Research on adaptive robust control of lower limb rehabilitation exoskeleton

Abstract

In order to improve the trajectory tracking accuracy of the lower limb rehabilitation exoskeleton robot, this paper proposes an adaptive robust error compensation control method (ARCEC) based on RBF neural network. First, the dynamics of the single-leg swing phase of the lower-limb exoskeleton are modeled using Lagrange’s equations, taking into account factors such as joint friction, flexible transmission, and the torque arising from human–robot interaction. Subsequently, nominal model compensation, online approximation via RBF neural networks, and nonlinear error feedback are integrated to mitigate the impact of model uncertainty and external disturbances on trajectory tracking performance. Simulation and experimental results demonstrate that compared to traditional PID control and sliding mode control (SMC), the ARCEC method exhibits significant advantages in trajectory tracking accuracy. It achieves up to a 30% reduction in tracking error, enabling the lower-limb exoskeleton to precisely track human gait curves.