Dynamic Neural Networks for Kinematic Redundancy Resolution of Parallel Stewart Platforms

Aquil Mirza Mohammed, Shuai Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

46 Citations (Scopus)

Abstract

Redundancy resolution is a critical problem in the control of parallel Stewart platform. The redundancy endows us with extra design degree to improve system performance. In this paper, the kinematic control problem of Stewart platforms is formulated to a constrained quadratic programming. The Karush-Kuhn-Tucker conditions of the problem is obtained by considering the problem in its dual space, and then a dynamic neural network is designed to solve the optimization problem recurrently. Theoretical analysis reveals the global convergence of the employed dynamic neural network to the optimal solution in terms of the defined criteria. Simulation results verify the effectiveness in the tracking control of the Stewart platform for dynamic motions.
Original languageEnglish
Article number7166310
Pages (from-to)1538-1550
Number of pages13
JournalIEEE Transactions on Cybernetics
Volume46
Issue number7
DOIs
Publication statusPublished - 1 Jul 2016

Keywords

  • Constrained quadratic programming
  • kinematic redundancy
  • recurrent neural networks
  • Stewart platform

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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