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
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