Practical Prescribed-Time Stabilization and Tracking for Uncertain Nonlinear Systems with Disturbance Inputs

P. Krishnamurthy, F. Khorrami

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

Abstract

We develop an output-feedback control design that achieves practical prescribed-time stabilization, i.e., convergence to within a pre-specified neighborhood around the origin within a pre-specified ("prescribed") time interval. The control design is applicable to a class of uncertain nonlinear systems in a strict-feedback-like form with parametric and functional uncertainties throughout the system as well as exogenous disturbance inputs. The designed output-feedback control design achieves prescribed-time convergence of both the observer errors and the system state variables as well as the control input. Additionally, by considering the limiting case as the size of the desired convergence neighborhood around the origin is reduced to zero, it is shown that the prior results on prescribed-time stabilization (to zero in prescribed time) can be obtained without having to explicitly introduce the temporal scale transformation used in the prior works. It is also shown that the control design approach can be applied to tracking problems to achieve practical convergence of tracking errors.

Original languageEnglish (US)
Title of host publication60th IEEE Conference on Decision and Control, CDC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4588-4593
Number of pages6
ISBN (Electronic)9781665436595
DOIs
StatePublished - 2021
Event60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States
Duration: Dec 13 2021Dec 17 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2021-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference60th IEEE Conference on Decision and Control, CDC 2021
Country/TerritoryUnited States
CityAustin
Period12/13/2112/17/21

ASJC Scopus subject areas

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

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