Advanced Control Methods (ADCON)

Professor Urpo Kortela, Systems Engineering Laboratory, Department of Process and Environmental Engineering, University of Oulu
urpo.kortelaoulu.fi
http://www.infotech.oulu.fi/adcon

Background and Mission

Process control is an efficient means of improving the operation of a process, the productivity of a plant, and the quality of products. In process engineering, even a small improvement in the operation of a process can have great economic and environmental influences.

In order to develop better - fast, accurate, robust and fault tolerant - process control, model-based modern control methods and efficient adaptive and learning techniques are required. During recent years, the developments in these fields have introduced new tools for use in control engineering: neuro-fuzzy systems, guided random search techniques, predictive control, etc. In process engineering, these new tools have found applications in non-linear process modelling and control, plant optimisation, monitoring, scheduling, fault diagnosis, etc. The application area of control engineering methods can be extended also to systems beyond the realm of traditional process engineering.

Although the above mentioned techniques have been the subject of intensive scientific research, there are still many theoretical and practical problems to be investigated. A clear tendency and need for real industrial applications of advanced control methods can be seen. The gap between theory and practice needs to be narrowed. As there is no universal best solution that would apply to all control problems, knowledge of different model structures and alternative learning methods can be used to offer advice on the choice of the most suitable technique for a specific application. The results of the synthesis are readily applicable in industry.

The Infotech research group carrying the name "Advanced Control Methods (ADCON)" has been formed from the Systems Engineering Laboratory in the Department of Process and Environmental Engineering at the University of Oulu.

The scientific research of the Infotech Oulu research group in Advanced Control Methods prospered in 2004. There was a number of on-going research and training projects. Several projects found financial support from the Academy of Finland. In basic research work, the group has been concerned with the development of novel structures and algorithms for identification of non-linear systems, as well as robust, predictive, adaptive, constrained and learning control of industrial processes. The ADCON project unit of Infotech Oulu has several applied projects involving close collaboration with industry. The results from the academic research have been extensively used in applied projects.

The Advanced Control Methods research group of Infotech Oulu has a wide variety of research programmes considering the development and application of modern information technology. In the future, the research will continue to be active, directed towards multivariable and learning systems, model-based modern control design, and fault diagnosis. Collaboration with international partners will be emphasised, along with active publishing in scientifically recognised forums. A major goal is to decrease the gap between control and systems theory and industrial practice.

Personnel

External Funding

Doctoral Theses

Hätönen, Jari (2004) Issues of algebra and optimality in Iterative Learning Control. Acta Universitatis Ouluensis C 205.

Selected Publications

Hätönen J & Ylinen R (2003) Polynomial systems theory applied to the analysis and design of multidimensional systems. International Journal of Applied Mathematics and Computer Science 13(1):15-27.

Ikonen E & Najim K (2004) Process identification using distributed Wiener logic processors. Differential Equations & Dynamical Systems 12(1-2):171-194.

Najim K, Del Moral P & Ikonen E (2004) An improved version of the McMurtry-Fu reinforcement learning scheme. International Journal of Systems Science 34(1):37-47.

Najim K, Ikonen E & Aït-Kadi D (2004) Stochastic processes: estimation, optimization and analysis. Kogan Page Science, London, U.K., 332 p.

Najim K, Poznyak A & Ikonen E (2004) Optimization based on a team of automata with binary outputs. Automatica 40(8):1349-1359.