500 citations · 2.4k across the 118 of their papers we have counts for
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Experimental Validation for Distributed Control of Energy Hubs
Varsha Behrunani, Philipp Heer, John Lygeros
As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-p…
Efficient safe learning for controller tuning with experimental validation
Marta Zagorowska, Christopher König, Hanlin Yu +3
Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms…
Parallel Model Predictive Control for Deterministic Systems
Yuchao Li, Aren Karapetyan, Niklas Schmid +3
In this note, we consider infinite horizon optimal control problems with deterministic systems. Since exact solutions to these problems are often intractable, we propose a parallel…
Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems
Samuel Balula, Efe C. Balta, Dominic Liao-McPherson +2
Learning-based control methods for industrial processes leverage the repetitive nature of the underlying process to learn optimal inputs for the system. While many works focus on l…
Degradation-aware data-enabled predictive control of energy hubs
Varsha Behrunani, Marta Zagorowska, Mathias Hudoba de Badyn +3
Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy…
On the Guarantees of Minimizing Regret in Receding Horizon
Andrea Martin, Luca Furieri, Florian Dörfler +2
Towards bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes th…