Funnel control based on unknown system dynamics estimator for hydraulically-driven lower limb exoskeleton robot

Area of Science:

• Robotics
• Control Systems Engineering
• Biomechanics

Background:

• Hydraulically-driven lower limb exoskeleton robots (HDLLERs) enhance human mobility but face challenges in precise control.
• Human-robot coupling and system uncertainties hinder accurate trajectory tracking.

Purpose of the Study:

• To develop a robust control strategy for HDLLERs that addresses unknown dynamics and external disturbances.
• To improve the trajectory tracking precision and overall performance of lower limb exoskeleton robots.

Main Methods:

• A novel funnel control (FC) strategy integrated with an unknown system dynamics estimator (USDE) was proposed.
• The HDLLER dynamic model was transformed into a Brunovsky canonical form to manage complexity.
• High gain observers (HGO) were used for state reconstruction, and a modified funnel function constrained tracking errors.

Main Results:

• The proposed FC-USDE strategy effectively compensated for unknown internal and external dynamic influences.
• Simulations and experiments demonstrated improved transient and steady-state performances in trajectory tracking.
• The method successfully managed the 'explosion of complexity' in high-order systems.

Conclusions:

• The developed funnel control strategy with an unknown system dynamics estimator offers a robust solution for precise trajectory tracking in HDLLERs.
• The approach enhances exoskeleton robot performance by mitigating uncertainties and disturbances.
• Validated through simulations and gait experiments, the method shows significant potential for exoskeleton applications.