TAILIEUCHUNG - SYSTEMATIC METHOD FOR ANALYSIS OF PERFORMANCE LOSS WHEN USING SIMPLIFIED MPC FORMULATIONS

The Australian Year of the Farmer, 2012, is shaping up to be a positive year for Australian farmers. With improved seasonal conditions across much of the country, favourable commodity prices and a forecast that the value of farm exports is expected to rise some six percent, this is a good time to be an Australian farmer. And the story of Australian agriculture refl ects this. The growth in the farm sector over the past 20 years has consistently outperformed other sectors, and was a key reason Australia avoided a recession during the global fi nancial crisis. Agriculture has an enormous uptake of. | SLOVAK UNIVERSITY OF TECHNOLOGY IN BRATISLAVA FACULTY OF CHEMICAL AND FOOD TECHNOLOGY SYSTEMATIC METHOD FOR ANALYSIS OF PERFORMANCE LOSS WHEN USING SIMPLIFIED MPC FORMULATIONS DIPLOMA THESIS FCHPT-5414-28512 2010 Bc. Robert Taraba SLOVAK UNIVERSITY OF TECHNOLOGY IN BRATISLAVA FACULTY OF CHEMICAL AND FOOD TECHNOLOGY F c H p T SYSTEMATIC METHOD FOR ANALYSIS OF PERFORMANCE LOSS WHEN USING SIMPLIFIED MPC FORMULATIONS DIPLOMA THESIS FCHPT-5414-28512 Study programme Study field Supervisor Consultant Automation and Informatization in Chemistry and Food Industry Automation Ing. Michal Kvasnica PhD. MSc Henrik Manum Work place NTNU Trondheim Bratislava 2010 Bc. Robert Taraba Slovak University of Technology in Bratislava Faculty of Chemical and Food Technology Department of Information Engineering and Process Control Academic year 2009 2010 Reg. No. FCHPT-5414-28512 s T u F c H p T DIPLOMA THESIS TOPIC Student Be. Robert Taraba Student s ID 28512 Study programme Automation and Informatization in Chemistry and Food Industry Study field Automation Thesis supervisor Ing. Michal Kvasnica PhD. Consultant Henrik Manum Workplace NTNU Trondheim Topic Systematic Method for Analysis of Performance Loss when Using Simplified MPC Formulations Specification of Assignment Aim of this project is to analyze and compare system response using model predictive control MPC implemented on a full-order and reduced model. We would like to find the maximum worst-case difference between the filll-order controller and low-order controller which use full and reduced model respectively. There are several reasons why usage of the model reduction to reduce model state variables is important . the more states variables model contains the more complex the regulator must be. This fact is very important especially for explicit MPC. One of the greatest strength of MFC is the possibility of effective involving constraints on inputs state and output variables. As it was mentioned before the .

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