A Comparsion Of State Estimation Algorithms For Hybrid Systems

The dynamic nature of hybrid systems involves discrete switching behavior between several operating modes and continuous plant dynamics governed by continuous models in each mode. State estimation is a important class of approaches for online monitoring and diagnosis of hybrid systems, which relies on the estimation of unknown variables using a filtering approach. Focused hybrid estimation methods concentrate on most likely system evolution trajectories based on probabilistic and best-first enumeration. On the other hand, switched Dynamic Bayesian Networks-based particle filter methods track the continuous behavior in individual modes of operation, and switch modes when transition conditions are met. In this paper, we study and compare these two algorithms. The theoretical analysis and experimental results show the advantages and disadvantages of both approaches.