Model-Based Fault Diagnosis Algorithms for Robotic Systems

In this paper, three model-based fault diagnosis algorithms for robotic systems are designed, compared, simulated, and implemented. The first algorithm is based on a nonlinear adaptive observer (NLAO), where sufficient condition the convergence of estimator derived in terms linear matrix inequality (LMI) under persistence excitation condition. second an extended Kalman filter (AEKF). Unlike traditional approaches, parameters considered as augmented state variables, AEKF directly estimates from measurement data. third cascade (NLO) linearized (LAKF), called exogenous (AXKF). pros cons each discussed. performance compared single-link joint robot system. Furthermore, implemented ball-balancing to detect estimate magnitude actuator faults.