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Hierarchical and distributed model predictive control
of large-scale systems
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The new structured and tractable control design methods for large-scale systems we will develop will be based on a hierarchical, distributed model-based control approach in which a multi-level model of the system is used to determine optimal control signals, and in which the controllers operate along several time scales and at different control levels. We will develop both the necessary new theory and the corresponding control design methods for using a combination and integration of techniques from computer science, operations research, optimisation, and control engineering. This will result in systematic approaches that outperform existing control strategies, which are often case-dependent and based on heuristics and simplifications. In order to adapt to dynamic changes in the demands, the structure of the system, and the environment, adaptive on-line control is required. Therefore, we will use a model-based approach, which will allow the controller to predict the effects of future control actions on the system, and to take external inputs and demands into account.

We will also take various aspects of large-scale complex systems into account that are often not considered in current control methods such as their hybrid nature, the variety of – often conflicting – objectives and constraints that play a role, and the interactions between the different time scales of the system dynamics and the control actions. This implies that we need a multi-level, multi-objective, distributed control approach. Other important aspects of our approach are communication of information between subsystems, and cooperation between their controllers towards a common goal. In addition to performing fundamental research on hierarchical and distributed control of large-scale systems we also concentrate on applications, in particular on combined cycle plants (CCP), hydro-power valley operations, and water capture systems.