Multilayer State Predictors for Nonlinear Systems with Time-Varying Measurement Delays
In this paper, we propose state predictors for stable genuinely nonlinear systems
with time-varying measurement delay, with no restriction on its bound or serious limitations on the
growth of the nonlinearities. The measurement delay is assumed to be continuous. A state prediction
is generated by chains of nonlinear dynamic observers operating at different layers. On each layer,
these observers reconstruct the unmeasurable state vector at different delayed time-instants, which
partition the maximal variation interval of the time-varying delay. This partition determines the
number of observers in the layer. Transitions from a layer to the next one are triggered by an online
estimate of the magnitude of the state. Consistently, in passing to the next layer the partition is
refined and the number of observers increased. In this sense, our predictor is nonlinear and adaptive.