A Novel Q-Learning Based Adaptive Optimal Controller Implementation for a Humanoid Robotic Arm

SG Khan, G Herrmann, F.L Lewis, AG Pipe, CR Melhuish

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    This paper presents the implementation of a novel model-free Q-learning based discrete adaptive optimal controller for a humanoid robotic arm. The controller uses a novel adaptive dynamic programming (ADP) reinforcement learning (RL) approach to develop an optimal policy on-line. This is in contrast with the other optimal control design techniques which are carried out off-line and need full information of the system dynamics. The RL tracking controller was implemented for two links (shoulder flexion and elbow flexion joints) of the arm of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) torso. The constrained case (joint limits) of the RL scheme was tested for a single link (elbow flexion) of the BERT II arm by modifying the cost function to deal with the extra nonlinearity due to the joint constraint.
    Original languageEnglish
    Pages (from-to)13528-13533
    Number of pages6
    JournalIFAC-PapersOnLine
    Volume44
    Issue number1
    DOIs
    Publication statusPublished - 2011

    Fingerprint

    Dive into the research topics of 'A Novel Q-Learning Based Adaptive Optimal Controller Implementation for a Humanoid Robotic Arm'. Together they form a unique fingerprint.

    Cite this