Rapid Vibration Suppression and Trajectory Tracking of a Serial Manipulator with Multi-Flexible Links
Chengyi Wang, Yilong Huang, Ji Wang

TL;DR
This paper introduces a backstepping output-feedback control framework combined with DeepONet neural operators for rapid vibration suppression and precise trajectory tracking in multi-flexible-link serial manipulators, enhancing performance and scalability.
Contribution
The novel integration of DeepONet neural operators with backstepping control enables real-time, scalable vibration suppression and trajectory tracking in flexible robotic manipulators.
Findings
Faster vibration suppression compared to traditional methods.
Successful experimental validation on a two-link flexible manipulator.
Effective end-effector trajectory convergence under the proposed control scheme.
Abstract
Flexible robotic manipulators (FRMs) offer advantages in lightweight design and large workspace, but their structural flexibility induces vibrations, accelerates fatigue, degrades tracking performance, and limits operational speed. These challenges are further amplified in multi-link serial manipulators, where increased overall length leads to greater structural flexibility. This article presents a backstepping output-feedback framework for fast vibration suppression and tip tracking of an n-degree-of-freedom serial flexible manipulator robot (nDSFMR), with a DeepONet-based approximation for practical deployment. Each link-joint is modeled as a Timoshenko beam coupled with an ODE and transformed into a canonical hyperbolic PDE with boundary dynamics. A backstepping-based boundary controller at the joint is developed to equivalently inject distributed damping along the beam, enabling…
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