Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives.

Area of Science:

• Robotics
• Biomechanics
• Control Systems

Background:

• Exoskeleton active walking assistance requires accurate human joint torque estimation.
• Low-pass filtering of joint trajectories, necessary for smoothing, introduces phase delays.
• These phase delays significantly degrade the performance of exoskeleton assistance.

Purpose of the Study:

• To introduce a novel exoskeleton active walking assistance control framework.
• To address the performance limitations caused by phase delays in human joint trajectory data.
• To improve the effectiveness of exoskeleton-assisted walking.

Main Methods:

• Development of frequency adaptive dynamics movement primitives (FADMPs) as an online learning and prediction algorithm.
• Implementation of a model-based controller relying on human joint torque estimation.
• Compensation for phase delay introduced by low-pass filtering of joint motion trajectories.

Main Results:

• FADMPs can online estimate fundamental frequency, learn, and predict human joint trajectories.
• Simulations and experiments demonstrate that phase delay negatively impacts exoskeleton assistance.
• The proposed FADMP-based framework successfully compensates for phase delay, improving assistance performance.

Conclusions:

• The FADMP-based control framework offers a significant improvement in exoskeleton active walking assistance.
• Compensating for phase delay is crucial for optimizing exoskeleton performance.
• This approach enhances the effectiveness and reliability of robotic walking assistance systems.