the data-driven determination of the parameters leads to an optimization framework with optimized controller parameters, which could provide higher control accuracy. Experiments are conducted in the process control of biochemical continuous sterilization, and the performance of the proposed method is compared with those of the methods of MPC based on linear model, the nonlinear MPC with neural network approximator, and MPC nonlinear with successive linearisations. The experimental results verify that the proposed framework could achieve higher control accuracy.
Mathematics Subject Classification ID: 93B45
Mathematics Subject Classification ID: 93B18
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 6972675
zbMATH Keywords: nonlinear model predictive control
zbMATH Keywords: hybrid-learning algorithm
zbMATH Keywords: linearization
describes a project that uses: ANFIS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/9402684
OpenAlex ID: W2747143215
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description: scientific article; zbMATH DE number 6972675