Rapid Vibration Suppression and Trajectory Tracking of a Serial Manipulator with Multi-Flexible Links
For roboticists working with flexible manipulators, this provides a practical controller that suppresses vibrations and tracks trajectories faster than existing methods, though the improvement is incremental.
This paper presents a backstepping output-feedback framework for vibration suppression and tip tracking of multi-link flexible manipulators, using a DeepONet to approximate backstepping kernels for real-time implementation. Experiments on a two-link manipulator show faster vibration suppression and trajectory convergence compared to LQR with feedforward control.
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 rapid vibration suppression and trajectory tracking, only using available boundary measurements. To enable real-time implementation and scalability, a DeepONet neural operator is introduced to approximate the backstepping kernels, significantly reducing computational cost and facilitating fast controller updates under varying operating conditions. Experiments on a two-link flexible manipulator demonstrate faster vibration suppression and convergence of the end-effector to the desired trajectory, compared with a linear quadratic regulator (LQR) with feedforward control.