Postdoc on Integrated Optimization-based and Learning-based Control for Large-scale Hybrid Systems

Updated: about 1 month ago
Deadline: 01 Jun 2021

In this postdoc project we will develop integrated optimization-based and learning-based control methods for large-scale hybrid systems — in particular network systems with piecewise affine (PWA) dynamics. More specifically, the aim is to develop several approaches to combine model predictive control (MPC) and reinforcement learning in a multi-agent control setting. This also includes setting up the overall multi-agent control framework with temporal and spatial divisions of the network, setting up multi-scale multi-resolution PWA models, and developing distributed control approaches that integrate optimization-based and learning-based control.

Applications include multi-modal transportation networks and smart multi-energy networks.

The postdoc project is part of the European ERC Advanced Grant project CLariNet – a novel control paradigm for large-scale hybrid networks. The goal of CLariNet is to create a completely new paradigm for control of large-scale networks with hybrid dynamics by bridging the gap between optimization-based control and learning-based control. The breakthrough idea is to bridge that gap by using piecewise affine models and to unite the optimality of optimization-based control with the on-line tractability of learning-based control.

The department Delft Center for Systems and Control (DCSC) of the faculty Mechanical, Maritime and Materials Engineering, coordinates the education and research activities in systems and control at Delft University of Technology. The Centers' research mission is to conduct fundamental research in systems dynamics and control, involving dynamic modelling, advanced control theory, optimisation and signal analysis. The research is motivated by advanced technology development in physical imaging systems, renewable energy, robotics and transportation systems.

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