Adaptive output feedback control of flexible-joint robots using neural networks: Dynamic surface design approach

Sung Jin Yoo, Jin Bae Park, Yoon Ho Choi

Research output: Contribution to journalArticlepeer-review

138 Citations (Scopus)


In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

Original languageEnglish
Pages (from-to)1712-1726
Number of pages15
JournalIEEE Transactions on Neural Networks
Issue number10
Publication statusPublished - 2008

Bibliographical note

Funding Information:
Manuscript received July 10, 2007; revised February 20, 2008; accepted June 4, 2008. First published September 26, 2008; current version published October 8, 2008. This work was supported in part by Yonsei University Institute of TMS Information Technology, a Brain Korea 21 program, and MOCIE through EIRC program with Yonsei Electric Power Research Center (YEPRC) at Yonsei University, Seoul, Korea.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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