Manufacturing systems, power networks, transportation systems, road traffic networks, process plants, and other large-scale networked systems are often composed of multiple subsystems, with many embedded sensors and actuators, and characterised by complex dynamics and mutual influences such that local control decisions 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 in order to achieve a safe, efficient, and robust operation. Otherwise, serious disasters and malfunctions could occur (such as the breakdown of the power grid in North America and in Italy in 2003). To deal with these problems and to cope with the complexity of the control task, we propose to use a hierarchical control set-up in which the control tasks are distributed over time and space. In such a set-up, systems of supervisory and strategic functionality reside at higher levels, while at lower levels the single units, or local agents, must guarantee specific operational objectives. At any level, the local agents must negotiate their outcomes and requirements with lower and higher levels. We will develop methods for designing controllers for complex largescale systems based on such a hierarchical control framework. In particular, we propose to use Model Predictive Control (MPC), which has already proven its usefulness for control of smallscale systems, but which cannot yet be applied to large-scale systems due to computational, coordination, and communication problems. We will solve these issues and develop new MPC methods for large-scale networked systems, both under normal operation conditions, and in the presence of uncertainty and disturbances. We will perform both fundamental research and more application-oriented research in which the methods developed in the project are applied to case studies and benchmarks provided by the partners from industry.