Tuning Methods for Model Predictive Controllers | Daniel H. Olesen
| | Abstract | Model Predictive Control (MPC) is an optimal control strategy, and can be considered as an extension of the Linear Quadratic Gaussian Controller. It has become a popular control strategy in industry, since it provides a systematic approach in handling constraints on outputs and actuators.
The aim of this thesis has been to investigate tuning methods for ARIMAX-based predictive controllers. This class of controllers have been chosen because of the ability to obtain off-set free tracking in the face of constant disturbances.
We have evaluated diff erent performance measures for a closed loop control system to asses deterministic, stochastic and robust performance. The measures has been used to develop a tuning toolbox for SISO systems, which visualizes the performance of control designs. A study has been performed in expressing performance measures for MIMO systems as scalar quantities. The derived measures has been used to de fine an optimization problem, which synthesize tunings based on deterministic and stochastic objectives with ensured robustness.
We have succesfully applied the developed methods for tuning of a Gas-Oil Furnace, a Wood-Berry Distillation Column and a Cement Mill Circuit. | | Type | Master's thesis [Academic thesis] | | Year | 2012 | | Publisher | Technical University of Denmark, DTU Informatics, E-mail: reception@imm.dtu.dk | | Address | Asmussens Alle, Building 305, DK-2800 Kgs. Lyngby, Denmark | | Series | IMM-M.Sc.-2012-69 | | Note | | | Electronic version(s) | [pdf] | | Publication link | http://www.imm.dtu.dk/English.aspx | | BibTeX data | [bibtex] | | IMM Group(s) | Scientific Computing |
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