Closed loop iterative learning control for consistency tracking in lower limb rehabilitation robotic system with...

Area of Science:

• Robotics
• Control Systems Engineering
• Biomedical Engineering

Background:

• Consensus tracking control is vital for Lower Limb Rehabilitation Robotic Systems (LLRRS) to ensure state variables converge.
• Initial state deviations in LLRRS pose challenges for achieving accurate motion tracking.

Purpose of the Study:

• To address motion tracking control issues in LLRRS with initial state deviations.
• To design and verify a controller capable of achieving consensus tracking for LLRRS state variables.

Main Methods:

• A dynamic mathematical model of the LLRRS was established.
• A closed-loop PD-type accelerated iterative learning controller with initial state learning was designed using output measurements and a variable learning gain.
• Mathematical analysis, simulation, and experimental prototype testing were employed for verification.

Main Results:

• The proposed controller demonstrated applicability for consensus tracking control of LLRRS state variables.
• Experimental results showed maximum tracking errors of 7.14° for the hip joint angle and 5.74° for the knee joint angle.
• The algorithm's feasibility and effectiveness were corroborated through prototype testing.

Conclusions:

• The developed controller effectively achieves consensus tracking control for LLRRS, even with initial state deviations.
• The controller utilizes system output measurements and a variable learning gain, simplifying implementation.
• The study validates the proposed algorithm's performance in real-world rehabilitation scenarios.