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Learning-based model predictive control: Toward safe learning in control
L Hewing, KP Wabersich, M Menner… - Annual Review of …, 2020 - annualreviews.org
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
[HTML][HTML] Behavioral theory for stochastic systems? A data-driven journey from Willems to Wiener and back again
T Faulwasser, R Ou, G Pan, P Schmitz… - Annual Reviews in …, 2023 - Elsevier
The fundamental lemma by Jan C. Willems and co-workers is deeply rooted in behavioral
systems theory and it has become one of the supporting pillars of the recent progress on …
systems theory and it has become one of the supporting pillars of the recent progress on …
On recurrent neural networks for learning-based control: recent results and ideas for future developments
F Bonassi, M Farina, J **e, R Scattolini - Journal of Process Control, 2022 - Elsevier
This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks
(RNN) in control design applications. The main families of RNN are considered, namely …
(RNN) in control design applications. The main families of RNN are considered, namely …
Automated verification and synthesis of stochastic hybrid systems: A survey
A Lavaei, S Soudjani, A Abate, M Zamani - Automatica, 2022 - Elsevier
Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …
framework describing many systems, from engineering to the life sciences: they enable the …
Robust control for dynamical systems with non-gaussian noise via formal abstractions
Controllers for dynamical systems that operate in safety-critical settings must account for
stochastic disturbances. Such disturbances are often modeled as process noise in a …
stochastic disturbances. Such disturbances are often modeled as process noise in a …
A survey of feedback particle filter and related controlled interacting particle systems (CIPS)
A Taghvaei, PG Mehta - Annual Reviews in Control, 2023 - Elsevier
In this survey, we describe controlled interacting particle systems (CIPS) to approximate the
solution of the optimal filtering and the optimal control problems. Part I of the survey is …
solution of the optimal filtering and the optimal control problems. Part I of the survey is …
[HTML][HTML] Long-term stochastic model predictive control for the energy management of hybrid electric vehicles using Pontryagin's minimum principle and scenario …
This paper presents a new approach to efficiently integrate long prediction horizons subject
to uncertainty into a stochastic model predictive control (MPC) framework for the energy …
to uncertainty into a stochastic model predictive control (MPC) framework for the energy …
Sample-based neural approximation approach for probabilistic constrained programs
This article introduces a neural approximation-based method for solving continuous
optimization problems with probabilistic constraints. After reformulating the probabilistic …
optimization problems with probabilistic constraints. After reformulating the probabilistic …
Decision-making under uncertainty: beyond probabilities: Challenges and perspectives
This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …
Non-convex scenario optimization
Scenario optimization is an approach to data-driven decision-making that has been
introduced some fifteen years ago and has ever since then grown fast. Its most remarkable …
introduced some fifteen years ago and has ever since then grown fast. Its most remarkable …