Tracking control using hierarchical fuzzy-regulated optimal approach for robotic manipulators

Robotic manipulators have captured the attention of many specialists owing to its importance in academic research and industrial automation. This paper proposes a novel hierarchical self-organizing fuzzy optimal controller for the trajectory tracking control of robotic manipulator systems. The hierarchical self-organizing fuzzy optimal controller employs a self-organizing fuzzy logic system as a superior control strategy regulator for a subordinate optimal tracking controller. Using the self-organizing learning and fuzzy inference operation, the weighting matrix in the optimal controller is configured adaptively according to the robotic dynamical behavior. The optimal tracking control law under this hierarchical architecture is derived using the maximum principle. Stability and robustness of the hierarchical self-organizing fuzzy optimal controller are then analyzed and proved through the Lyapunov stability approach and Barbalat's Lemma. A simulation study demonstrates the effectiveness and feasibility of this hierarchical system, and compares it with a self-organizing fuzzy controller.