Multi-fault detection for attitude control system based on RVM regression

As satellite attitude control system (ACS) failures have concurrent and multiple features, both the sensors and actuators may occur faults. Model identification and residual evaluation were applied to ACS fault detection based on relevance vector machine (RVM) theory. RVM regression was utilized to perform offline regression modeling for the satellite's sun sensor, gyro and reaction wheel, and obtain the regression modeling through analyzing input/output history data stream. As a result, the accuracy of modeling identification was affected by regression model. A comparison between least square support vector regression (LSSVR) and RVM regression was analyzed. The simulation result shows that the RVM is much better than LSSVR. Different scenarios with sun sensor, gyro and reaction wheel to realize concurrent and multiple fault detection were simulated. The result shows that the RVM regression is convenient to the attitude control system.