Force control for flexible robots using neural networks

Joseph Borowiec, Antonios Tzes

Research output: Contribution to journalConference article

Abstract

Force control for flexible link robots using neural networks is considered in this article. The nonlinear dynamics of the robot manipulator are identified through a recurrent neural network (RNN), which is trained in an off-line manner. Inversion of the RNN-based model dynamics leads to a feedforward component. The feedback controller gains are derived from the minimization of a discrete linear quadratic cost functional, subject to the model dynamics inferred by the linearization of the neural network along the desired trajectory. Sufficient conditions for temporal gain switching bounds are provided. The proposed control scheme is employed in simulation studies on a two link rigid-flexible manipulator.

Original languageEnglish (US)
Pages (from-to)1950-1954
Number of pages5
JournalProceedings of the American Control Conference
Volume3
StatePublished - Dec 1 1999
EventProceedings of the 1999 American Control Conference (99ACC) - San Diego, CA, USA
Duration: Jun 2 1999Jun 4 1999

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Recurrent neural networks
Force control
Dynamic models
Robots
Neural networks
Flexible manipulators
Linearization
Manipulators
Trajectories
Feedback
Controllers

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Force control for flexible robots using neural networks. / Borowiec, Joseph; Tzes, Antonios.

In: Proceedings of the American Control Conference, Vol. 3, 01.12.1999, p. 1950-1954.

Research output: Contribution to journalConference article

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