Simultaneous State and Parameter Estimation Using Robust Receding-Horizon Nonlinear Kalman Filter

Abstract

Use of measurements corrupted with gross errors for state estimation leads to biased state estimates, which, in turn, deteriorates the performances of model based process monitoring and control schemes. In this work, with the aim of minimizing the effects of gross errors on state estimates, robust versions of receding horizon nonlinear Kalman filter (RNK) are developed by integrating M-estimators with RNK. Two M-estimators, viz. Huber's fair function and Hampel's redescending estimator, have been considered for incorporating robustness in RNK. The proposed robust RNK formulation is further used to develop a robust simultaneous state and parameter estimation scheme. The efficacies of the proposed estimation schemes have been demonstrated by conducting simulation studies on a benchmark continuous stirred tank reactor (CSTR) system. The simulation studies reveal that, the proposed robust RNK formulations are able to generate estimation performances comparable to that of robust moving horizon estimation (MHE) scheme. Further, the proposed robust RNK based state and parameter estimator is able to estimate drifting model parameter accurately even in the presence of biased temperature measurements.