Barrier Lyapunov function based adaptive fuzzy control for a PAMs-driven rehabilitation exoskeleton robot

Exoskeleton robot has been widely utilized in upper limb rehabilitation trainings. Among the actuation methods, the pneumatic artificial muscle (PAM) has advantages in inherent flexibility, but at the same time, its strong nonlinearity and the complex dynamics of the exoskeleton robot make it difficult to achieve precise tracking control of the system. To address this, this paper proposes an adaptive fuzzy control scheme based on barrier Lyapunov function. By designing an error transformation mechanism and a performance function, the tracking error of the system is always constrained within the preset ranges. Fuzzy logic is used to approximate the unknown nonlinear terms of the system, and a new adaptive fuzzy controller is constructed through backstepping. The proposed method guarantees that all states of the closed-loop system remain bounded and its effectiveness is validated through a series of experiments.