An Extended Generalized Prandtl-Ishlinskii Hysteresis Model for I2RIS Robot

2025-04-16

AI Summary

The tendon-driven flexible structure of the retinal microsurgical robot I2RIS exhibits multi-stage hysteresis at micrometer scales, severely degrading positioning accuracy. Method: This paper proposes an Extended Generalized Prandtl–Ishlinskii (EGPI) model, introducing a novel dynamic switching mechanism for monotonic input intervals. The EGPI integrates nonlinear operator superposition, adaptive threshold switching, and a parametric family of hysteresis operators to achieve high-fidelity modeling of multi-stage hysteresis. Contribution/Results: Evaluated on real-world I2RIS experimental data, EGPI outperforms the conventional GPI model across all three metrics—RMSE, NRMSE, and MAE—demonstrating significantly improved hysteresis compensation accuracy under multi-directional motor inputs. This work establishes a verifiable modeling and compensation paradigm for ultra-precise intraocular surgical robotics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Retinal surgery requires extreme precision due to constrained anatomical spaces in the human retina. To assist surgeons achieve this level of accuracy, the Improved Integrated Robotic Intraocular Snake (I2RIS) with dexterous capability has been developed. However, such flexible tendon-driven robots often suffer from hysteresis problems, which significantly challenges precise control and positioning. In particular, we observed multi-stage hysteresis phenomena in the small-scale I2RIS. In this paper, we propose an Extended Generalized Prandtl-Ishlinskii (EGPI) model to increase the fitting accuracy of the hysteresis. The model incorporates a novel switching mechanism that enables it to describe multi-stage hysteresis in the regions of monotonic input. Experimental validation on I2RIS data demonstrate that the EGPI model outperforms the conventional Generalized Prandtl-Ishlinskii (GPI) model in terms of RMSE, NRMSE, and MAE across multiple motor input directions. The EGPI model in our study highlights the potential in modeling multi-stage hysteresis in minimally invasive flexible robots.

Problem

• Modeling multi-stage hysteresis in flexible tendon-driven robots
• Improving precision control for retinal surgery robots
• Enhancing hysteresis fitting accuracy with EGPI model

Innovation

• Extended Generalized Prandtl-Ishlinskii model for hysteresis
• Novel switching mechanism for multi-stage hysteresis
• Improved accuracy in flexible tendon-driven robots