Iterative Learning Robust PD-SDRE Control for Active Transfemoral Prostheses

Abstract

In this paper, we present a novel control strategy for active prosthetic legs. The approach uses an intelligent robust Proportional-Derivative State-Dependent Riccati Equation controller to reduce the use of biomechanical energy, enhance performance and robustness. We include an Iterative Learning Control algorithm, to minimise control errors and allow the controller gains to adapt over time, and robust Sliding Mode Control to specifically address potential parametric and non-parametric uncertainties, disturbances, and noise. We conduct tests to demonstrate that the proposed controller not only maintains stability but also outperforms existing methods in terms of energy efficiency and tracking. Application of the proposed method in simulations shows significant improvements when compared to other methods from the literature, with up to 98.3% reduction in position tracking error and up to 91.9% reduction in control cost. Furthermore, for angular tracking of the hip and knee, improvements of up to 32.6% and 44.9%, along with torque reductions of up to 67.5% and 87.5%, are observed. This study represents a step forward in providing an effective solution for controlling active prosthetic devices.