Practical issues in state estimation using particle filters: Case studies with polymer reactors

In this paper, we compare Kalman update based filters with particle filters using simulations on polymerization processes. In particular, we compare the unscented Kalman filter (UKF) and the particle filter (PF) for the case of significant plant–model mismatch. The sequential importance resampling particle filter is shown to be less robust than the Kalman update-based filters. This issue is solved by bootstrapping the PF with the UKF, i.e., using the UKF as the proposal distribution; this retains its ability to estimate non-Gaussian distributions while providing robustness with respect to plant–model mismatch. Finally, we explore methods of obtaining a point estimate from the state distributions of the PF.

► We compare estimators for polymerization processes under various conditions.
► Particle filters are not very robust to structural plant–model mismatch.
► Bootstrapped (unscented) particle filters are robust to plant–model mismatch.
► Clustering can provide a method for extracting point estimates in particle filters.