Performance Monitoring of High Speed Train Predictive Controller Based on Subspace LQG

In view of the performance degradation of subspace predictive controller of high-speed train in complex and changeable environment, a performance monitoring algorithm of train predictive controller based on subspace linear quadratic Gaussian (LQG) benchmark is proposed. Firstly, the performance benchmark based on LQG is designed by the subspace matrix, which can be obtained during using subspace identification to process the historical train operation data. By solving the real-time performance index of the train online and then comparing with the established performance benchmark, the evaluation index of the train is obtained, and the train predictive controller can be evaluated on line. Then, when the evaluation result is degradation, it needs to diagnose the concrete type, that is, to establish the performance degradation mode database of controller, and a classifier based on support vector machines is designed to train and study the four performance degradation sources, which are noise variance change, process model mismatch, output constraint saturation and control parameter setting improperly. The accuracy of the test set input to the classifier is 95.63%, 92.49%, 90.52% and 97.56%, which shows that the classifier has high reliability and accuracy. © 2021, Editorial Department of JVMD. All right reserved.