Hangos, Katalin

Current Positions

Research Professor
  Process Control Research Group
  Computer and Automation Research Institute
  Hungarian Academy of Sciences
  H-1518 Budapest
  P.O. Box 63., Kende u. 13-17.
  Tel: (36) 1 279 6101 (office)
  Fax: (36) 1 466 7503
  E-mail: hangos@sztaki.hu


Professor, Head of Department
  Department of Electrical Engineering and Information Systems
  Faculty of Information Technology
  University of Pannnonia
  H-8200 Veszprém
  Egyetem u. 10
  Tel: (36) 88 624 607 (office)
  E-mail: hangos.katalin@virt.uni-pannon.hu

Research
Publication list (searchable!)
Pulication list (pdf)
Curriculum Vitae (English)
Curriculum Vitae (Hungarian)
Interview in English
Informal CV in Hungarian
Self-intro lecture in Hungarian

 

Research

The following main directions are followed.

Hangos, K. M. and I. T. Cameron:
Process Modelling and Model Analysis.
Academic Press, London, pp. 1-543. (2001)

Hangos, K. M., J. Bokor and G. Szederkényi:
Analysis and Control of Nonlinear Process Systems.
Springer-Verlag, London, pp. 1-308 (2004)

Hangos, K. M., R. Lakner and M. Gerzson:
Intelligent Control Systems: An Introduction with Examples.
Kluwer Academic Publisher, pp. 1-301 (2001)

Formal methods of computer science and artificial intelligence are applied to construct, verify, analyze and simplify process models in a rigorous and automated way. In particular, a formal method for defining syntax and semantics of process models has been proposed. The effect of algebraic, model building and model simplification transformations has been analyzed on the computational and dynamic properties of process models. The approaches and methods of model reduction have also been investigated and applied to define the notion of minimal process models and analyze their properties. The integration frameworks of multiscale process models have been classified, and the newly introduced model metrics have been used to evaluate the computational properties of multi-scale models. Intelligent diagnostic systems that utilize the structure of multi-scale process models have also been developed.
The laws of thermodynamics have been used for analyzing structural stability of process systems. An entropy-based storage function has been found for nonlinear process systems, which enables to analyze their passivity. A Hamiltonian description of process models has also been developed which has been used for constructing stabilizing and loop-shaping controllers for nonlinear process systems. Quasi-polynomial system models being generalized Lotka-Volterra systems have been used for a wide class of nonlinear process systems for stability analysis and controller design purposes. It was shown that a Lotka-Volterra system is globally stable with a formerly known entropy-like Lyapunov function candidate if and only if there exists a local dissipative-Hamiltonian description of the system in the neighbourhood of the equilibrium point with a quadratic Hamiltonian function. Furthermore, a method for the estimation of guaranteed quadratic stability neighborhood has been developed using linear matrix inequalities. Globally stabilizing feedback controller design was shown to be solved by using bilinear matrix inequalities.

A graph-theoretical method has been developed for process model structure driven design of stabilizing and disturbance rejective distributed controller structures. It was shown that the set of optimal SISO controllers can be determined in polynomial time. Furthermore, a polynomial algorithm has been proposed for integrated process and control structure design which has been extended to the fault-tolerant case, too.
Constraint type qualitative models have been used for model-based generation of operating procedures for a distillation column. Coloured Petri nets (CPNs) have been proposed for model-based verification of such operating procedures. Furthermore, an intelligent event-driven diagnostic system has been developed which is based on the CPN model of the process system and able to refine its model using the observed events.