Stability Optimization of Lower-Limb Exoskeleton Robots Based on Fuzzy Sliding Mode Control

With the growing application of lower-limb exoskeleton robots in medical rehabilitation and human movement assistance, achieving high-precision trajectory tracking while rejecting external disturbances has become a key issue. Existing control methods often struggle with unmodeled human dynamics and model uncertainties. While sliding mode control (SMC) offers excellent robustness against these challenges, it typically suffers from the chattering phenomenon. Therefore, this paper focuses on the optimization of robust trajectory tracking for lower-limb exoskeleton robots using fuzzy sliding mode control (FSMC). A mathematical model of the driving joints is established by using a brushless DC motor, and the transfer function of the motor's angular position is derived. Subsequently, a sliding mode controller is designed, and simulation experiments are conducted using step signals, walking, and leg-lifting trajectory inputs. Finally, fuzzy logic is introduced to dynamically adjust the switching gain of the SMC, effectively mitigating chattering and enhancing the exoskeleton's tracking accuracy under human-robot interaction disturbances. This study provides a reliable control scheme for the practical application of lower-limb exoskeletons by addressing system robustness and trajectory tracking precision.