77,780 research outputs found
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
Robust fault detection for networked systems with distributed sensors
Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the robust fault detection problem for a class of discrete-time networked systems with distributed sensors. Since the bandwidth of the communication channel is limited, packets from different sensors may be dropped with different missing rates during the transmission. Therefore, a diagonal matrix is introduced to describe the multiple packet dropout phenomenon and the parameter uncertainties are supposed to reside in a polytope. The aim is to design a robust fault detection filter such that, for all probabilistic packet dropouts, all unknown inputs and admissible uncertain parameters, the error between the residual (generated by the fault detection filter) and the fault signal is made as small as possible. Two parameter-dependent approaches are proposed to obtain less conservative results. The existence of the desired fault detection filter can be determined from the feasibility of a set of linear matrix inequalities that can be easily solved by the efficient convex optimization method. A simulation example on a networked three-tank system is provided to illustrate the effectiveness and applicability of the proposed techniques.This work was supported by national 973 project under Grants 2009CB320602 and 2010CB731800, and the NSFC under Grants
60721003 and 60736026
An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation
In this work we design a receiver that iteratively passes soft information
between the channel estimation and data decoding stages. The receiver
incorporates sparsity-based parametric channel estimation. State-of-the-art
sparsity-based iterative receivers simplify the channel estimation problem by
restricting the multipath delays to a grid. Our receiver does not impose such a
restriction. As a result it does not suffer from the leakage effect, which
destroys sparsity. Communication at near capacity rates in high SNR requires a
large modulation order. Due to the close proximity of modulation symbols in
such systems, the grid-based approximation is of insufficient accuracy. We show
numerically that a state-of-the-art iterative receiver with grid-based sparse
channel estimation exhibits a bit-error-rate floor in the high SNR regime. On
the contrary, our receiver performs very close to the perfect channel state
information bound for all SNR values. We also demonstrate both theoretically
and numerically that parametric channel estimation works well in dense
channels, i.e., when the number of multipath components is large and each
individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin
Mathematical control of complex systems
Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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