Dynamic data reconciliation based on robust estimation and particle filters

Lili Wang, Chunxiang Huang, Wen Jing, Lixiang Sun


Since process measurement data inevitably contain random or gross errors, it requires reconciliation. Dynamic data reconciliation methods have limitations in handling. Based on the robust estimation principle, a new robust estimation function is proposed in this paper. With its advantages in mathematic concepts and parameter adjustment, it can reconcile and detect random and gross errors simultaneously. The dynamic data correction method for particle filter has the problem of particle shortage. The combination of robust estimation and particle filter is used to update the particle weight by means of a robust function. Moreover, the particle filter can increase the particle confidence and avoid the degradation of particles in the dynamic processes. The efficiency of the proposed approach is proved by the results of simulation performed on CSTR system.

Ключові слова

dynamic data reconciliation; robust estimation; particle filter; robust estimation function; process model CSTR

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DOI: https://doi.org/10.15589/SMI20170206