Fault-tolerant Nonlinear MPC using Particle Filtering^*

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• 作者：Laurentz E. Olivier^* ; ^{laurentz.olivier@sasol.com" class="auth_mail" title="E-mail the corresponding author} ; Ian K. Craig^** ; ^{ian.craig@up.ac.za" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fault-tolerant ; generalized likelihood ratio ; model predictive control ; nonlinear ; particle filter
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：7
• 页码：177-182
• 全文大小：500 K

文摘

A fault-tolerant nonlinear model predictive controller (FT-NMPC) is presented in this paper. State estimates, required by the NMPC, are generated with the use of a particle filter. Faults are identified with the nonlinear generalized likelihood ratio method (NL-GLR), for which a bank of particle filters is used to generate the required fault innovations and covariance matrices. A simulated grinding mill circuit serves as the platform for illustrating the use of this fault detection and isolation (FDI) scheme along with the NMPC. The results indicate that faults can be correctly identified and compensated for in the NMPC framework to achieve optimal performance in the presence of faults.