Drive-Based Motivation for Coordination of Limit Cycle Behaviors

Craig Thompson, Paul Reverdy

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

1 Scopus citations


Constructing autonomous systems capable of high-level behaviors often involves reducing these behaviors to a collection of low-level tasks. This requires developing a method for switching among possible tasks, for example using a hybrid automaton. Recent work has developed an alternative approach using continuous dynamical systems that have an internal drive state to select the desired task. In one particular result, authors considered a scenario where individual behaviors were encoded in control vector fields with unique, globally stable equilibria. A further level of complexity arises when one seeks to create a system that switches between tasks encoded as globally attracting sets with recurrent behaviors, rather than as point attractors. This work outlines the problem using the recently-developed drive-based dynamical framework. First we generalize the formulation of tasks as one part attracting set and one part recurrent behavior on said attracting set. Then as a proof-of-concept we demonstrate the existence of an attracting set consisting of orbits that repeatedly flow between two canonical limit cycles (e.g., Hopf oscillators).

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728113982
StatePublished - Dec 2019
Externally publishedYes
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

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


Conference58th IEEE Conference on Decision and Control, CDC 2019

ASJC Scopus subject areas

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


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