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Sequential Predictors For Delay Compensation For Discrete Time Systems With Time-Varying Delays, Frédéric Mazenc, Michael Malisoff, Indra Narayana Sandilya Bhogaraju
Sequential Predictors For Delay Compensation For Discrete Time Systems With Time-Varying Delays, Frédéric Mazenc, Michael Malisoff, Indra Narayana Sandilya Bhogaraju
Faculty Publications
We study time-varying linear discrete time systems with uncertainties and time-varying measurement delays, whose outputs are perturbed by uncertainty. We build sequential predictors, which ensure input-to-state stability with respect to the uncertainties and which can be constructed using output values under arbitrarily long delays. The number of required sequential predictors is any upper bound for the delay in our feedback stabilized closed loop systems. We illustrate our work in a digital control problem for a continuous time system that is discretized through sampling.
Sequential Predictors For Delay Compensation For Perturbed Discrete Time Systems, Frederic Mazenc, Michael Malisoff
Sequential Predictors For Delay Compensation For Perturbed Discrete Time Systems, Frederic Mazenc, Michael Malisoff
Faculty Publications
We provide sequential predictors for delayed time-varying discrete time linear systems with outputs, where the system and output measurements are perturbed by uncertainty. Our predictors ensure input-to-state stability with respect to uncertainties in the system and in the output, under arbitrarily long input delays. Our strategy is to introduce a number of dynamical extensions that equals the length of the input delay. Our example illustrates the usefulness of our control design.
Sampled-Data Estimator For Nonlinear Systems With Arbitrarily Fast Rate Of Convergence, Frederic Mazenc, Michael Malisoff, Silviu Iulian Niculescu
Sampled-Data Estimator For Nonlinear Systems With Arbitrarily Fast Rate Of Convergence, Frederic Mazenc, Michael Malisoff, Silviu Iulian Niculescu
Faculty Publications
We study continuous-time nonlinear systems with discrete measurements. We provide an estimate of the state variable that converges with a rate of convergence that can be made arbitrarily large by reducing the size of the largest sampling interval. Our proof of the convergence result is based on a recently developed trajectory based approach.