Model Predictive Control for 3D Steerable Needles: A Hierarchical Approach to Reduce Tissue Trauma

Sajjad Hussain; Mahdi Tavakoli; Bruno Siciliano; Fanny Ficuciello

IROS 20251 citations

Model Predictive Control for 3D Steerable Needles: A Hierarchical Approach to Reduce Tissue Trauma

Sajjad Hussain, Mahdi Tavakoli, Bruno Siciliano, Fanny Ficuciello

Abstract

This paper presents a three-dimensional (3D) control framework for bevel-tip steerable needles that combines model predictive control (MPC) with hierarchical supervisory logic. The MPC layer uses a reduced-order two-mode switching model to generate the desired control actions, while the supervisory logic adaptively prioritizes in-plane and out-of-plane corrections based on real-time error magnitudes. This hierarchical approach smoothly modulates the axial rotation commands to minimize abrupt needle flips, thereby reducing the so-called "drilling effect", a key source of tissue trauma. The simulation results show that the proposed approach reduces tissue trauma by more than 50% compared to conventional pulse-width-modulated sliding mode controllers while achieving mean absolute error and targeting errors in the submillimeter range.

BibTeX

@inproceedings{iros2025_modelpredictivec,
  title = {Model Predictive Control for 3D Steerable Needles: A Hierarchical Approach to Reduce Tissue Trauma},
  author = {Sajjad Hussain and Mahdi Tavakoli and Bruno Siciliano and Fanny Ficuciello},
  booktitle = {IROS 2025},
  year = {2025}
}