Dynamic gain-scheduled control and extended linearisation: extensions, explicit formulae and stability

Weiwei Yang, Nadjib Hammoudi, Guido Herrmann*, Mark Lowenberg, Xiaoqian Chen

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

12 Citations (Scopus)

Abstract

A controller designed for linearizations at various trim/operating points of a nonlinear system using linear approaches is not necessarily performing well or stable once scheduled with a state under dynamic conditions; the key idea of using this scheduled control law design is to retain states close to the current, usually dynamically varying, operating point. Dynamic gain scheduling (DGS) is a technique aimed to resolve this controller scheduling issue for rapidly changing dynamics and states. It entails scheduling the control law gains with a fast varying state variable rather than with a slowly varying state. It has been successfully applied to aircraft system models, allowing also for nested loops. The aim of this paper is to extend dynamic gain scheduling to a more general setting allowing for more complex multi-input dynamics incorporating the more simple static scheduling within the same controller. Hence, given a linear design, suitable transformations are provided which allow fast scheduling of multi-variable controllers. Important parallels to the approach of extended linearization are drawn. Theoretical results are shown, providing explicit formulae related to nonlinear dynamic inversion (NDI) control. Several examples of varying nonlinear complexity are presented in order to emphasize the characteristics of the approach.
Original languageEnglish
Pages (from-to)163-179
Number of pages17
JournalInternational Journal of Control
Volume88
Issue number1
Early online date2 Sep 2014
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • dynamic gain scheduling
  • nonlinear systems
  • extended linearization

Fingerprint Dive into the research topics of 'Dynamic gain-scheduled control and extended linearisation: extensions, explicit formulae and stability'. Together they form a unique fingerprint.

  • Cite this