Using Sequential Kalman Filters for State Estimation of Nonlinear Systems

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Modal series is a new approach for modeling and analysis of nonlinear systems. This paper provides application of modal series to state estimation of nonlinear systems and introduces a new state estimation approach for nonlinear systems which uses a modal series model of nonlinear systems for Kalman filtering. The method implies a sequential use of Kalman filters which each one tries to decrease estimation errors of states. To validate the proposed approach, results of simulation of LQG control of a cart and pole using proposed approach has been compared with classical LQG control.