This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor asynchronous sampling systems with correlated noises. The state updates uniformly and the sensors sample randomly. Based on the measurement augmentation method, the asynchronous sampling system is transformed to the synchronous sampling one. Local filter is designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrix between any two local filters is derived. Finally, the optimal distributed fusion filter is proposed by using matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]
This paper proposes a procedure of synthetic detection for the location of a change point and outliers in bilinear time series models with a change after an unknown time point. Based on Bayesian framework, we first derive the conditional posterior distribution of the change point and from that distribution estimate the position of the change point. Then we use these results to detect the outliers in the time series before and after that change point via Gibbs sampler algorithm. Our simulation studies show that the proposed procedure is effective. [ABSTRACT FROM PUBLISHER]
This paper addresses a method of nonlinear controller construction based on a model with a state-dependent representation for a nonlinear system. In this method, the controller construction and generation of manipulated values are separated. A nonlinear system and its controller are firstly expressed by the coefficients of the state-dependent representation without any approximation. At the stage of controller implementation for the nonlinear system, the manipulated values are calculated by means of an algorithm off the numerical analysis. The properties of the proposed method are analysed. With the analytical considerations and simulation studies, the proposed method is compared with several nonlinear control methods, such as exact linearization method and the linear approximation method, and its merits are verified. [ABSTRACT FROM AUTHOR]