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Hierarchical and distributed model predictive control
of large-scale systems
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Manufacturing systems, traffic networks, process plants, electricity networks are often composed of multiple subsystems, characterised by complex dynamics and mutual influences such that local control decisions may have long-range effects throughout the system. This results in a huge number of problems that must be tackled for the design of an overall control system. Improper control and insufficient coordination of these large-scale systems could result in a hugely suboptimal performance or in serious malfunctions or disasters.
Current centralised control design methods cannot deal with large-scale systems due to the tremendous computational complexity of the centralised control task and due to scalability issues and communication bandwidth limitations, all of which make on-line, real-time centralised control infeasible.
The main objective of the project is therefore to develop new and efficient methods and algorithms for distributed and hierarchical model-based predictive control of large-scale, complex, networked systems with embedded controllers, and to validate them in several significant applications. We will design these methods to be much more robust than existing methods in the presence of large disturbances, and component, subsystem, or network failures, with a performance approaching that of a fully centralised methodology. The resulting control methods can be applied in a wide range of application fields such as power generation and transmission networks, chemical process plants, manufacturing systems, road networks, railway networks, flood and water management systems, and large-scale logistic systems.