This PhD 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 (i.e., systems with a combination of continuous dynamics and event-driven dynamics or switching) 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 aim of this PhD project is to develop integrated optimization-based and learning-based control method for constrained hybrid systems with performance guarantees. In particular, we will derive develop integrated optimization-based and learning-based control methods for piecewise affine (PWA) systems subject to input, output, and state constraints with performance guarantees in terms of optimality, constraint satisfaction, stability, safety, etc. For the cases where formal performance guarantees cannot be derived, probabilistic guarantees as used in, e.g., randomized algorithms will be derived instead. We will also consider issues such as computational complexity, error bounds, robustness, finite termination effects, etc. We will also investigate and characterize the various trade-offs (e.g., between accuracy/granularity of the system model and computational efficiency, and between allowed computation time and control performance/constraint violations).
This PhD project has a fundamental flavor. In addition, applications for the case studies include multi-modal transportation networks, smart multi-energy networks, and integrated intelligent transportation and energy systems.
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