Electrical Engineering and Systems Science - Systems and Control

ARRTOC: Adversarially Robust Real-Time Optimization and Control
Real-Time Optimization (RTO) plays a crucial role in the process operation hierarchy by determining optimal set-points for the …
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An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems
Most solutions to the inventory management problem assume a centralization of information that is incompatible with organisational …
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Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. The interest in …
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Distributional Reinforcement Learning for Scheduling of Chemical Production Processes
Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. …
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Data-driven distributionally robust MPC using the Wasserstein metric
A data-driven MPC scheme is proposed to safely control constrained stochastic linear systems using distributionally robust …
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