Perceptual control theory

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Related publications (29)

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

David Pascucci, Gijs Plomp

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise ...

2020

Open loop control theory algorithms for high-speed 3D MEMS optical switches

Matthias Imboden

There is a world-wide push to create the next-generation all-optical transmission and switching technologies for exascale data centers. In this paper we focus on the switching fabrics. Many different types of 2D architectures are being explored including M ...

2020

Grid-aware Distributed Model Predictive Control of Heterogeneous Resources in a Distribution Network: Theory and Experimental Validation

Mario Paolone, Fabrizio Sossan, Rahul Kumar Gupta

In this paper, we propose and experimentally validate a scheduling and control framework for distributed energy resources (DERs) that achieves to track a day-ahead dispatch plan of a distribution network hosting controllable and stochastic heterogeneous re ...

2020

Control theory in biology and medicine: Introduction to the special issue

Auke Ijspeert

From September-December 2017, the Mathematical Biosciences Institute at Ohio State University hosted a series of workshops on control theory in biology and medicine, including workshops on control and modulation of neuronal and motor systems, control of ce ...

SPRINGER2019

Kernel methods and Model predictive approaches for Learning and Control

Sanket Sanjay Diwale

Data-driven modeling and feedback control play a vital role in several application areas ranging from robotics, control theory, manufacturing to management of assets, financial portfolios and supply chains. Many such problems in one way or another are rela ...

EPFL2019

A data-driven approach to model-reference control with applications to particle accelerator power converters

Alireza Karimi, Achille Nicoletti, Michele Martino

A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can b ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Optimal Control of Part Load Vortex Rope in Francis Turbines

François Avellan, François Gallaire, Simon Pasche

The mitigation of the precessing vortex core developing in the draft tube of Francis turbines operating under part load conditions is crucial to increase the operation flexibility of these hydraulic machines to balance the massive power production of inter ...

ASME2019

Runaway electron beam stability and decay in COMPASS

Karin Söderström, Joan Decker, Gergely Papp

This paper presents two scenarios used for generation of a runaway electron (RE) beam in the COMPASS tokamak with a focus on the decay phase and control of the beam. The first scenario consists of massive gas injection of argon into the current ramp-up pha ...

IOP Publishing Ltd2019

Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks

Vassily Hatzimanikatis, Ljubisa Miskovic, Georgios Savoglidis, Milenko Tokic

Analysis of the dynamic and steady-state properties of biochemical networks hinges on information about the parameters of enzyme kinetics. The lack of experimental data characterizing enzyme activities and kinetics along with the associated uncertainties i ...

2019

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