A Robust Strategy for Sensor Fault Reconstruction of Lower Limb Rehabilitation Exoskeleton Robots

Ensuring the reliability and stability of lower limb rehabilitation exoskeleton robots during rehabilitation training is of paramount importance. Sensor faults in such systems can degrade overall performance and may even pose significant safety hazards. Consequently, the effective reconstruction of sensor faults has become a critical challenge in ensuring the safe and dependable operation of lower limb rehabilitation exoskeleton robots. This paper presents a novel sensor fault reconstruction method for systems subject to unknown external disturbances. Initially, an equivalent input disturbance (EID) approach based on an improved sliding mode observer is developed to mitigate the adverse effects of disturbances on the fault reconstruction process. Subsequently, a novel high-order sliding mode observer (NHSMO) is proposed to accurately reconstruct sensor faults. In contrast to conventional sliding mode observers, the proposed NHSMO guarantees finite-time convergence of the observation error, thereby enhancing both estimation accuracy and robustness. The effectiveness of this method is validated through both simulation and experimental results, demonstrating its superior fault reconstruction capabilities and strong resilience to external disturbances.