Linguistic Lyapunov reinforcement learning control for robotic manipulators

We propose a Lyapunov theory based linguistic reinforcement learning (RL) framework for stable tracking control of robotic manipulators. In particular, we employ Lyapunov theory to constrain fuzzy rule consequents for ensuring stability of the designed controller. Proposed fuzzy RL controller employs Lyapunov theory dictated rules for discovering an optimal yet stable control strategy for robotic manipulators. Furthermore, our proposed linguistic RL controller handles payload variations and external disturbances quite effectively. We validate linguistic Lyapunov RL controller on two benchmark control problems: (i) a standard two-link robotic arm manipulator, and (ii) a two link selective compliance assembly robotic arm (SCARA). Simulation results and comparison against (a) baseline fuzzy Q learning (FQL) controller, and (b) a recently proposed Lyapunov theory based Markov game controller showcases our controller's superior tracking performance and lower computational complexity. Furthermore, our controller exhibits high stability with disturbances and payload variations.