Advanced optimized nonlinear control strategies for prosthetic knee joints.

Area of Science:

• Biomedical Engineering
• Control Systems Engineering

Background:

• Prosthetic knee joints are vital for restoring mobility in patients with knee osteoarthritis or injuries.
• Replicating natural knee biomechanics is key to enhancing patient quality of life and physical capabilities.

Purpose of the Study:

• To present a novel control methodology for a two-part prosthetic knee joint replacement.
• To optimize controller parameters using a genetic algorithm (GA) for improved responsiveness.
• To validate the stability and performance of advanced control techniques.

Main Methods:

• Implementation of sliding mode control (SMC) and super-twisting sliding mode control (STSMC).
• Parameter optimization via genetic algorithm (GA) with integral time absolute error objective.
• Mathematical stability analysis using Lyapunov criteria.
• Performance evaluation through MATLAB/Simulink simulations and hardware-in-loop experiments with C2000 Delfino MCU F28379D Launchpad.

Main Results:

• Demonstrated robust stability of the proposed controllers under Lyapunov criteria.
• Comparative analysis of different control strategies via simulations revealed varying efficacies.
• Real-time validation confirmed the practical applicability of the methodology.

Conclusions:

• The novel control methodology effectively manages prosthetic knee joint dynamics.
• Advanced control techniques, optimized by GA, enhance prosthetic knee performance and robustness.
• The study provides a foundation for informed prosthetic knee design and refinement.