Methodology of Multi-group Particle Filter for Robust State Estimation in Nonlinear Dynamic Process Systems

State estimation plays an important role for both process control and fault detection. In this paper, a methodology of multi-group particle filter is proposed for the uncertainty problem of state initialization in the nonlinear process systems. The measurement test criterion is introduced to indirectly identify whether the state initialization is accurate. According to the result of identification, multi-group particle filter is selected to generate the initial particles under bad state initialization, which can increase the probability of generating correct initial particles. The rectified errors of observed variables are used for the selection of the optimal particles. Finally, reliable state estimation would be derived through iterative particles. The proposed methodology of multi-group particle filter is compared with the generic particle filtering method through two examples of nonlinear dynamic systems. The results demonstrate the effectiveness and robustness of the proposed methodology.