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In this paper, the problem of connectivity assessment for an underwater random sensor network is investigated. The weighted vertex connectivity is introduced as a metric to evaluate the connectivity of the weighted expected graph of a random sensor network where the elements of the weight matrix represent the operational probability of their corresponding communication links. The proposed weighted...
This paper presents an overview of a learning methodology for detecting and diagnosing faults in nonlinear dynamic systems. The main idea behind this approach is to monitor the plant for any off-nominal behavior due to faults utilizing on-line approximators. In the presence of a failure, the on-line approximator can be used as an estimate of the nonlinear fault function for fault diagnosis purposes...
It is shown that for the case of minimum phase stable plants there exists, among the set of stabilizers controllers, a solution, simultaneously decoupling the outputs and at-tenuating the perturbations of the system. At the same time, the dynamics is assigned to stable positions. The at-tenuation is given by an approximation of the controllers rejecting the perturbations, which are singulars. These...
In this paper a PI decentralized constant-volume controller for open-channels is designed by minimizing the H2 norm of a suitable transfer function matrix. Since the fundamental property which must be retained for all possible perturbations of the plant is stability of the feedback system, we have studied whether the proposed feedback design is robust in the face of output-multiplicative perturbations...
When local tracks are fused to produce a global track, allowing for dependence, the approximations necessary for economic track representation can result in an unlocalised, posterior track distribution and tracker failure. This paper describes two approaches to robust track fusion. Each produces a modified version of the usual formula which undoes the adverse effects of the approximations. The general...
This paper considers a robustly convergent algorithm for worst case identification using FIR models. A new and stronger notion of robust convergence is established, and error bounds are obtained for a fixed model order as the length of data tends to infinity. The algorithm is shown to be implementable as a solution to an LMI optimisation problem. A robustly convergent algorithm for identification...
In this paper, a wavelet-based neural network (WNN) is introduced for nonlinear system identification. The structure of the WNN is similar to that of multi-layer perceptron (MLP), except that here the activation function of the hidden nodes is replaced by a wavelet function. It will be proved that any function f ∊ L2(Rn) can be approximated on any bounded domain by the WNN. Employing the MLP-like...
The robust stabilization problem is solved by constructing variable structure state-feedback control laws based on a conic partition of the state-space. The control Lyapunov function candidate and the conic partition are induced by a polyhedral region of interest. Nonlinear systems are approximated as piecewise affine in every sub-region of the partition. The partition has a simple and systematic...
The audio watermarking method presented below offers copyright protection to an audio signal by modifying its temporal characteristics. The amount of modification embedded is limited by the necessity that the output signal must not be perceptually different from the original one. The watermarking method presented here does not require the original signal for watermark detection. The watermark key...
Numerical integration of nonlinear (NL) partial differential equations (PDEs) is studied via transformation of the system and then realization of the transformed system to a passive generalized Kirchhoff circuit. The Kirchhoff circuit is appropriately discretized in order to be represented by a discrete multidimensional (MD) system, using principles of wave digital filters (WDFs).
In this paper, we present a motion estimation scheme based on a non-uniform hierarchical triangular mesh and the nodal motion vectors optimization by means of a multi-resolution differential method. The mesh pyramid is obtained by locally splitting triangular elements, where the prediction error is high. The multiresolution source image pyramid is coupled to the mesh pyramid in such a way that the...
This paper considers an approximate robust receding horizon control algorithm for a class of hybrid systems by exploiting the equivalence between piecewise linear systems and mixed logical dynamical systems. The control algorithm consists of two control modes which are a state feedback mode and a receding horizon control mode. In the receding horizon control mode, the constrained positively invariant...
This paper shows that, in the SISO case, optimal mixed-sensitivity 2-norm controllers are also solutions to the optimal robust disturbance attenuation problem (ORDAP). That is, they deliver robust sensitivity reduction despite unstructured uncertainty at the optimal level. We explicitely identify one set of ORDAP weights for which such a controller is ORDAP optimal. Functional analysis duality theory...
This paper is concerned with the regulation problem of open-channel hydraulic systems. More precisely the water flow and level within the reach are controlled trough the opening rates of two gates localized at each side of the reach. The hydrodynamics of such a system is governed by the non linear Saint-Venant partial differential equations. In the first part of the paper a reduction method which...
In this paper the robust positivity of polynomials under coefficient perturbation is investigated. This robust positivity of polynomials can be used for polynomial systems in order to determine the robust asymptotic stability of the system. We assume that the polynomials under investigation depend linearly on some parameters. Our aim is to determine the parameter perturbation region as a hypercube,...
The problem of estimating the state of discrete-time linear systems when uncertainties affect the system matrices is addressed. A quadratic cost function is considered, involving a finite number of recent measurements and a prediction vector. This leads to state the estimation problem in the form of a regularized least-squares one with uncertain data. The optimal solution (involving on-line scalar...
In this paper we consider the MIMO form of the optimal robust disturbance attenuation problem (ORDAP) posed by Zames and Owen. In particular, an operator theoretic solution is developed solving the problem exactly. The solution is given in terms of an operator defined on particular Banach space versions of matrix valued H2 spaces. The results obtained here generalize similar results obtained for SISO...
This paper deals with the combination of sliding mode control (SMC) and fuzzy logic system (FLS) for a class of non-linear multi-input multi-output (MIMO) uncertain systems. An adaptive scheme for the fuzzy control is developed to approximate the unknown system functions. A SMC is then applied to reduce the effects of both approximation errors and external disturbances. Thanks to Lyapunov's theory,...
This paper lies in the lineage of recent works studying the asymptotic behaviour of robust-scatter estimators in the case where the number of observations and the dimension of the population covariance matrix grow at infinity with the same pace. In particular, we analyze the fluctuations of bilinear forms of the robust shrinkage estimator of covariance matrix. We show that this result can be leveraged...
Deep neural networks (DNN) are typically optimized with stochastic gradient descent (SGD) using a fixed learning rate or an adaptive learning rate approach (ADAGRAD). In this paper, we introduce a new learning rule for neural networks that is based on an auxiliary function technique without parameter tuning. Instead of minimizing the objective function, a quadratic auxiliary function is recursively...
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