Zobrazeno 1 - 10
of 8 109
pro vyhledávání: '"De Schutter A"'
Handling model mismatch is a common challenge in model-based controller design, particularly in model predictive control (MPC). While robust MPC is effective in managing uncertainties, its inherent conservatism often makes it less desirable in practi
Externí odkaz:
http://arxiv.org/abs/2412.10625
Unconstrained global optimisation aims to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processes as surrogate models, leverag
Externí odkaz:
http://arxiv.org/abs/2412.04882
The growing volume of available infrastructural monitoring data enables the development of powerful datadriven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodology to estim
Externí odkaz:
http://arxiv.org/abs/2412.02643
Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To add
Externí odkaz:
http://arxiv.org/abs/2412.01597
Online model predictive control (MPC) for piecewise affine (PWA) systems requires the online solution to an optimization problem that implicitly optimizes over the switching sequence of PWA regions, for which the computational burden can be prohibiti
Externí odkaz:
http://arxiv.org/abs/2412.00490
PieceWise Affine (PWA) approximations for nonlinear functions have been extensively used for tractable, computationally efficient control of nonlinear systems. However, reaching a desired approximation accuracy without prior information about the beh
Externí odkaz:
http://arxiv.org/abs/2410.16960
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 leverages a model of the system to optimize the control inputs, while
Externí odkaz:
http://arxiv.org/abs/2409.12789
This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to efficiently solve finite-horizon optimal control problems in mixed-logical dynamical systems. Optimization-based control of such systems with
Externí odkaz:
http://arxiv.org/abs/2409.11267
Nonlinear Programs (NLPs) are prevalent in optimization-based control of nonlinear systems. Solving general NLPs is computationally expensive, necessitating the development of fast hardware or tractable suboptimal approximations. This paper investiga
Externí odkaz:
http://arxiv.org/abs/2405.20387
Model mismatch often poses challenges in model-based controller design. This paper investigates model predictive control (MPC) of uncertain linear systems with input constraints, focusing on stability and closed-loop infinite-horizon performance. The
Externí odkaz:
http://arxiv.org/abs/2405.15552