Our changing society is demanding smart and intelligent engineering solutions for major technological challenges, where resources and energy, as well as human resources, have to be used in the most efficient way, to arrive at sustainable and smart solutions for, e.g.
• industrial production (smart industries; manufacturing and processing)
• energy generation and distribution (integrated generation, smart grids)
• transportation systems (smart cars, drones, planes, next-generation infrastructures)
• urban environments (smart cities, smart buildings)
One of the common characteristics in these challenges is the control and optimized operation of highly complex, large-scale, multi-physics and interacting dynamic systems. In these automated operations, computation, (internet-) communication and control are integrated in efficiently operating plug and play intelligent automation systems, that warrant flexibility, robustness, stability and performance. The result is automation systems that optimally optimally manage and control cyber-physical systems of systems.
In this development dynamic models play a key role. They serve important purposes of simulation, diagnosis, and learning/understanding the characteristics of processes in their behavior over time, and they have a paramount role in (model-based) simulation, diagnostics, measurement, control and optimization. In view of the technological developments, the models will have to reflect the large-scale interconnected and networked character of the dynamic processes of study.
Models can be built either on the basis of first-principles relations or on the basis of experimental data, or a combination of both. Data-driven modelling is particularly important for (a) effectively incorporating the emergent behavior of systems, (b) quantifying and minimizing the effect of uncertainties, (c) adapting to time-varying behavior, (d) accurately estimating the parameters in first-principles models and (e) possibly avoiding the time-consuming task of first principles modelling. Therefore effective data-driven modelling tools for dynamic networks are essential ingredients for operating and controlling many of our future engineering systems. Since a comprehensive theory for data-driven modelling of (parts of) dynamic networks is lacking, the overall objective of this project is to
develop a comprehensive theory for the data-driven modelling of dynamic networks, that can address (a) the identification of dynamics and interconnection structure (topology) of local parts of the network, (b) aspects and optimal choices of sensor and actuator placements and of experiment design (c) incorporation of prior (partial) knowledge on network topology and local network dynamics and (d) the properties of identified (local) models that are relevant for model-based distributed control.
The conclusion of the project is that a comprehensive theory has been developed for data-driven modeling in dynamic networks, both for identifying local modules and full networks. This theory involves exploitation of the fundamental concept of network identifiability, and the development of constructive methods for selecting nodes to be measured (sensor placement) and excitations to be added (actuator placement). Effective and scalable algorithms have been developed for the actual identification of the network modules. Extensions have been made towards nondirected networks that are based on physical couplings between subsystems, and towards the use of networked identified models for distributed control. The prime software tools have been collected in a Matlab Toolbox.
