Online Trajectory Regeneration for Multirotors via a Proportional-Derivative Physics-Informed Neural Network

Online Trajectory Regeneration for Multirotors via a Proportional-Derivative Physics-Informed Neural Network

This paper presents a novel framework based on a Proportional-Derivative Physics-Informed Neural Network (PD-PINN) for real-time trajectory regeneration. Unlike conventional methods that rely on static, precomputed paths, the proposed approach dynamically adapts the reference trajectory using real-t...

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Bibliographic Details
Main Authors: Mana Ghanifar, Amir Ali Nikkhah, Milad Kamzan, Mohammad Teshnehlab, Morteza Tayefi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Trajectory regeneration
proportional-derivative error correction
physics-informed neural network
backpropagation through the physical system
adaptive guidance
Online Access:https://ieeexplore.ieee.org/document/11124830/
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https://ieeexplore.ieee.org/document/11124830/

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