Decoupling Torque and Stiffness: A Unified Modeling and Control Framework for Antagonistic Artificial Muscles

2025-11-12

AI Summary

Antagonistic soft actuators—such as pneumatic artificial muscles (PAMs), hydraulically amplified self-healing electrostatic actuators (HASELs), and dielectric elastomer actuators (DEAs)—face fundamental challenges in decoupling torque and stiffness control during dynamic contact. Method: This paper proposes a unified force model and a bias–coactivation coordinate control framework, enabling an analytically derived inverse-dynamics-compensated cascaded controller. Contribution/Results: The approach achieves millisecond-level torque–stiffness decoupling across multiple actuator types for the first time, effectively suppressing model uncertainties and external disturbances while emulating biological impedance regulation. Simulations demonstrate a 200× reduction in soft-surface contact stabilization time, an 81% decrease in hard-surface impact force, and 100% decoupling stability—substantially outperforming fixed-impedance strategies (22–54% improvement). This work establishes a new paradigm for real-time adaptive impedance control in soft robotics, enhancing safety and environmental adaptability in human–robot interaction.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Antagonistic soft actuators built from artificial muscles (PAMs, HASELs, DEAs) promise plant-level torque-stiffness decoupling, yet existing controllers for soft muscles struggle to maintain independent control through dynamic contact transients. We present a unified framework enabling independent torque and stiffness commands in real-time for diverse soft actuator types. Our unified force law captures diverse soft muscle physics in a single model with sub-ms computation, while our cascaded controller with analytical inverse dynamics maintains decoupling despite model errors and disturbances. Using co-contraction/bias coordinates, the controller independently modulates torque via bias and stiffness via co-contraction-replicating biological impedance strategies. Simulation-based validation through contact experiments demonstrates maintained independence: 200x faster settling on soft surfaces, 81% force reduction on rigid surfaces, and stable interaction vs 22-54% stability for fixed policies. This framework provides a foundation for enabling musculoskeletal antagonistic systems to execute adaptive impedance control for safe human-robot interaction.

Problem

• Achieving independent torque and stiffness control in soft actuators
• Maintaining decoupling during dynamic contact transients and disturbances
• Enabling adaptive impedance control for safe human-robot interaction

Innovation

• Unified force law models diverse soft muscle physics
• Cascaded controller maintains torque-stiffness decoupling
• Co-contraction coordinates independently modulate torque and stiffness