Event-triggered extended Kalman filter for nonlinear multi-rate systems

In this paper, the event-triggered filtering problem is investigated for a class of nonlinear multi-rate systems. The nonlinearity is represented by a linear form with a norm-bounded uncertainty. In order to deal with the uncertainty resulting from the nonlinearities, a new augmentation approach is proposed to transform the multi-rate nonlinear system into a single-rate system. Based on the measurement outputs according to the event-triggering mechanism, an extended Kalman filter is adopted to estimate the systems state. Firstly, the upper bounds of the filtering error covariance are obtained at each sampling instant. Then, by recurring to the Lagrange multiplier method, the filter gains are obtained by minimizing the upper bound of the filtering error covariance. Finally, a simulation example is given to verify the effectiveness of the proposed filtering approach.