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In all automation systems the task of decision-making and control is the most challenging part and the part where human interaction as operators are most common. However, as optimization, more difficult control challenges and higher expectations evolve, also control and decision-making needs to be automated. Systems with multiple inputs and multiple outputs interacting dynamically need to be controlled. For such control tasks, the system description is usually based on the identification procedures. The course studies various approaches to structuring the problem through control structures. A special emphasis is put on understanding, designing and implementing model-predictive control. In that context, both linear models and especially non-linear models and processes are studied. The course also aims at giving a fundament to continued work on these topics on an applied research level.
Lectures, discussions and laboratory-based project work. Fundaments for research projects.