Robust adaptive fault detection and diagnosis observer design for a class of nonlinear systems with uncertainty and unknown time-varying internal delay

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.

Dynamic event-triggered fuzzy non-fragile control of DC microgrids

This paper studies the event-triggered fuzzy non-fragile control of uncertain DC microgrids subject to false data injection (FDI) attacks, controller saturation, network delays and premise mismatching. Firstly, a dynamic event-triggered mechanism (ETM) is proposed, which can save more communication bandwidth than the static ETMs, and remove the complex Zeno-free computation required by the continuous-time ETMs. Secondly, a fuzzy time-delay closed-loop system model is established, which provides a unified framework to study the effects of the dynamic ETM, FDI attacks, uncertainties, saturation, delays and premise mismatching. Thirdly, mean-square exponential stability criteria are established, and co-design method for the saturated fuzzy non-fragile (SFNF) controller and the dynamic ETM is presented. Simulation results confirm that the SFNF controller can stabilize the unstable DC microgrid, while the dynamic ETM significantly reduces the triggering rate by 84.98%. Comparisons show that the proposed controller performs better than the non-fragile controller, fuzzy controller and robust linear controller, and the dynamic ETM achieves a lower triggering rate than the static ETMs.