Nonstochastic information flows in networked dynamical systems

反馈控制是制造、车辆和能源系统的关键要素，需要在安全和任务关键环境中保证严格的性能界限。当这些控制系统通过通信网络实现时，流经它们的信息量成为性能的关键决定因素。然而，非概率控制目标使标准信息理论不适用。该项目旨在开发一种新的非随机信息理论，以分析和设计服从最坏情况性能限制的网络动态系统。这将为分布式控制、过滤和因果关系推理产生鲁棒的、无概率的算法。

Feedback control is a crucial element of manufacturing, vehicular and energy systems, and is needed to guarantee hard performance bounds in safety- and mission-critical environments. When these control systems are implemented over communication networks, the amount of information flowing through them becomes a critical determinant of performance. However, the nonprobabilistic control objectives make standard information theory inapplicable. This project aims to develop a novel, nonstochastic theory of information in order to analyse and design networked dynamical systems that obey worst-case performance limits. This will yield robust, probability-free algorithms for distributed control, filtering and causality inference.