After receiving my diploma in electrical engineering from TU Dresden, I joined the Max Planck Institute for Intelligent Systems in 2017. My current research focuses on control strategies for wireless cyber-physical systems. This involves a tight integration of communication and control system, taking into account network imperfections such as constrained bandwidth, transport delays, and packet drops. To solve these challenges I look at classical control methods as well as at leveraging machine learning methods.
The ability to learn is an essential aspect of future intelligent systems that are facing uncertain environments. However, the process of learning a new model or behavior often does not come for free, but involves a certain cost. For example, gathering informative data can be challenging due to physical limitations, or updating mode...
Future intelligent systems such as autonomous robots, self-driving cars, or manufacturing systems will be connected over communication networks. Facilitated by the network, the individual agents can coordinate their actions and thus achieve functionality exceeding the individual unit (for example, driving in formation or collaborati...
Cyber-physical systems (CPS) tightly integrate physical processes with computing and communication, thus, enabling emerging applications such as coordinated flight of autonomous vehicles or controlling factory automation machinery over wireless networks. The adoption of wireless technology offers unprecedented flexibility in sharing...
In Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, 10th ACM/IEEE International Conference on Cyber-Physical Systems, April 2019 (inproceedings) Accepted
Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals below 100 ms. Low-power wireless is preferred for its flexibility, low cost, and small form factor, especially if the devices support multi-hop communication. Thus far, however, closed-loop control over multi-hop low-power wireless has only been demonstrated for update intervals on the order of multiple seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance such as jitter or packet loss, and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for linear dynamic systems. Using experiments on a testbed with multiple cart-pole systems, we are the first to demonstrate the feasibility and to assess the performance of closed-loop control and coordination over multi-hop low-power wireless for update intervals from 20 ms to 50 ms.
IEEE Internet of Things Journal, 2019 (article) Accepted
The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems