We consider the practical design of linear controllers to meet a given set of H-2 specifications. The Q-parametrization reduces the problem to a quadratic minimization subject to multiple quadratic constraints, which ...
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We consider the practical design of linear controllers to meet a given set of H-2 specifications. The Q-parametrization reduces the problem to a quadratic minimization subject to multiple quadratic constraints, which we solve using semi-definite programming (SDP) methods. Each SDP iteration requires calculating a primal and dual search direction and minimizing the cost function along the plane defined by these search directions. The primal direction requires solving a least squares problem whose normal equation matrix is composed of a block-Toeplitz portion plus other structured matrices. We make use of Kronecker products and FFT's to greatly reduce the calculation. The dual search direction and plane search are accelerated by low-rank representations of the SDP structured matrices. As an example, we design controllers which explore the optimal tradeoff between in-band residual and out-of-band enhancement of acoustic radiation from a (mathematically modeled) submerged spherical shell, while simultaneously constraining two sensitivity measures. For this example we show that significant reduction in out-of-band enhancement is possible with only minor in-band penalties.
This paper describes some fundamental contributions to the theory and applicability of optimal bounding ellipsoid (OBE) algorithms for signalprocessing. All reported OBE algorithms are placed in a general framework w...
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This paper describes some fundamental contributions to the theory and applicability of optimal bounding ellipsoid (OBE) algorithms for signalprocessing. All reported OBE algorithms are placed in a general framework which fruitfully demonstrates the relationship between the set-membership principles and least square error identification. Within this framework, flexible measures for adding explicit adaptation capability are formulated and demonstrated through simulation. Computational complexity analysis of OBE algorithms reveals that they are of O (m2) complexity per data sample with m the number of parameters identified, in spite of their well-known propensity toward highly selective updating. Two very different approaches are described for rendering a specific OBE algorithm, the set-membership weighted recursive least squares algorithm, of O (m) complexity. The first approach involves an algorithmic solution in which a suboptimal test for innovation is employed. The performance is demonstrated through simulation. The second method is an architectural approach in which complexity is reduced through parallel computation.
作者:
Bishop, Adrian N.
NICTA Control and Signal Processing Group Canberra Research Laboratory Australia
A distributed control law for quadrilateral (four-agent) formation control with bearing-only measurements and relative pair-wise inter-agent angle constraints is introduced. The control law is weak in the sense that e...
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Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. The detection of these impulses can be useful for fault diagnosis purposes. Recently there has been an incre...
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Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. The detection of these impulses can be useful for fault diagnosis purposes. Recently there has been an increasing trend towards the use of higher-order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. This paper considers the use of the third- and fourth-order Wigner moment spectra, called the Wigner bi- and tri-spectra respectively, for analysing such signals. Expressions for the auto- and cross-terms in these distributions are presented and discussed. It is shown that the Wigner trispectrum is a more suitable analysis tool and its performance is compared to its second-order counterpart for detecting impulsive signals. These methods are also applied to measured data sets from a car engine and an industrial gearbox. (C) 1997 Academic Press Limited.
Independent Component Analysis (ICA) methods are being increasingly applied to the analysis of electromagnetic (EM) brain signals. However, these powerful techniques still generally require subjective a posteriori ana...
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Independent Component Analysis (ICA) methods are being increasingly applied to the analysis of electromagnetic (EM) brain signals. However, these powerful techniques still generally require subjective a posteriori analysis in order to visualise neurophysiologically meaningful components in the outputs. Standard implementations of ICA are restrictive mainly due to the square mixing assumption (i.e., as many sources as measurement channels) - this is especially so with large multichannel recordings. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought;as in tracking the changing scalp topographies of rhythmic activities. Through constraining the ICA solution it is possible to extract signals that are statistically independent, yet which are similar to some reference signal which incorporates the a priori information. We demonstrate this method on a multichannel recording of an epileptiform electroencephalogram (EEG), where we automate the repeated simultaneous extraction of both rhythmic seizure activity, as well as alpha-band activity, over an epoch of EEG. Subjective analysis of the results shows scalp topographies with realistic spatial distributions which conform to our neurophysiologic expectations. This work shows that constraining ICA can be a very useful technique, especially in automated systems and we demonstrate that this can be successfully applied to EM brain signal analysis.
State estimation in critical networked infrastructure such as the transportation and electricity (smart grid) networks is becoming increasingly important. Consequently, the security of state estimation algorithms has ...
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Within a dynamical embedding (DE) framework it is possible to extract information on multiple-sources underlying just a single channel recording of electromagnetic brain activity. Independent Component Analysis (ICA) ...
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This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image processing and Artificial Neural Networks. The aim is to develop a fast and effective...
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ISBN:
(纸本)9781457702556
This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits;i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study.
In this work we present a technique for applying Blind Source Separation (BSS) to single channel recordings of Electromagnetic (EM) brain signals. Single channel recordings of brain signals are preprocessed through th...
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Locally linear embedding (LLE) is a method for nonlinear dimensionality reduction, which calculates a low dimensional embedding with the property that nearby points in the high dimensional space remain nearby and simi...
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