A Matrix Pencil Approach to Efficient Realization of Dynamic Scaling-Based Output-Feedback Controllers for Uncertain Nonlinear Strict-Feedback Systems

P. Krishnamurthy, F. Khorrami

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

Abstract

We propose a matrix pencil based approach for design of output-feedback stabilizing controllers for a general class of uncertain nonlinear strict-feedback-like systems. While the dynamic controller structure is based on the dual dynamic high-gain scaling based approach, we cast the design procedure within a general matrix pencil based structure unlike prior results that utilized conservative algebraic bounds of terms arising in Lyapunov inequalities. The proposed approach models the detailed system structure and state dependence structure of uncertain terms. The design freedoms in the dynamic high-gain scaling based controller are extracted in terms of generalized eigenvalues associated with matrix pencils formulated to capture the detailed structures of bounds in the Lyapunov analysis. The proposed matrix pencil approach enables efficient computation of non-conservative bounds with reduced algebraic complexity and significantly enhances feasibility of application of the dual dynamic high-gain scaling based control designs.

Original languageEnglish (US)
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages745-750
Number of pages6
ISBN (Electronic)9781728174471
DOIs
StatePublished - Dec 14 2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
CountryKorea, Republic of
CityVirtual, Jeju Island
Period12/14/2012/18/20

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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