Simultaneous data reconciliation and gross error detection for dynamic systems using particle filter and measurement test

Abstract

Good dynamic model estimation plays an important role for both feedforward and feedback control, fault detection, and system optimization. Attempts to successfully implement model estimators are often hindered by severe process nonlinearities, complicated state constraints, systematic modeling errors, unmeasurable perturbations, and irregular measurements with possibly abnormal behaviors. Thus, simultaneous data reconciliation and gross error detection (DRGED) for dynamic systems are fundamental and important. In this research, a novel particle filter (PF) algorithm based on the measurement test (MT) is used to solve the dynamic DRGED problem, called PFMT-DRGED. This strategy can effectively solve the DRGED problem through measurements that contain gross errors in the nonlinear dynamic process systems. The performance of PFMT-DRGED is demonstrated through the results of two statistical performance indices in a classical nonlinear dynamic system. The effectiveness of the proposed PFMT-DRGED applied to a nonlinear dynamic system and a large scale polymerization process is illustrated.