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 a sufficient condition for the convergence of the estimator is derived in terms of linear ma...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Hasan, Agus, Tahavori, Maryamsadat, Midtiby, Henrik Skov
Format: Article
Language:English
Subjects:
Actuators
Algorithms
Estimation
Extended Kalman filter
Fault detection
Fault diagnosis
Heuristic algorithms
Kalman filter
Kalman filters
Linear matrix inequalities
Mathematical models
Noise measurement
nonlinear observer
Observers
Parameters
robotics
Robots
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Summary: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 a sufficient condition for the convergence of the estimator is derived in terms of linear matrix inequality (LMI) under persistence of excitation condition. The second algorithm is based on an adaptive extended Kalman filter (AEKF). Unlike traditional approaches, where the fault parameters are considered as augmented state variables, the AEKF directly estimates the fault parameters from measurement data. The third algorithm is based on a cascade of a nonlinear observer (NLO) and a linearized adaptive Kalman filter (LAKF), called the adaptive exogenous Kalman filter (AXKF). The pros and cons for each algorithm are discussed. The performance of the algorithms is compared in a single-link joint robot system. Furthermore, the algorithms are implemented in a ball-balancing robot to detect and estimate the magnitude of the actuator faults.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3233672