Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty

Bong Seok Park, Sung Jin Yoo, Jin Bae Park, Yoon Ho Choi

Research output: Contribution to journalArticlepeer-review

273 Citations (Scopus)


This brief proposes an adaptive neural sliding mode control method for trajectory tracking of nonholonomic wheeled mobile robots with model uncertainties and external disturbances. The dynamic model with model uncertainties and the kinematic model represented by polar coordinates are considered to design a robust control system. Self recurrent wavelet neural networks (SRWNNs) are used for approximating arbitrary model uncertainties and external disturbances in dynamics of the mobile robot. From the Lyapunov stability theory, we derive online tuning algorithms for all weights of SRWNNs and prove that all signals of a closed-loop system are uniformly ultimately bounded. Finally, we perform computer simulations to demonstrate the robustness and performance of the proposed control system.

Original languageEnglish
Pages (from-to)207-214
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Issue number1
Publication statusPublished - 2009

Bibliographical note

Funding Information:
Manuscript received September 06, 2007; revised November 25, 2007. Manuscript received in final form March 13, 2008. First published December 9, 2008; current version published December 24, 2008. Recommended by Associate Editor T. Zhang. This work was supported in part by Yonsei University Institute of TMS Information Technology, by a Brain Korea 21 program, and by MOCIE through EIRC program with Yonsei Electric Power Research Center (YEPRC) at Yonsei University, Seoul, Korea.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty'. Together they form a unique fingerprint.

Cite this