Spacecraft Collision Avoidance with Constrained Control via Discrete-Time Generating Functions

Kwangwon Lee, Chandeok Park

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

Abstract

This study presents sub-optimal collision-free transfers of spacecraft subject to constraints on control magnitude. In order to mitigate the difficulty in solving an optimal control problem considering directly inequality constraints, the penalty and barrier functions are incorporated into the cost function of optimal tracking problem. Then, the sub-optimal control law is derived by employing the discrete-time generating functions representing the canonical transformation in the discrete-time Hamilton-Jacobi theory. The proposed approach allows us to derive the control law as an algebraic form of the states of spacecraft, reference solution, and obstacles without any iterative process and initial guess. The numerical simulations validate the proposed approach by showing that spacecraft can reach the target point while avoiding obstacles with constrained control.

Original languageEnglish
Title of host publicationMED 2018 - 26th Mediterranean Conference on Control and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages565-569
Number of pages5
ISBN (Print)9781538678909
DOIs
Publication statusPublished - 2018 Aug 20
Event26th Mediterranean Conference on Control and Automation, MED 2018 - Zadar, Croatia
Duration: 2018 Jun 192018 Jun 22

Publication series

NameMED 2018 - 26th Mediterranean Conference on Control and Automation

Other

Other26th Mediterranean Conference on Control and Automation, MED 2018
CountryCroatia
CityZadar
Period18/6/1918/6/22

Bibliographical note

Funding Information:
This work has been supported by the National GNSS Research Center program of Defense Acquisition Program Administration and Agency for Defense Development.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization

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