Research GroupsNetworked Control Systems and Information Fusion

Networked Control Systems and Information Fusion

 

CYBER-PHYSICAL SYSTEMS

Technological advances in computing, communication and control have paved the way for a new generation of engineering systems, called cyber-physical systems (CPS) where physical and software components are deeply intertwined. Application areas are immense, ranging from autonomous driving to energy systems and customised manufacturing for the Industry 4.0. A main challenge of CPS is that they feature tight interactions between the continuous dynamics of physical systems and the discrete dynamics of cyber components. Our research focuses on modelling, analysis, monitoring and control of CPS, combining tools from control theory with tools that are typical of computer and network science such as automata and graph theory.

NETWORKED CONTROL AND ESTIMATION

Networked control systems (NCS) are systems where monitoring and control tasks are carried out using a communication network. The concept of NCS is extremely appealing for industry, especially for wireless automation, but it raises many theoretical and practical challenges due to the fact that communication networks introduce several forms of uncertainty such as transmission delay and packet dropouts. Our research focuses on the design of monitoring and control systems that are resilient against network imperfections, as well as on energy-aware transmission logics like event- and self-triggered control.

DATA FUSION

Data fusion aims to combine information from multiple, often heterogeneous, data sources so as to provide better situation awareness that any individual source could give. Fundamental theoretical issues in data fusion are to avoid double counting of information and at the same time allow the useful integration of complementary information. Relevant applications of data fusion that are of interest to our group concern distributed monitoring/surveillance via sensor networks and cooperation of autonomous agents (e.g. self-driving vehicles or mobile robots).

SENSOR NETWORKS

Sensor networks consist of the interconnection of multiple nodes with sensing, communication, processing and, sometimes, motion capabilities. Such networks have nowadays wide application in various domains including environmental/industrial/health monitoring, intelligent transportation systems, smart cities/buildings, ground/air/maritime surveillance, etc.. The main challenge of our work on this topic is to develop distributed estimation algorithms that are scalable with respect to the network size, perform as close as possible to the centralized estimator, and are communication/energy efficient.

ADAPTIVE AND LEARNING SYSTEMS

Adaptation refers to the ability of a system to adjust to different conditions within its environment. Modern engineering systems are complex and must be flexible and capable of learning and adapting in the face of varying operating conditions so as to optimize performance. The activity of our research group in the broad area of adaptive and learning systems ranges from application of machine learning techniques in identification, estimation, and control to real-time control reconfiguration and data-driven control design.