An approach to out-of-sequence measurements in feedback control systems

Complex communication network architectures are becoming more frequent in control applications. In such networked control systems, it is often the case that information on process variables is received out-of-time-order. This paper presents a Bayesian approach to handling this out-of-sequence information problem. Such approach leads to a solution involving the joint probability density of current state and past measurements not yet received. Under linear Gaussian assumptions, the Bayesian solution reduces to an augmented state Kalman filter. Our approach augments the state dynamically based on the list of missing observations. As this solution can be time and memory consuming, two simplified implementations of the algorithm are presented.