Craig, Ian K2023-07-032023-07-0320232023Van Niekerk, 2023, On-line auto-tuning of multivariable industrial processes using Bayesian optimisation, Dissertation, University of Pretoria, Pretoria.http://hdl.handle.net/2263/91252DOI:https://doi.org/10.25403/UPresearchdata.23554806.v1Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2023.Auto-tuning of multi-input multi-output (MIMO) controllers of a bulk tailing treatment (BTT) surge tank is presented. Two controllers are selected for optimisation. The first controller is a decentralised proportional-integral (PI) controller that controls a plant simulated using a linear model. The second controller is a multivariable inverse PI controller that controls a plant simulated using a non-linear process model. Objective functions are designed to promote set point tracking and disturbance rejection. The search domain constraints are determined by intuitively expanding the search domain around the tuning parameters of the reference controller. Results show that Bayesian optimisation is successful in improving the performance of the set point tracking and disturbance rejection controllers for the surge tank process.en© 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.UCTDBayesian optimisationGaussian processesAuto-tuningMulti-input multi-output (MIMO) controllersBulk tailing treatmentEngineering, built environment and information technology SDG-09SDG-09: Industry, innovation and infrastructureDissertationu92215302https://doi.org/10.25403/UPresearchdata.23554806.v1