HUMAN-INSPIRED ALGEBRAIC CURVES FOR WEARABLE ROBOT CONTROL.

Area of Science:

• Robotics and Control Systems
• Biomechanics and Human Motion Analysis
• Pattern Recognition and Data Modeling

Background:

• Unified gait cycle representations are crucial for advanced wearable robot control in rehabilitation.
• Conventional methods divide the gait cycle, leading to complex, period-specific controllers.
• There is a need for a continuous, unified model of human walking gait data.

Purpose of the Study:

• To propose algebraic curves as a method for representing human walking gait data.
• To enable simplified and unified controller design for wearable robots.
• To ensure controller robustness across the entire gait cycle.

Main Methods:

• Utilized the 3L fitting algorithm to generate implicit polynomial algebraic curves from human walking data.
• Applied these curves to an impedance model of knee joint motion driven by hip angle.
• Derived conditions for satisfying a robust relative degree property throughout the gait cycle.

Main Results:

• Successfully generated algebraic curves that continuously represent human walking gait.
• Demonstrated that these algebraic curves satisfy a robust relative degree condition.
• Established the suitability of algebraic curve representations for nonlinear output tracking control.

Conclusions:

• Algebraic curves offer a unified and effective representation for human walking gait data.
• The proposed method simplifies wearable robot controller design by ensuring a consistent relative degree.
• This approach enhances the applicability of advanced nonlinear control techniques in rehabilitation robotics.