Adaptive dynamic inversion via time-scale separation

Naira Hovakimyan, Eugene Lavretsky, Chengyu Cao

Research output: Contribution to journalArticle

Abstract

This paper presents a full state feedback adaptive dynamic inversion method for uncertain systems that depend nonlinearly upon the control input. Using a specialized set of basis functions that respect the monotonic property of the system nonlinearities with respect to control input, a state predictor is defined for derivation of the adaptive laws. The adaptive dynamic inversion controller is defined as a solution of a fast dynamical equation, which achieves time-scale separation between the state predictor and the controller dynamics. Lyapunov-based adaptive laws ensure that the predictor tracks the state of the nonlinear system with bounded errors. As a result, the system state tracks the desired reference model with bounded errors. Benefits of the proposed design method are demonstrated using Van der Pol dynamics with nonlinear control input.

Original languageEnglish (US)
Pages (from-to)1702-1711
Number of pages10
JournalIEEE Transactions on Neural Networks
Volume19
Issue number10
DOIs
StatePublished - Aug 28 2008

Keywords

  • Adaptive control
  • Nonaffine systems
  • Time-scale separation
  • Ultimate boundedness

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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