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Mobile Robot Path-Tracking Using an Adaptive Critic Learning PD Controller

  • Xin Xu
  • Xuening Wang
  • Dewen Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

This paper proposes a novel self-learning PD (Proportional- Derivative) control method for mobile robot path-tracking problems. In the self-learning PD control method, a reinforcement-learning (RL) module is used to automatically fine-tune the PD coefficients with only evaluative feedback. The optimization of the PD coefficients is modeled as a Markov decision problem (MDP) with continuous state space. Using an improved AHC (Adaptive Heuristic Critic) learning control method based on recursive least-squares algorithms, the near-optimal control policies of the MDP are approximated efficiently. Besides its simplicity, the self-learning PD controller can be adaptive to uncertainties in the environment as well as the mobile robot dynamics. Simulation and experimental results on a real mobile robot illustrate the effectiveness of the proposed method.

Keywords

Mobile Robot Reward Function Critic Network Continuous State Space Adaptive Critic 
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 2004

Authors and Affiliations

  • Xin Xu
    • 1
    • 2
  • Xuening Wang
    • 2
  • Dewen Hu
    • 2
  1. 1.School of ComputerNational University of Defense TechnologyChangshaP.R. China
  2. 2.Department of Automatic ControlNational University of Defense TechnologyChangshaP.R. China

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