Multiobjective tuning technique for MPC in grinding circuits

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dc.creatorAndre S. Yamashita
dc.creatorWellington T. Martins
dc.creatorThomás V. B. Pinto
dc.creatorGuilherme Vianna Raffo
dc.creatorThiago A. M. Euzébio
dc.date.accessioned2025-05-29T14:03:53Z
dc.date.accessioned2025-09-08T23:33:11Z
dc.date.available2025-05-29T14:03:53Z
dc.date.issued2023
dc.identifier.doi10.1109/ACCESS.2023.3269559
dc.identifier.issn21693536
dc.identifier.urihttps://hdl.handle.net/1843/82616
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.rightsAcesso Restrito
dc.subjectCircuitos integrados
dc.subject.otherTuning , Integrated circuit modeling , Process control , Monte Carlo methods , Biological system modeling , Optimization , Minerals
dc.subject.otherGrinding circuit , model predictive control , dynamic matrix control , controller tuning , multiobjective optimization , compromise optimization
dc.subject.otherModel Predictive Control , Tuning Techniques , Grinding Circuit , Linear Model , Optimization Problem , Nonlinear Model , Linear System , Feasible Solution , Multi-objective Optimization , Dead Time , Multi-objective Optimization Problem , Tuning Method , Closed-loop Performance , Tuning Procedure , Tuning Algorithm , Closed-loop Response , Transfer Function , Tuning Parameter , Adaptive Control , Process Performance , Prediction Horizon , Control Horizon , Output Response , Feed Flow Rate , Reference Trajectory , Predictive Control , Transfer Function Matrix , Fresh Feed , Nonlinear Model Predictive Control , Tuning Strategy
dc.titleMultiobjective tuning technique for MPC in grinding circuits
dc.typeArtigo de periódico
local.citation.epage43054
local.citation.spage43041
local.citation.volume11
local.description.resumoWe investigate the control challenges in grinding circuits—slow dynamics, long dead times, variable coupling— and the controller tuning challenge, that is, the difficulty in translating operating goals into tuning goals and closed-loop performance. A tuning algorithm for DMC (dynamic matrix control), suitable for the mineral processing industry, is proposed. The tuning problem is posed as a multiobjective optimization problem, in which the tuning goals are directly related to the desired closed-loop performance of process variables. The problem is solved using a compromise optimization, which minimizes the Euclidian distance between a feasible solution and the Utopia solution. Three case studies are presented, which validate the tuning algorithm for DMC in linear and non-linear grinding circuit models. The closed-loop performance obtained with the proposed tuning algorithm is compared to the one obtained through a benchmark tuning technique from the literature. The proposed tuning method has the following features: i) it shapes the closed-loop response according to the goal definitions for linear systems; ii) it requires tailored initial guesses and search spaces to converge to a stabilizing solution in non-linear applications; and iii) it allows the user to specify the desired closed-loop performance behavior in the tuning procedure, allowing the implementation of an adequate controller for each situation.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/10107407

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