Advanced optimized nonlinear control strategies for prosthetic knee joints.

Abstract

Prosthetic knee joints are at the forefront of medical innovation, serving as crucial tools in restoring mobility and enhancing the quality of life for individuals grappling with knee-related ailments like osteoarthritis and injuries. By faithfully replicating the intricate biomechanics of the natural knee, these devices empower recipients to regain lost physical capabilities and lead active, fulfilling lives. This paper presents a novel methodology employing advanced control techniques, including sliding mode control (SMC) and super-twisting sliding mode control (STSMC), to explore lower limb dynamics and effectively manage a two-part knee joint replacement. Through meticulous parameter optimization using a genetic algorithm (GA), guided by the integral time absolute error as the optimization objective, the controllers are finely tuned to maximize performance and responsiveness in real-world scenarios. The stability of the proposed controllers is thoroughly validated using mathematical analysis based on Lyapunov stability criteria. This ensures they perform robustly and can withstand disturbances. Comprehensive performance evaluations conducted via MATLAB/Simulink simulations offer valuable insights into the comparative efficacy of different control strategies under varying conditions, facilitating informed decision-making and refinement of prosthetic knee design. Real-time validation of the proposed methodology is achieved through a hardware-in-loop experimental setup featuring the advanced C2000 Delfino MCU F28379D Launchpad.