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Adaptive Neural Network Path Tracking of Unmanned Ground Vehicle

  • Xiaohong Liao
  • Zhao Sun
  • Liguo Weng
  • Bin Li
  • Yongduan Song
  • Yao Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Unmanned ground vehicles (UGVs) play an increasingly important role in future space exploration and battlefield. This work is concerned with the automatic path tracking control of UGVs. By using the structure properties of the system, neuro-adaptive control algorithms are developed for high precision tracking without involving complex design procedures – the proposed control scheme only demands partial information of the system, no detail description of the system model is needed. Furthermore, uncertain effects such as external disturbance and uncertain parameters can easily be handled. In addition, all the internal signals are uniformly bounded and the control torque is smooth anywhere.

Keywords

Mobile Robot Sliding Mode Control Torque Propose Control Scheme Path Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaohong Liao
    • 1
  • Zhao Sun
    • 1
  • Liguo Weng
    • 1
  • Bin Li
    • 1
  • Yongduan Song
    • 1
  • Yao Li
    • 2
  1. 1.Department of Electrical EngineeringNorth Carolina A&T State UniversityGreensboroUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of MarylandCollege ParkUSA

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