Artificial evolution based cost-reference particle filter for nonlinear state and parameter estimation in process systems with unknown noise statistics and model parameters

Abstract

The cost-reference particle filter (CRPF) is a variant of the particle filter (PF). It is simpler, more robust, and more flexible than the standard PF. Particularly, it does not require any statistical information on both state noises and observation noises in its application. However, in order to successfully apply CRPF to the nonlinear state estimation in dynamic process systems, the knowledge of the model parameters should be known a priori. The standard CRPF cannot handle the problem of state and parameter estimation (SPE) with unknown noise statistics and model parameters. To eliminate the above limitation, this paper proposes an evolution algorithm for the SPE in nonlinear dynamic process systems. The algorithm is the combination of the artificial evolution (AE) with CRPF, called AE-CRPF, to estimate the states and the parameters simultaneously. The proposed AE-CRPF is applied to two nonlinear dynamic process systems for practical applications. The results demonstrate the effectiveness and robustness of the proposed method.