Iterative Learning Motion Control of Continuum Robots Based on Neural Ordinary Differential Equations

Abstract

Traditional data-driven control methods often require large amounts of training data, posing significant challenges for continuum robots. Recently, neural ordinary differential equation (NODE) methods have demonstrated impressive capabilities for data-efficient modeling of continuum robots. However, existing NODE-based control methods still face limitations in terms of convergence and robustness. In this paper, we propose a data-driven iterative learning control system for continuum robots, leveraging NODE for modeling. Within this framework, by incorporating online parameter learning, the proposed control system continuously adapts to various uncertainties associated with continuum robots, resulting in improved convergence and robustness in repetitive tasks. The effectiveness of the proposed method is validated through simulations and physical experiments, and comparative analysis highlights its superior accuracy over existing approaches.