28 citations · 77 across the 23 of their papers we have counts for
6 papers · 1 filter
Nonmyopic Global Optimisation via Approximate Dynamic Programming
Filippo Airaldi, Bart De Schutter, Azita Dabiri
Global optimisation to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically empl…
Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees
Samuel Mallick, Azita Dabiri, Bart De Schutter
Online model predictive control (MPC) for piecewise affine (PWA) systems requires the online solution to an optimization problem that implicitly optimizes over the switching sequen…
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control
Samuel Mallick, Filippo Airaldi, Azita Dabiri +2
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which…
Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
Caio Fabio Oliveira da Silva, Azita Dabiri, Bart De Schutter
This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to solve finite-horizon optimal control problems in mixed-logical dynamical…
Distributed Model Predictive Control for Piecewise Affine Systems Based on Switching ADMM
Samuel Mallick, Azita Dabiri, Bart De Schutter
This paper presents a novel approach for distributed model predictive control (MPC) for piecewise affine (PWA) systems. Existing approaches rely on solving mixed-integer optimizati…
A Comparison Benchmark for Distributed Hybrid MPC Control Methods: Distributed Vehicle Platooning
Samuel Mallick, Azita Dabiri, Bart De Schutter
Distributed model predictive control (MPC) is currently being investigated as a solution to the important control challenge presented by networks of hybrid dynamical systems. Howev…