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…
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