Robust Adaptive Control of the Nonlinearly Parameterized Human Shank Dynamics for Electrical Stimulation Applications

Abstract

In this paper, we introduce two robust adaptive controllers for the human shank motion tracking problem that is inherent in neuromuscular electrical stimulation (NMES) systems. The control laws adaptively compensate for the unknown parameters that appear nonlinearly in the musculoskeletal dynamics while providing robustness to additive disturbance torques. The adaptive schemes exploit the Lipschitzian and/or the concave/convex parameterizations of the model functions. The resulting control laws are continuous and guarantee practical tracking for the shank angular position. The performance of the two robust adaptive controllers is demonstrated via simulations.