Adaptive super-twisting sliding mode control for robust tracking of wearable PAM-driven robotic manipulator

Abstract

Pneumatic Artificial Muscle (PAM)-actuated rehabilitation robots provide a safe and compliant alternative to traditional rigid motor-driven systems, mimicking the inherent viscoelasticity of human muscle. However, precise trajectory tracking is a difficult control problem due to their highly nonlinear pressure-force-contraction relationship, multibody leg dynamics, friction effects, and external disturbances. This paper proposes an Adaptive Finite Time Super-Twisting Sliding Mode Control (ASTSMC) strategy for robust tracking of a leg rehabilitation manipulator capable of performing hip/knee flexion-extension exercises. The human leg is represented as a planar 2-DOF system that includes the hip and knee joints. The Euler-Lagrange formulation is used to derive a comprehensive nonlinear dynamic model that includes viscous-Coulomb friction and antagonistic PAM torque generation. The controller employs a second-order sliding mode law to ensure a continuous control signal, significantly suppressing high-frequency oscillations. Additionally, in order to manage unknown lumped uncertainties without knowing the upper bounds beforehand, an adaptive gain law is integrated. Finite-time convergence of the tracking errors is confirmed by stability analysis using the Lyapunov method. Simulation results in MATLAB/Simulink validate that the proposed ASTSMC achieves superior trajectory tracking accuracy and robustness against parameter variations compared to standard sliding mode control schemes. The substantial reduction in chattering improves patient comfort and safety, proving that the proposed framework could be applied for advanced robotic neurorehabilitation in clinical settings.