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.
|Translated title of the contribution||A Novel Q-Learning Based Adaptive Optimal Controller Implementation for a Humanoid Robotic Arm|
|Title of host publication||18th IFAC World Congress, Milan|
|Publication status||Published - 2011|