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A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning

التفاصيل البيبلوغرافية
العنوان: A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning
المؤلفون: Yang, Yi, Wang, Xuchen, Voyles, Richard M., Ma, Xin
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Robotics, Computer Science - Neural and Evolutionary Computing
الوصف: This paper develops a predefined-time convergent and noise-tolerant fractional-order zeroing neural network (PTC-NT-FOZNN) model, innovatively engineered to tackle time-variant quadratic programming (TVQP) challenges. The PTC-NT-FOZNN, stemming from a novel iteration within the variable-gain ZNN spectrum, known as FOZNNs, features diminishing gains over time and marries noise resistance with predefined-time convergence, making it ideal for energy-efficient robotic motion planning tasks. The PTC-NT-FOZNN enhances traditional ZNN models by incorporating a newly developed activation function that promotes optimal convergence irrespective of the model's order. When evaluated against six established ZNNs, the PTC-NT-FOZNN, with parameters $0 < \alpha \leq 1$, demonstrates enhanced positional precision and resilience to additive noises, making it exceptionally suitable for TVQP tasks. Thorough practical assessments, including simulations and experiments using a Flexiv Rizon robotic arm, confirm the PTC-NT-FOZNN's capabilities in achieving precise tracking and high computational efficiency, thereby proving its effectiveness for robust kinematic control applications.
Comment: 14 pages, 4 figures; as accepted for publication
نوع الوثيقة: Working Paper
DOI: 10.26599/TST.2024.9010202
URL الوصول: http://arxiv.org/abs/2412.20477
رقم الانضمام: edsarx.2412.20477
قاعدة البيانات: arXiv
الوصف
DOI:10.26599/TST.2024.9010202