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    Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators
    (Elsevier Ltd, 2021) Yilmaz B.M.; Tatlicioglu E.; Savran A.; Alci M.
    With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning is designed. The stability is investigated via Lyapunov type techniques where global uniform ultimate boundedness of closed loop system is guaranteed. Numerical simulation results obtained from a two degree of freedom robot manipulator model and experiments performed on a robot manipulator demonstrate the efficacy of the proposed control methodology. © 2021 Elsevier B.V.
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    Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators In Task Space
    (Institute of Electrical and Electronics Engineers Inc., 2021) Yilmaz B.M.; Tatlicioglu E.; Savran A.; Alci M.
    End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this work. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented. IEEE