用简单模型精准预测液压软体执行器的压强变化，适合实时控制。
Simplifying Data-Driven Modeling of the Volume-Flow-Pressure Relationship in Hydraulic Soft Robotic Actuators
- 采用多项式回归等低复杂度模型拟合体积-流量-压强关系。
- 多变量多项式模型在参数更少时仍保持高精度预测能力。
- 适合需要快速计算的软体机器人实时控制场景。
软体机器人具有柔韧性和适应性，但传统物理模型难以捕捉其复杂的非线性行为。本研究探索了基于数据驱动的方法来建模液压软体执行器中的体积-流量-压强关系，重点分析低复杂度但高精度的模型。通过对堆叠气球式执行器系统进行回归分析，比较了含与不含自回归输入的指数型、多项式及神经网络模型。结果表明，更简单的模型（尤其是多变量多项式）在参数更少的情况下仍能有效预测压强动态变化。该研究为实时软体机器人应用提供了实用建模方案，兼顾模型复杂度与计算效率，同时可推广至需显式解析模型的其他技术领域。
原文摘要 · Abstract (English)
Soft robotic systems are known for their flexibility and adaptability, but traditional physics-based models struggle to capture their complex, nonlinear behaviors. This study explores a data-driven approach to modeling the volume-flow-pressure relationship in hydraulic soft actuators, focusing on low-complexity models with high accuracy. We perform regression analysis on a stacked balloon actuator system using exponential, polynomial, and neural network models with or without autoregressive inputs. The results demonstrate that simpler models, particularly multivariate polynomials, effectively predict pressure dynamics with fewer parameters. This research offers a practical solution for real-time soft robotics applications, balancing model complexity and computational efficiency. Moreover, the approach may benefit various techniques that require explicit analytical models.
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