Composite learning control of flexible-link manipulator using NN and DOB

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Abstract

This paper investigates the singular perturbation (SP) theory-based composite learning control of a flexible-link manipulator using neural networks (NNs) and disturbance observer (DOB). For the dynamics, the system states are separated into fast and slow variables in terms of time scale. For the multi-input-multi-output slow dynamics, the intelligent control is designed where NNs are used for system uncertainty approximation and the DOB is used for compound disturbance estimation. The main contribution is that a novel controller using NN and DOB is constructed to deal with unknown dynamics and time-varying disturbances while the composite learning algorithm is proposed with prediction error. For the fast dynamics, sliding mode control is employed. The boundedness of the tracking error is proved via Lyapunov approach. The simulation results show that the DOB-based composite neural control can greatly improve the tracking precision.

Original languageEnglish
Article number7930483
Pages (from-to)1979-1985
Number of pages7
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume48
Issue number11
DOIs
StatePublished - Nov 2018

Keywords

  • Composite neural learning
  • disturbance observer (DOB)
  • flexible-link manipulator
  • perturbation theory

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