Problem
Research questions and friction points this paper is trying to address.
Soft Robots
Orientation Control
Model Predictive Control
Adiabatic Spectral Submanifolds
Orientation Control of Soft Robots via Adiabatic Spectral Submanifolds
🤖 AI Summary
研究通过扩展adiabatic spectral submanifolds方法，提高软体机器人在复杂环境下的位置和姿态控制精度，使用模型预测控制降低跟踪误差。
📝 Abstract
Soft robots are commonly sought for safety-critical interactions in delicate environments, where accurate position and orientation control is imperative. Model predictive control (MPC) offers a solution, but it requires a model of the robot's infinite-dimensional nonlinear dynamics that is at once accurate and computationally cheap. Recent theory on adiabatic spectral submanifolds (aSSMs) and their applications to soft robots provide data-driven model-reduction methods to construct such models. Here, we extend these methods to identify aSSMs from enlarged observable datasets and upgrade the currently available aSSM-MPC schemes. Evaluated on a high-fidelity finite-element simulation of a pressure-actuated soft arm, our controller reduces position and orientation tracking error by more than 60% compared to existing data-driven baselines.
Innovation
Methods, ideas, or system contributions that make the work stand out.
adiabatic spectral submanifolds
model predictive control
soft robots
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