Control of nonholonomic systems using reference vector fields

Dimitra Panagou, Herbert G. Tanner, Kostas J. Kyriakopoulos

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

Abstract

This paper presents a control design methodology for n-dimensional nonholonomic systems. The main idea is that, given a nonholonomic system subject to κ Pfaffian constraints, one can define a smooth, N-dimensional reference vector field F, which is nonsingular everywhere except for a submanifold containing the origin. The dimension N ≤ n of F depends on the structure of the constraint equations, which induces a foliation of the configuration space. This foliation, together with the objective of having the system vector field aligned with F, suggests a choice of Lyapunov-like functions V. The proposed approach recasts the original nonholonomic control problem into a lower-dimensional output regulation problem, which although nontrivial, can more easily be tackled with existing design and analysis tools. The methodology applies to a wide class of nonholonomic systems, and its efficacy is demonstrated through numerical simulations for the cases of the unicycle and the n-dimensional chained systems, for n = 3, 4.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2831-2836
Number of pages6
ISBN (Print)9781612848006
DOIs
StatePublished - 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

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

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Country/TerritoryUnited States
CityOrlando, FL
Period12/12/1112/15/11

ASJC Scopus subject areas

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

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