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prof. Ing. Miroslav Fikar, DrSc.
Identification number: 3374
University e-mail: miroslav.fikar [at] stuba.sk
 
Rektor - Vedenie STU (REKP REK)
Profesor DrSc. - Department of Information Engineering and Process Control (IIEAM FCFT)
Director - Department of Information Engineering and Process Control (IIEAM FCFT)
Chancellor - Slovak university of technology in Bratislava
 
External colleague - Institute of Applied Informatics, Automation and Mechatronics (MTF)

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Basic information

Basic information about a final thesis

Type of thesis: Dissertation thesis
Thesis title:Algorithms for Process Modelling and Fast Model Predictive Control
Written by (author): Ing. Alexander Szűcs, PhD.
Department: Institute of Information Engineering, Automation and Mathematics (FCFT)
Thesis supervisor: prof. Ing. Miroslav Fikar, DrSc.
Opponent 1:prof. Ing. Boris Rohaľ-Ilkiv, CSc.
Opponent 2:doc. Ing. Martin Hromčík, PhD.
Final thesis progress:Final thesis was successfully defended.


Additional information

Additional information about the final thesis follows. Click on the language link to display the information in the desired language.

Language of final thesis:English

Slovak        English

Title of the thesis:Algorithms for Process Modelling and Fast Model Predictive Control
Summary:This work aims to contribute to modelling and fast predictive control of processes. It can be divided into several topics. Process modelling is investigated and an effective approximation technique is de- scribed. It can be used to approximate an original non-linear process model as a hybrid system with piecewise affine dynamics. We discuss three different cases, how one can obtain the approximation of an arbitrary nonlinear function. The most trivial case assumes that the analytic form of the nonlinear term is already known. On the other hand, if only some set of input-output measurements are given, we employ a two-stage procedure to obtain the final approximation. This method aims to select the appropriate subset of basis functions and consecutively finding a proper linear combination of them. Once we possess the analytic formula of our approximated function, we can obtain the final PWA approximation by solving standard nonlinear programs. We show, that under mild assumptions, the task can be transformed into a series of one-dimensional problems. Finally, we demonstrate the efficiency of our technique on an illustrative example, involving a highly nonlinear reactor. The second part of the work deals with fast model predictive control. We investigate the problem of reduction of the amount of memory needed to describe explicit MPC solutions. The main idea of explicit MPC stems from pre-computation of the optimal control action for all possible initial conditions and subsequently storing them in a form of a look-up table. On one hand, this concept allows faster implementation, but on the other, requirements for memory storage increase too. In order to eliminate this drawback, we continue with a description of an effective, three-layer compression technique, allowing fast implementation on low-cost hardware platforms. This three-layer procedure first identifies similarities between polytopic regions in form of an affine transformation. If such a mapping exists, certain regions can be represented using less data. The second layer then applies data de-duplication to identify and remove repeating sequences of data. Regions are then described by integer pointers to such a unique set. Finally Huffman encoding is applied to compress such integer pointers using prefix-free variable-length bit encoding. The chapter ends with efficiency evaluation of the proposed technique on several, randomly generated feedback law examples. The final chapter is devoted to the so-called operator splitting methods, by means of one can solve convex optimisation problems very efficiently by simply decomposing the original possibly complex problem into a series of simple operations well known from linear algebra. Several algorithms and their range of applicability are presented.
Key words:explicit model predictive control, fast model predictive control, hybrid systems

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