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 journalArticle

203 Citations (Scopus)

Abstract

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
Volume17
Issue number1
DOIs
Publication statusPublished - 2009 Jan 1

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Sliding mode control
Mobile robots
Neural networks
Control systems
Robust control
Robustness (control systems)
Closed loop systems
Dynamic models
Kinematics
Tuning
Trajectories
Uncertainty
Computer simulation

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

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Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. / Park, Bong Seok; Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho.

In: IEEE Transactions on Control Systems Technology, Vol. 17, No. 1, 01.01.2009, p. 207-214.

Research output: Contribution to journalArticle

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