Výsledky vyhledávání - "nonlinear model based control"

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Output feedback stochastic nonlinear model predictive control of a polymerization batch process

Autor: Eric Bradford, Marcus Reble, Lars Imsland

Publikováno v: ECC
2019 18th European Control Conference (ECC)

Nonlinear model predictive control (NMPC) is one of the few methods that can handle multivariate nonlinear control problems while accounting for process constraints. Many dynamic models are however affected by significant stochastic uncertainties tha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf4fe2a297248280a70b4dd1e5f4c487
https://doi.org/10.23919/ecc.2019.8795684

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Stochastic Nonlinear Model Predictive Control Using Gaussian Processes

Autor: Lars Imsland, Eric Bradford

Publikováno v: ECC
2018 European Control Conference (ECC) 1027-1034

Model predictive control is a popular control approach for multivariable systems with important process constraints. The presence of significant stochastic uncertainties can however lead to closed-loop performance and infeasibility issues. A remedy i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7eca77b5d730cc443fc13c0147b336a4
https://doi.org/10.5281/zenodo.1407532

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Stochastic NMPC of Batch Processes Using Parameterized Control Policies

Autor: Bradford, Eric, Imsland, Lars

Nonlinear model predictive control (NMPC) is an effective method for optimal operation of batch processes. Most dynamic models however contain significant uncertainties. It is therefore important to take these uncertainties into account in the formul

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5e35f08a0ef6913e64df9d843e74f66

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Optimal control via reinforcement learning with symbolic policy approximation

Autor: Robert Babuska, Eduard Alibekov, Jiří Kubalík

Publikováno v: IFAC-PapersOnLine, 50(1)

Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e502df56e5792d3230ab049d031ecebc
http://resolver.tudelft.nl/uuid:5679b1a1-9a8a-42e4-b8d8-012296cea0a0

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Polynomial chaos explicit solution of the optimal control problem in model predictive control

Autor: F. De Belie, Tom Lefebvre, Guillaume Crevecoeur

Publikováno v: 2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)
AIM

A difficulty still hindering the widespread application of Model Predictive Control (MPC) methodologies, remains the computational burden that is related to solving the associated Optimal Control (OC) problem for every control period. In contrast to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6925e0748079ee6df028a45c90b8f4e9
https://biblio.ugent.be/publication/8529774/file/8529776

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A field-programmable gate array implementation of a real-time nonlinear

Autor: Iplikci, S, Bahtiyar, B

This paper presents the very first field-programmable gate array (FPGA) implementation of the Runge-Kutta model predictive control (RKMPC) mechanism to the real-time experimental electromagnetic levitation system, which is an unstable nonlinear conti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3566::04390b031c2b1bcc8ebf706187c34346
http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/23195

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