Online performance monitoring and diagnosis based on RTSID and KPLS

In the case that the control system performance is detected in poor state, it is desirable that the underlying cause can be found and the information can be used for the adjustment and recovery. Developing a recursive two-stage identification (RTSID) algorithm and a recursive control performance ass...

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Bibliographic Details
Main Authors: Wang Jian-Guo, Wang Jia-Long, Zhao Jing-Hui, Ma Shi-Wei, Rao Wen-Tao, Zhang Yong-Jie
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Closed loop systems
closed-loop
Conferences
Control performance monitoring
Control systems
Covariance matrices
Diagnosis
Kernel
Least squares method
Monitoring
Online
Process control
Recursive
Strategy
System identification
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Description
Summary:In the case that the control system performance is detected in poor state, it is desirable that the underlying cause can be found and the information can be used for the adjustment and recovery. Developing a recursive two-stage identification (RTSID) algorithm and a recursive control performance assessment algorithm with kernel partial least square (KPLS), then integrating the proposed identification algorithm into the performance diagnosis, this paper presents an online control performance monitoring and diagnosis strategy for the closed-loop system. The proposed algorithms are applied to the typical research example and the effectiveness of the strategy is validated.
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2015.7259960