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Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
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Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics.

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    Summary

    This study introduces a novel intelligent control system for robot manipulators, enhancing sliding-mode control (SMC) with fuzzy neural networks (FNNISMC). This approach achieves precise position tracking and robustness, even with incomplete system knowledge.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Robot manipulators require precise control for tasks.
    • Sliding-mode control (SMC) offers robustness but can suffer from chattering and requires detailed system models.
    • Intelligent control methods aim to improve performance and reduce reliance on exact system parameters.

    Purpose of the Study:

    • To design and analyze an intelligent control system for n-link robot manipulators.
    • To enhance the robustness and precision of robot control by integrating SMC with fuzzy neural networks (FNNISMC).
    • To address limitations of conventional SMC, such as the need for detailed system information and chattering control efforts.

    Main Methods:

    • Development of a conventional sliding-mode control (SMC) scheme for robot joint position tracking.
    • Proposal of a fuzzy-neural-network inherited SMC (FNNISMC) scheme.
    • Design of a fuzzy neural network (FNN) framework to mimic the SMC law.
    • Derivation of adaptive tuning algorithms using projection algorithm and Lyapunov stability theorem for network parameters.

    Main Results:

    • The proposed FNNISMC system demonstrates high-precision position tracking for robot manipulators.
    • The FNNISMC scheme exhibits firm robustness, effectively managing actuator dynamics.
    • Numerical simulations and experimental results validate the FNNISMC system's performance on a two-link robot manipulator.
    • Quantitative comparisons show the superiority of FNNISMC over previous intelligent control schemes.

    Conclusions:

    • The FNNISMC scheme effectively combines the robustness of SMC with the adaptive capabilities of fuzzy neural networks.
    • This approach relaxes the need for detailed system information and mitigates chattering issues inherent in traditional SMC.
    • The FNNISMC system provides a robust and high-precision control solution for robot manipulators, validated through simulations and experiments.