1. Model predictive control for a bending pneumatic muscle based on an online modified generalized Prandtl–Ishlinskii model.
- Author
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Ru, Hongge, Yang, Yuqi, Wang, Bo, and Huang, Jian
- Subjects
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FUZZY neural networks , *PNEUMATIC control , *PREDICTION models , *FUZZY integrals , *PNEUMATIC actuators - Abstract
Pneumatic actuators exhibit significant potential across various applications owing to their compliance, yet achieving precise motion control remains challenging due to rate-dependent and asymmetric hysteresis. While the Prandtl–Ishlinskii model adeptly captures intricate hysteresis traits, its practical control usage often necessitates intricate inversions, resulting in elevated computational burden and limited accommodation of system uncertainties and model inaccuracies. This study introduces an online, rate-dependent modified generalized Prandtl–Ishlinskii model derived via the gradient descent algorithm. This model is seamlessly amalgamated with a model predictive control strategy, addressing the inversion challenge inherent in the Prandtl–Ishlinskii model. Leveraging integration with a three-layer fuzzy neural network controller, the proposed approach achieves closed-loop trajectory tracking control for a soft bending pneumatic muscle. Convergence analysis, grounded in Lyapunov theory, underscores the efficacy of the proposed model. Comprehensive real-world comparative experiments affirm the approach's effectiveness and reliability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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