to the Process Control & Informatics Unit

The research efforts of our automatic control laboratory focus on modeling, identification and control of industrial systems. There are several tools of quantitative analysis employed by our group, including optimization, statistics and computational intelligence and machine learning methods (neural networks, fuzzy logic, evolutionary algorithms, support vector machines). Special emphasis is given on the development of Model Predictive Control (MPC) methodologies and algorithms. Adaptive and self learning systems as well as robust H-infinity have also been designed. Recently our interests lie in the interface of control and optimization theory and applications to the optimal control of hybrid systems and dynamic decision making under uncertainty.