Decoupling Torque and Stiffness: A Unified Modeling and Control Framework for Antagonistic Artificial Muscles
Amirhossein Kazemipour, Robert K. Katzschmann
- Year
- 2025
- Access
- Open access
Abstract
Antagonistic artificial muscles can decouple joint torque and stiffness, but contact transients often degrade this independence. We present a unified real-time framework applicable across pneumatic, electrohydraulic, and dielectric elastomer artificial muscle families: a separable Padé force model with a minimal two-state dynamic wrapper, a cascaded inverse-dynamics controller in co-contraction/bias coordinates, and a bio-inspired depth-adaptive interaction policy that schedules stiffness based on penetration depth. The controller runs in under 1 ms per control tick and demonstrates independent torque and stiffness tracking, including a fixed-torque stiffness-step test that preserves torque regulation through stiffness transitions. In a coupled impedance contact protocol simulated across soft-to-rigid environments, comparing depth-adaptive stiffness to fixed-stiffness baselines reveals a shock/load versus stability tradeoff. These results provide a control-oriented foundation for musculoskeletal antagonistic robots to execute adaptive impedance behaviors in dynamic interactions.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992