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- W4200473109 abstract "This paper presents a learning-based model-free Neuro-Fuzzy control framework for robot manipulators. A real-time learning strategy based on the emotional learning concept is applied to an ANFIS-type (Adaptive Neuro-Fuzzy inference system) fuzzy controller to solve the trajectory-following problem for robot manipulators. The proposed learning strategy eliminates the dependency of the ANFIS controller on the training phase by real-time updating of its neural network component parameters. In this strategy, an agent unit evaluates the controller’s performance by continuously monitoring the robot manipulator. Based on the evaluation results, the Agent applies appropriate changes to the controller parameters for enhancing its overall performance. Numerical simulations show the effectiveness of the proposed learning-based control framework for trajectory tracking of a 2-DOF robot manipulator. The normalized root mean square (NRSME) of tracking errors is less than 1 deg for each robot’s joint. The controller also shows a robust behavior in the presence of uncertainties, when 50% and 20% uncertainties are applied to the robot manipulator’s physical parameters and closed-loop control signals, respectively." @default.
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- W4200473109 date "2021-11-11" @default.
- W4200473109 modified "2023-09-29" @default.
- W4200473109 title "Adaptive Real-time Learning-based Neuro-Fuzzy Control of Robot Manipulators" @default.
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- W4200473109 doi "https://doi.org/10.1109/iccma54375.2021.9646224" @default.
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