A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control

Yizhou Fang, Antonios Armaou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper proposes a method to surpass the computational hurdles associated with nonlinear model predictive control (NMPC) via a formulation of advanced-step bilinear Carleman approximation-based MPC (ACMPC). This formulation is a combination of bilinear Carleman approximation (also known as Carleman linearization) based MPC (BCMPC) and advanced-step nonlinear MPC (asNMPC). It takes action based on a prediction of the future initial state, reduces the amount of computation by analytically predicting future system behavior and providing the sensitivity of the cost function to the manipulated variables as the search gradient. Through a linear approximation, it updates the pre-calculated optimal control signals as soon as the real system states are obtained on-line. Thus, it significantly reduces the required time of computing the optimal control signals on-line before they are injected into the system. Regulating an open loop unstable CSTR under disturbance is illustrated as a case-study example.

Original languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4027-4032
Number of pages6
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period12/12/1612/14/16

Fingerprint

Nonlinear Model Predictive Control
Model predictive control
Formulation
Optimal Control
Approximation
Linearization
Cost functions
Linear Approximation
Cost Function
Disturbance
Update
Unstable
Gradient
Computing
Prediction
Optimal control

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

Cite this

Fang, Y., & Armaou, A. (2016). A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 4027-4032). [7798879] (2016 IEEE 55th Conference on Decision and Control, CDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798879
Fang, Yizhou ; Armaou, Antonios. / A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control. 2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 4027-4032 (2016 IEEE 55th Conference on Decision and Control, CDC 2016).
@inproceedings{26ad1a37306141378a0081b96ac8bebb,
title = "A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control",
abstract = "This paper proposes a method to surpass the computational hurdles associated with nonlinear model predictive control (NMPC) via a formulation of advanced-step bilinear Carleman approximation-based MPC (ACMPC). This formulation is a combination of bilinear Carleman approximation (also known as Carleman linearization) based MPC (BCMPC) and advanced-step nonlinear MPC (asNMPC). It takes action based on a prediction of the future initial state, reduces the amount of computation by analytically predicting future system behavior and providing the sensitivity of the cost function to the manipulated variables as the search gradient. Through a linear approximation, it updates the pre-calculated optimal control signals as soon as the real system states are obtained on-line. Thus, it significantly reduces the required time of computing the optimal control signals on-line before they are injected into the system. Regulating an open loop unstable CSTR under disturbance is illustrated as a case-study example.",
author = "Yizhou Fang and Antonios Armaou",
year = "2016",
month = "12",
day = "27",
doi = "10.1109/CDC.2016.7798879",
language = "English (US)",
series = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4027--4032",
booktitle = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",
address = "United States",

}

Fang, Y & Armaou, A 2016, A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control. in 2016 IEEE 55th Conference on Decision and Control, CDC 2016., 7798879, 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 4027-4032, 55th IEEE Conference on Decision and Control, CDC 2016, Las Vegas, United States, 12/12/16. https://doi.org/10.1109/CDC.2016.7798879

A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control. / Fang, Yizhou; Armaou, Antonios.

2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 4027-4032 7798879 (2016 IEEE 55th Conference on Decision and Control, CDC 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control

AU - Fang, Yizhou

AU - Armaou, Antonios

PY - 2016/12/27

Y1 - 2016/12/27

N2 - This paper proposes a method to surpass the computational hurdles associated with nonlinear model predictive control (NMPC) via a formulation of advanced-step bilinear Carleman approximation-based MPC (ACMPC). This formulation is a combination of bilinear Carleman approximation (also known as Carleman linearization) based MPC (BCMPC) and advanced-step nonlinear MPC (asNMPC). It takes action based on a prediction of the future initial state, reduces the amount of computation by analytically predicting future system behavior and providing the sensitivity of the cost function to the manipulated variables as the search gradient. Through a linear approximation, it updates the pre-calculated optimal control signals as soon as the real system states are obtained on-line. Thus, it significantly reduces the required time of computing the optimal control signals on-line before they are injected into the system. Regulating an open loop unstable CSTR under disturbance is illustrated as a case-study example.

AB - This paper proposes a method to surpass the computational hurdles associated with nonlinear model predictive control (NMPC) via a formulation of advanced-step bilinear Carleman approximation-based MPC (ACMPC). This formulation is a combination of bilinear Carleman approximation (also known as Carleman linearization) based MPC (BCMPC) and advanced-step nonlinear MPC (asNMPC). It takes action based on a prediction of the future initial state, reduces the amount of computation by analytically predicting future system behavior and providing the sensitivity of the cost function to the manipulated variables as the search gradient. Through a linear approximation, it updates the pre-calculated optimal control signals as soon as the real system states are obtained on-line. Thus, it significantly reduces the required time of computing the optimal control signals on-line before they are injected into the system. Regulating an open loop unstable CSTR under disturbance is illustrated as a case-study example.

UR - http://www.scopus.com/inward/record.url?scp=85010792464&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85010792464&partnerID=8YFLogxK

U2 - 10.1109/CDC.2016.7798879

DO - 10.1109/CDC.2016.7798879

M3 - Conference contribution

AN - SCOPUS:85010792464

T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016

SP - 4027

EP - 4032

BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Fang Y, Armaou A. A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 4027-4032. 7798879. (2016 IEEE 55th Conference on Decision and Control, CDC 2016). https://doi.org/10.1109/CDC.2016.7798879