Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks

Sung Jin Yoo, Jin Bae Park, Yoon Ho Choi

Research output: Contribution to journalArticle

134 Citations (Scopus)

Abstract

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

Original languageEnglish
Pages (from-to)1342-1355
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume36
Issue number6
DOIs
Publication statusPublished - 2006 Dec 1

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Control surfaces
Robots
Neural networks
Control systems
Backstepping
Adaptive systems
Robust control
Explosions
Actuators
Controllers
Uncertainty

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

Cite this

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Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks. / Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 36, No. 6, 01.12.2006, p. 1342-1355.

Research output: Contribution to journalArticle

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