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 Scopus citations

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

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'A Formulation of advanced-step bilinear Carleman approximation-based nonlinear model predictive control'. Together they form a unique fingerprint.

  • 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