An asymptotically optimal NMPC core for multi degrees of freedom rigid bodies

Chyon Hae Kim, Shimon Sugawara, Shigeki Sugano

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

Abstract

We address the applicability of nonlinear model predictive control (NMPC) to rigid bodies with multi degrees of freedom. These systems have complicated nonlinear motion equations that cause large computational time for NMPC.We propose an asymptotically optimal NMPC core based on the uniform state sampling concept. We examined the optimality and the computational cost of the proposed core using double and triple inverted pendulum models. The result showed that the proposed core calculates sub-time-optimal motions with 100 and 1,000 times faster than the competing cores with maintaining the same optimality. We applied the proposed core to a sixth inverted pendulum model. The result showed that the proposed NMPC core is able to calculate a sub-time-optimal motion of the model.

Original languageEnglish
Title of host publicationNMPC'12 - IFAC Conference on Nonlinear Model Predictive Control, Conference Program
Pages207-212
Number of pages6
EditionPART 1
DOIs
Publication statusPublished - 2012 Oct 9
Event4th IFAC Conference on Nonlinear Model Predictive Control, NMPC'12 - Noordwijkerhout, Netherlands
Duration: 2012 Aug 232012 Aug 27

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
Volume4
ISSN (Print)1474-6670

Conference

Conference4th IFAC Conference on Nonlinear Model Predictive Control, NMPC'12
CountryNetherlands
CityNoordwijkerhout
Period12/8/2312/8/27

ASJC Scopus subject areas

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'An asymptotically optimal NMPC core for multi degrees of freedom rigid bodies'. Together they form a unique fingerprint.

  • Cite this

    Kim, C. H., Sugawara, S., & Sugano, S. (2012). An asymptotically optimal NMPC core for multi degrees of freedom rigid bodies. In NMPC'12 - IFAC Conference on Nonlinear Model Predictive Control, Conference Program (PART 1 ed., pp. 207-212). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 4, No. PART 1). https://doi.org/10.3182/20120823-5-NL-3013.00031