Project description
An integrated control approach for large-scale networks with hybrid dynamics
Controlling large-scale networks with hybrid dynamics is very complex owing to the large size of the networks, the presence of disturbances and the limited computation time. Examples of such networks include road, railway, electricity, gas and water networks. Hybrid dynamics refers to a combination of continuous dynamics, mode switches and topology changes. State-of-the-art control methods are not suited for these large-scale networks as they either suffer from computational tractability issues or impose additional restrictions, resulting in a significantly reduced performance. To address this problem, the EU-funded CLariNet project will develop a new online control paradigm for large-scale networks with hybrid dynamics using a combination of optimisation-based and learning-based control.
Objective
I will develop efficient on-line control methods for large-scale Networks with Hybrid Dynamics (NHDs) in the presence of uncertainties, where hybrid dynamics refers to a combination of continuous dynamics, mode switches, and/or topology changes. This topic is one of the core fundamental open problems in the field of systems and control. It is also important from a societal point of view as today’s society depends heavily on the reliable and efficient operation of road, railway, electricity, gas, and water networks, all of which are examples of large-scale NHDs.
Control of large-scale NHDs is a very complex problem due to the large size of the networks, the presence of disturbances, and the hybrid dynamics, while a limited computation time is available. State-of-the-art control methods are not suited for large-scale NHDs as they either suffer from computational tractability issues or impose additional restrictions, resulting in a significantly reduced performance.
To address this problem, I will create a new on-line control paradigm for large-scale NHDs based on an innovative integration of multi-agent optimization-based and learning-based control, allowing to unite the optimality of optimization-based control with the on-line tractability of learning-based control. I will bridge the gap between optimization-based and learning-based control for NHDs through the use of multi-scale multi-resolution piecewise affine models, explicit consideration of the graph structure of the network, the unique knowledge and experience I have in both optimization-based control and learning-based decision making, and an interdisciplinary integration of approaches from systems and control, computer science, and optimization.
This will result in systematic, very reliable, highly scalable, high-performance on-line control methods for large-scale NHDs. I will demonstrate their feasibility, benefits, and impact for green multi-modal transportation networks and smart multi-energy networks.
Fields of science
Programme(s)
Topic(s)
Funding Scheme
ERC-ADG - Advanced GrantHost institution
2628 CN Delft
Netherlands