Model-Predictive Cyber-Physical Networking

点击翻译按钮获取中文摘要

Phase 1 of MPCPN project has established the underlying design principles of model-predictive cyber-physical networking. Main result is a novel interface between the controlled physical subsystems and the communication network by which the network communicates its predicted decisions and delay trajectories to the subsystem plant controllers. This information can be used by controllers to greatly enhance their performance, and to foresee upcoming bottlenecks. The ambition of the Phase 2 is to further develop the framework and to consider systems with larger size, thus addressing the aspects of scaling, clustering, learning, etc., as well as more general topologies of system interconnection. In addition, the research in Phase 2 will strengthen the focus on the questions of how the optimization problems within the predictive network control and in the predictive subsystem controllers can be further intertwined to mutually respond and adapt to the predicted status of the other components. It is anticipated that an enhanced interleaving can further improve the system performance and can avoid to run into capacity limits. Phase 2 research focus is motivated by and will also be validated as well as demonstrated by two use cases, namely "interacting autonomous racing cars" and "swarms of moving robots".