Scale-free Control for Complex Physical Network Systems
(See all publications and further details at Scale-FreeBack)
Scale-FreeBack is an ERC Advanced Grant awarded to Carlos Canudas-de-Wit, Director of Research at the National Center for Scientific Research, (CNRS), covering the period Sept. 2016-2022. The ERC is hosted by the CNRS, a government-funded research organization, under the aegis of the French Ministry of Research. The project is conducted at the GIPSA-Lab at Grenoble (a CNRS research unit associated with the University of Grenoble-Alpes, UGA) working in close collaboration with the NeCS group (a joint CNRS-INRIA team).
Motivation. Scale-FreeBack has been designed from the observation that systems have become far more complex than the analytical tools available for managing them. Complex systems have many components, communicate with each other, have self-decision-making mechanisms, share an enormous amount of information, and form networks. Research in control systems has challenged some of these features, but not in a very concerted way. Examples are intelligent traffic systems benefiting from many technical innovations; mobile phones, radars, cameras and magnetometers can be used to measure traffic flow and provide large sets of valuable data. Vehicles can communicate with the network infrastructure, as well as each other. However, these huge technological advances have not been used to the full so far: traffic lights are far from functioning optimally and traffic management systems do not always prevent the occurrence of congestions. Scale-FreeBack is a project with ambitious and innovative theoretical goals, which were adopted in view of the new opportunities presented by the latest large-scale sensing technologies.
Challengers & Objectives. The overall aim of Scale-FreeBack is to develop holistic scale-free control methods of controlling complex network systems in the widest sense, and to set the foundations for a new control theory dealing with complex physical networks with an arbitrary size. Scale-FreeBack envisions devising a complete, coherent design approach ensuring the scalability of the whole chain (modelling, observation, and control). It is also expected to find specific breakthrough solutions to the problems involved in managing and monitoring large-scale road traffic networks. Field tests and other realistic simulations to validate the theory will be performed using the equipment available at the Grenoble Traffic Lab center (see GTL), and a microscopic traffic simulator replicating the full complexity of the Grenoble urban network.
In connections with these objectives, the project addresses the following theoretical questions:
- When and how can a complex large-scale (homogenous) network system be represented by a scale-free model having the requisite controllability/observability properties?
- How can the internal states of a scale-free network system be monitored and estimated by using information originating from sources of various kinds?
- How will it be possible to design scale-free control algorithms and make them resilient to changes of scale, node/link failures/disconnections/attacks?
Research objectives. Scale-FreeBack is organized into 4 main topics:
1. Scale-free dynamic network modelling & analysis. Break down system network complexity by finding the appropriate level of scale aggregation, while imposing the control and observation model properties required.
- New control metrics for evolutionary scale-free models
- On-line partitioning algorithms for evolutionary scale-free models
- Scale-free models for large urban networks
2. Optimal sensor placement and state-state estimation over dynamic scale-free networks. Develop optimal sensor positioning strategies and state-state estimation algorithms for evolutionary dynamical networks based on information of various kinds.
- Optimal sensor placement in heterogeneous large scale networks
- State estimation for evolutionary scale-free models
- Vehicle-density monitoring and state estimation for large urban networks
3. Novel control methods for scale-free network systems. Design new scale-free model-based control methods for complex physical networks and ensure that they function robustly and resiliently.
- Optimal games and robust control for scale-free networks
- Resilient control in scale-free networks
- Traffic control in large urban networks
4. Proof-of-Concept: large-scale road networks. Validate the some of the main theoretical findings in real ring-road tests using field data, and/or in a large-scale traffic micro-simulator.
- Deployment of the supplementary technology required for the GTL
- Improvement and calibration of the micro-simulator
- Field simulations and validation tests