Event-Based Input and State Estimation for Linear Discrete Time-Varying Systems
In this paper, the joint input and state estimation problem is considered for linear discrete-time stochastic systems. An event-based transmission scheme is proposed with which the current measurement is released to the estimator only when the difference from the previously transmitted one is greater than a prescribed threshold. The purpose of this paper is to design an event-based recursive input and state estimator such that the estimation error covariances have guaranteed upper bounds at all times. The estimator gains are calculated by solving two constrained optimisation problems and the upper bounds of the estimation error covariances are obtained in form of the solution to Riccati-like difference equations. Special efforts are made on the choices of appropriate scalar parameter sequences in order to reduce the upper bounds. In the special case of linear time-invariant system, sufficient conditions are acquired under which the upper bound of the error covariance of the state estimation is asymptomatically bounded. Numerical simulations are conducted to illustrate the effectiveness of the proposed estimation algorithm.
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Citation : Hu, L. et al. (2017) Event-based input and state estimation for linear discrete time-varying systems. International Journal of Control, 91(1), pp. 101-113.
ISSN : 0020-7179
Research Group : DIGITS
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes