Model-based Robust Fault Diagnosis for Satellite Control Systems Using Learning and Sliding Mode Approaches

2009 ◽  
Vol 4 (10) ◽  
Author(s):  
Qing Wu ◽  
Mehrdad Saif
2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Pedro La Hera ◽  
Daniel Ortíz Morales

Model-based methods are used in industry for prototyping concepts based on mathematical models. With our forest industry partners, we have established a model-based workflow for rapid development of motion control systems for forestry cranes. Applying this working method, we can verify control algorithms, both theoretically and practically. This paper is an example of this workflow and presents four topics related to the application of nonlinear control theory. The first topic presents the system of differential equations describing the motion dynamics. The second topic presents nonlinear control laws formulated according to sliding mode control theory. The third topic presents a procedure for model calibration and control tuning that are a prerequisite to realize experimental tests. The fourth topic presents the results of tests performed on an experimental crane specifically equipped for these tasks. Results of these studies show the advantages and disadvantages of these control algorithms, and they highlight their performance in terms of robustness and smoothness.


2012 ◽  
Vol 457-458 ◽  
pp. 1070-1076 ◽  
Author(s):  
Fei Yan ◽  
Ming Jian Li

Based on the traditional method of analytical redundancy fault diagnosis, the advanced machine learning technology is combined with the model-based fault diagnosis so as to form a new intelligent approach to the fault diagnosis for satellite control systems. The support vector regression technique in statistical learning theory is employed to model the control system with a little sampling data firstly. Then the feasibility of detecting and identifying faults for the satellite attitude control system with the Mahalanobis distance is analyzed in detail. Finally a set of fault-detection observers are designed and implemented based on the residual evaluation. The simulation result indicates that the diagnosing method proposed in this paper is characterized with light computation burden and good real-time performance.


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