Nonconvex projection activated zeroing neurodynamic models for time-varying matrix pseudoinversion with accelerated finite-time convergence

Long Jin, Shuai Li, Huanqing Wang, Zhijun Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)

Abstract

Zeroing dynamics (ZD, or termed Zhang dynamics after its inventor), being a special type of neurodynamic methodology, has shown powerful abilities to solve a great variety of time-varying problems with monotonically increasing odd activation functions. In this paper, two limitations of existing ZD are identified, i.e., the convex restriction on projection operations of activation functions and the low convergence speed with relatively redundant formulations on activation functions. This work breaks them by proposing modified ZD models, allowing nonconvex sets for projection operations in activation functions and possessing accelerated finite-time convergence. Theoretical analyses reveal that the proposed ZD models are of global stability with timely convergence. Finally, illustrative simulation examples, including an application to the motion generation of a robot manipulator, are provided and analyzed to substantiate the efficacy and superiority of the proposed ZD models for real-time varying matrix pseudoinversion.

Original languageEnglish
Pages (from-to)840-850
Number of pages11
JournalApplied Soft Computing Journal
Volume62
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Accelerated finite-time convergence
  • Kinematic control
  • Nonconvex projection
  • Time-varying matrix pseudoinversion
  • Zeroing dynamics

ASJC Scopus subject areas

  • Software

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