A Lyapunov theory based adaptive fuzzy learning control for robotic manipulator

This work proposes to amalgamate the Fuzzy Q learning (FQL) with Lyapunov theory based control resulting in a controller with guaranteed stability for dynamic trajectory tracking control of robotic manipulators. FQL algorithm combines reinforcement learning (RL) approach with fuzzy modeling; however, it fails to address the stability issue of the designed controller. Proposed approach is specifically aimed at addressing this shortcoming. Proposed controller combines powerful generalization and learning capability of fuzzy systems with Lyapunov theory based control that guarantees stability. To demonstrate the viability and effectiveness of the Lyapunov theory based adaptive fuzzy learning approach over basic FQL methodology, we compare the performance of the controller on two degrees of freedom standard two link robot manipulator which is a highly coupled, time varying nonlinear system. Results validate that the proposed hybrid controller indeed leads to a superior performance in terms of both input torques at each joint and tracking accuracy in presence of external disturbances and payload mass variations.