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Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …
including the reliable integration of renewable energy sources into power grids, safe …
[HTML][HTML] Multi-zone building control with thermal comfort constraints under disjunctive uncertainty using data-driven robust model predictive control
This paper proposes a novel data-driven robust model predictive control (MPC) framework
for a multi-zone building considering thermal comfort and uncertain weather forecast errors …
for a multi-zone building considering thermal comfort and uncertain weather forecast errors …
Data-driven distributed and localized model predictive control
Motivated by large-scale but computationally constrained settings, eg, the Internet of Things,
we present a novel data-driven distributed control algorithm that is synthesized directly from …
we present a novel data-driven distributed control algorithm that is synthesized directly from …
Learning for predictive control: A dual gaussian process approach
Y Liu, P Wang, R Tóth - ar** based distributed MPC approach for linear systems with unknown input coupling
L Yang, A Ma, D Li - 2022 13th Asian Control Conference …, 2022 - ieeexplore.ieee.org
This paper presents an input-map** based distributed MPC approach for linear systems,
where each subsystem is coupled with the inputs of other subsystems, and the coupling …
where each subsystem is coupled with the inputs of other subsystems, and the coupling …