467 research outputs found
Models and Feedback Stabilization of Open Quantum Systems
At the quantum level, feedback-loops have to take into account measurement
back-action. We present here the structure of the Markovian models including
such back-action and sketch two stabilization methods: measurement-based
feedback where an open quantum system is stabilized by a classical controller;
coherent or autonomous feedback where a quantum system is stabilized by a
quantum controller with decoherence (reservoir engineering). We begin to
explain these models and methods for the photon box experiments realized in the
group of Serge Haroche (Nobel Prize 2012). We present then these models and
methods for general open quantum systems.Comment: Extended version of the paper attached to an invited conference for
the International Congress of Mathematicians in Seoul, August 13 - 21, 201
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Dissipativity analysis for discrete time-delay fuzzy neural networks with Markovian jumps
This paper is concerned with the dissipativity analysis
and design of discrete Markovian jumping neural networks with
sector-bounded nonlinear activation functions and time-varying
delays represented by Takagi–Sugeno fuzzy model. The augmented
fuzzy neural networks with Markovian jumps are first constructed
based on estimator of Luenberger observer type. Then, applying
piecewise Lyapunov–Krasovskii functional approach and stochastic
analysis technique, a sufficient condition is provided to guarantee
that the augmented fuzzy jump neural networks are stochastically
dissipative. Moreover, a less conservative criterion is established
to solve the dissipative state estimation problem by using
matrix decomposition approach. Furthermore, to reduce the computational
complexity of the algorithm, a dissipative estimator is
designed to ensure stochastic dissipativity of the error fuzzy jump
neural networks. As a special case, we have also considered the
mixed H∞ and passive analysis of fuzzy jump neural networks.
All criteria can be formulated in terms of linear matrix inequalities.
Finally, two examples are given to show the effectiveness and
potential of the new design techniques.Yingqi Zhang, Peng Shi, Ramesh K. Agarwal, and Yan Sh
Model Predictive Control for Continuous-Time Singular Jump Systems with Incomplete Transition Rates
This paper is concerned with model predictive control (MPC) problem for continuous-time Markov Jump Systems (MJSs) with incomplete transition rates and singular character. Sufficient conditions for the existence of a model predictive controller, which could optimize a quadratic cost function and guarantee that the system is piecewise regular, impulse-free, and mean square stable, are given in two cases at each sampling time. Since the MPC strategy is aggregated into continuous-time singular MJSs, a discretetime controller is employed to deal with a continuous-time plant and the cost function not only refers to the singularity but also considers the sampling period. Moreover, the feasibility of the MPC scheme and the mean square admissibility of the closed-loop system are deeply discussed by using the invariant ellipsoid. Finally, a numerical example is given to illustrate the main results
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