Servo torque fault diagnosis implementation for heavy-legged robots using insufficient information

The reliability of sensors and servos is paramount in diagnosing the Heavy-Legged Robot (HLR). Servo faults stemming from mechanical wear, environmental disturbances, or electrical issues pose significant challenges to traditional diagnostic methods, which rely heavily on delicate sensors. This study introduces a framework that solely relies on joint position and permanent magnet synchronous motor (PMSM) information to mitigate dependency on fragile sensors for servo-fault diagnosis. An essential contribution involves refining a model that directly connects PMSM currents to HLR motion. Moreover, to address scenarios where actual servo outputs and HLR cylinder velocities are unavailable, an improved sliding mode observer (ISMO) is proposed. Additionally, a Fourier expansion model characterizes the relationship between operation time and fault-free disturbance in the HLR. Subsequently, the dual-line particle filter (DPF) algorithm is employed to predict fault-free disturbance. The outputs of DPF serve as a feedforward to the ISMO, enabling the real-time servo torque fault diagnosis. The accuracy and validity of this technical framework are verified through various simulations in MATLAB/SIMSCAPE and real-world experiments.