Projects / Programmes source: ARIS

Rapid prototyping of advanced control algorithms in industrial environment

Research activity

Code Science Field Subfield
2.06.03  Engineering sciences and technologies  Systems and cybernetics  Methods and tools for design and implementation of control systems 

Code Science Field
T125  Technological sciences  Automation, robotics, control engineering 
P175  Natural sciences and mathematics  Informatics, systems theory 
industrial control systems, advanced control algorithms, rapid prototyping
Evaluation (rules)
source: COBISS
Researchers (5)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  16161  PhD Samo Gerkšič  Systems and cybernetics  Researcher  2007 - 2009  132 
2.  20241  PhD Gregor Kandare  Systems and cybernetics  Researcher  2007 - 2009  43 
3.  25655  PhD Boštjan Pregelj  Systems and cybernetics  Junior researcher  2007 - 2009  123 
4.  02830  PhD Stanislav Strmčnik  Systems and cybernetics  Researcher  2007 - 2009  488 
5.  12342  PhD Damir Vrančić  Systems and cybernetics  Head  2007 - 2009  338 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  89,990 
The efficiency of process operation in the field of process industry may be improved by using advanced control methods. The advanced methods are still not widely used in industrial practice due to time-consuming implementation and difficult performance assessment. The aim of the project is the development of a rapid prototyping environment for a set of advanced control methods, including the methodology for experimentation, software tools for rapid prototyping of control methods, and a performance assessment procedure. Special emphasis of the project will be on industrial case studies.
Significance for science
The project generated many new ideas and solutions. Among the most significant are method for improving control performance for various types of controllers (PID, predictive and multivariable), innovative tuning method for integrating processes and a new equalisation tuning method. Method for improving control performance is based on modification of magnitude optimum criterion. Namely, the original magnitude optimum criterion optimises tracking instead of control performance. By suitable modification of the criterion, the control performance was considerably improved, especially for dominantly lower-order processes. The method can be used on process models or directly on measurements in time-domain. A new method for integrating processes is based on changing the controller structure, since, for such processes, the amplitude optimum criterion cannot be directly applied on “schoolbook” controllers. This tuning method additionally extends the palette of processes which can be controlled by MOMI (magnitude optimum multiple integration) method. Similar, as for the previous method, it can be used on process models or on measurements in time-domain. The new “equalisation” method is additionally simplifying the experiment procedure and reducing controller output activity. The main goal was to equalise the open-loop and the closed-loop process response. The main characteristics of the method are simple experiment (where operator implicitly defines desired closed-loop response) and low sensitivity to disturbances, noise and nonlinearity in the process. The continuation of research and publications are expected in all three areas.
Significance for the country
The main goal of the project is development of rapid prototyping environment for advanced control methods. The selected methods are predictive controllers (Smith predictors and PFC), feed-forward control (compensation of disturbances), and multivariable controllers. Selected controllers including classical I, P, PI and PID controllers (which are also part of rapid prototyping environment) are covering vast majority of continuous control systems in industry. The main advantages of the program package are reduced time for tuning and testing advanced controllers and therefore personnel costs. Expected are: - reduced time for selection of appropriate controller type in control loop - reduced time for tuning controller parameters - improved control quality and therefore production quality - reduction of maintenence costs and time due to better control quality Reduction of production costs is expected to be up to 15% (depends on the current optimisation level).
Most important scientific results Annual report 2008, final report, complete report on dLib.si
Most important socioeconomically and culturally relevant results Annual report 2008, final report, complete report on dLib.si
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