Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data

2026-01-06

AI Summary

This work addresses the challenge of precise control in soft artificial muscles—such as HASEL actuators—whose dynamics are hindered by multiphysical nonlinearities, hysteresis, and memory effects. Leveraging spectral submanifold (SSM) theory, the authors propose a data-driven modeling and control framework that constructs an explicit input–output map directly on a low-dimensional slow manifold, using forced response trajectories obtained under slow inputs. This approach circumvents hysteresis without requiring decay experiments. By integrating feedback with feedforward control, the method significantly outperforms pure feedback or pure feedforward baselines in an antagonistic HASEL clutch joint, achieving substantially reduced tracking errors under identical conditions. The framework enables rapid characterization and high-precision real-time control of soft muscle systems.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Learning with Manifolds

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) actuators, a class of soft artificial muscles, offer high performance but exhibit memory effects and hysteresis. Here we present a data-driven reduction and control strategy grounded in spectral submanifold (SSM) theory. In the adiabatic regime, where inputs vary slowly relative to intrinsic transients, trajectories rapidly converge to a low-dimensional slow manifold. We learn an explicit input-to-output map on this manifold from forced-response trajectories alone, avoiding decay experiments that can trigger hysteresis. We deploy the SSM-based model for real-time control of an antagonistic HASEL-clutch joint. This approach yields a substantial reduction in tracking error compared to feedback-only and feedforward-only baselines under identical settings. This record-and-control workflow enables rapid characterization and high-performance control of soft muscles and muscle-driven joints without detailed physics-based modeling.

Problem

• soft robotics
• artificial muscles
• nonlinear dynamics
• hysteresis
• HASEL actuators

Innovation

• spectral submanifold
• data-driven control
• soft robotics
• HASEL actuators
• nonlinear dynamics