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Precoding for the downlink of a multibeam satellite system has been recently shown, under ideal conditions, to be promising technique towards employing aggressive frequency reuse gainfully. However, time varying phase uncertainties imposed by the components and the channel, combined with delayed feedback perturbs the channel state information at the transmitter (CSIT). In this paper, we consider a...
In this work, we study the design of a precoder on the user downlink of a multibeam satellite channel. The variations in channel due to phase noise introduced by on-board oscillators and the long round trip delay result in outdated channel information at the transmitter. The phase uncertainty is modelled and a robust design framework is formulated based on availability and power constraints. The optimization...
This paper is concerned with the problem of designing a robust state-feedback stabilizer for continuous-time Markov jump linear systems with time varying transition rates, i.e. for a non-homogeneous Markov jump linear system. Transition rates are assumed to be piecewise constant, so the Markov jump linear system is piecewise homogeneous in fact. The system is subject to mode-dependent, time varying...
In this paper an optimal robust controller, in terms of structured singular value- μ minimizing is designed for spin stabilized seeker, for achieving proper robust stability and robust performance with respect to the multiplicative input diagonal uncertainty. This optimal robust control can be designed using DK-iteration. This optimality involves solving a sequence of scaled H∞ problems. We do the...
In this paper the new one-degree and two-degree of freedom H∞ loop shaping robust controllers are designed for a seeker scan loop, which has model uncertainty and subjects to external & internal disturbances. Both one-degree and two-degree of freedom H∞ loop shaping are constructed of two main stages; first, the nominal plant is dynamically diagonalized and shaped by pre- and post-compensators...
We present a robust probabilistic method to classify targets based on their tracks. As is customary in supervised learning problems, it is assumed that example tracks from various classes are available to train a classifier. We present an optimal but computationally intensive sequential solution, and show that a computationally feasible naive Bayes approximation works better than ignoring sequential...
Design of controllers for uncertain systems is inherently paradoxical. Adaptive control approaches claim to adapt system parameters against uncertainties, but only if these uncertainties change slowly enough. Alternatively, robust control methodologies claim to ensure system stability against uncertainties, but only if these uncertainties remain within known bounds. This is while, in reality, disturbances...
The aim of this paper is robust identification of a lightly damped flexible beam model with parametric and nonparametric uncertainties. Our approach is based on worst case estimation theory where uncertainties are assumed to be unknown but bounded. We examine different outbounding algorithms (parallelotopic and ellipsoidal) for estimation of the feasible parameter set that has been delivered by the...
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