This study presents a novel optimal fuzzy logic control (FLC) strategy based on feedback linearization for the regulation of multivariable nonlinear systems. Building upon an enhanced Takagi–Sugeno (T-S) model previously developed by the authors, the proposed method incorporates a refined parameter-weighting scheme to optimize both local and global approximations within the T-S framework. This approach enables improved selection and minimization of the performance index. The effectiveness of the control strategy is validated through its application to a two-link serial robotic manipulator. The results demonstrate that the proposed FLC achieves robust performance, maintaining system stability and high accuracy even under the influence of noise and load disturbances, with well-damped system behavior and negligible steady-state error.
This study presents a novel optimal fuzzy logic control (FLC) strategy based on feedback linearization for the regulation of multivariable nonlinear systems. Building upon an enhanced Takagi–Sugeno (T-S) model previously developed by the authors, the proposed method incorporates a refined parameter-weighting scheme to optimize both local and global approximations within the T-S framework. This approach enables improved selection and minimization of the performance index. The effectiveness of the control strategy is validated through its application to a two-link serial robotic manipulator. The results demonstrate that the proposed FLC achieves robust performance, maintaining system stability and high accuracy even under the influence of noise and load disturbances, with well-damped system behavior and negligible steady-state error. Read More
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