SSM-Based Data-Driven Control for Soft Robotic Muscles

Data-driven spectral submanifold (SSM) modeling and closed-loop control for antagonistic electrohydraulic musculoskeletal joints.

Outcome

I built and controlled an antagonistic electrohydraulic musculoskeletal joint using a data-driven spectral submanifold (SSM) model for feedforward control. The combined controller was validated on hardware and reduced tracking error substantially compared with both feedback-only and feedforward-only baselines.

Problem

Electrohydraulic artificial muscles are compliant, strongly nonlinear, and history-dependent. In practice, this makes first-principles modeling hard to scale for real-time control. The goal of this project was to keep model-based control benefits while avoiding a heavy full-order model, by learning a compact dynamical representation directly from experiment data and deploying it on real hardware.

System

  • Hardware platform: antagonistic HASEL-clutch artificial-muscle joint with joint-angle sensing and high-voltage actuation
  • Data collection: forced-response trajectories across the operating range used to identify reduced-order dynamics
  • Modeling: SSM-based reduced model in the adiabatic regime, including a learned map between voltage input and joint motion
  • Control law: inverse-SSM feedforward combined with PI feedback, anti-windup, and command-rate/safety limits
  • Validation protocol: identical constraints and trajectories across three controllers (feedforward-only, feedback-only, combined)

Contribution

  • Built and integrated the antagonistic electrohydraulic hardware platform used for model identification and control validation
  • Implemented the real-time control stack and deployed the inverse-SSM + PI controller on physical hardware
  • Designed and executed benchmark experiments for fair comparison across controller variants
  • Co-developed the overall data-driven control pipeline and evaluation workflow

Technical Stack

  • Electrohydraulic antagonistic-joint hardware integration
  • Spectral submanifold (SSM) reduced-order modeling
  • Data-driven system identification from forced-response experiments
  • Inverse-model feedforward + PI feedback control
  • Real-time closed-loop control and benchmarking on hardware

Key Results

  • Combined inverse-SSM + PI controller: RMS error 2.38°, max error 10.41°
  • Feedforward-only baseline: RMS error 3.63°, max error 16.67°
  • Feedback-only baseline: RMS error 7.63°, max error 26.00°
  • Relative to feedback-only, combined control reduced RMS error by ~69% and max error by ~60%
  • Relative to feedforward-only, combined control reduced RMS error by ~34% and max error by ~38%

Media

Impact and Future Direction

This project shows a practical path for deploying data-driven nonlinear control on musculoskeletal robotic joints: learn compact dynamics from real data, then run model-based feedforward with feedback correction in real time. The same approach can be extended to multi-joint antagonistic systems and more demanding motion tasks where pure black-box control or full-order modeling alone is limiting.

Skills

data-driven control SSM modeling electrohydraulic muscles hardware integration real-time control benchmarking