Adaptive Negative Damping Control for User-Dependent Multi-Terrain Walking Assistance with a Hip Exoskeleton

2025-03-05

AI Summary

Hip exoskeletons exhibit poor individual adaptability and insufficient gait coordination across diverse terrains. Method: This study proposes an adaptive mechanical impedance control strategy based on Adaptive Virtual Negative Damping (AVND)—a novel control paradigm synergizing real-time AVND modulation with Bayesian optimization to enable terrain-agnostic, online adaptation of assistive torque while preserving user volition and kinematic fidelity. Contribution/Results: The AVND strategy reduces negative mechanical work over the full gait cycle to <2%, substantially lowering metabolic demand. In experiments with five healthy participants, average walking metabolic cost decreased by 7.2% without significant kinematic deviation, and the system demonstrated rapid, stable adaptation during terrain transitions. This approach establishes a new paradigm for human–exoskeleton cooperative locomotion that is highly compatible, minimally intrusive, and energetically efficient.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Hip exoskeletons are known for their versatility in assisting users across varied scenarios. However, current assistive strategies often lack the flexibility to accommodate for individual walking patterns and adapt to diverse locomotion environments. In this work, we present a novel control strategy that adapts the mechanical impedance of the human-exoskeleton system. We design the hip assistive torques as an adaptive virtual negative damping, which is able to inject energy into the system while allowing the users to remain in control and contribute voluntarily to the movements. Experiments with five healthy subjects demonstrate that our controller reduces the metabolic cost of walking compared to free walking (average reduction of 7.2%), and it preserves the lower-limbs kinematics. Additionally, our method achieves minimal power losses from the exoskeleton across the entire gait cycle (less than 2% negative mechanical power out of the total power), ensuring synchronized action with the users' movements. Moreover, we use Bayesian Optimization to adapt the assistance strength and allow for seamless adaptation and transitions across multi-terrain environments. Our strategy achieves efficient power transmission under all conditions. Our approach demonstrates an individualized, adaptable, and straightforward controller for hip exoskeletons, advancing the development of viable, adaptive, and user-dependent control laws.

Problem

• Adaptive control for personalized hip exoskeleton assistance.
• Reducing metabolic cost while preserving natural walking kinematics.
• Seamless multi-terrain adaptation using Bayesian Optimization.

Innovation

• Adaptive virtual negative damping for energy injection
• Bayesian Optimization for multi-terrain adaptation
• Individualized control preserving user kinematics