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System Output (system + output)
Selected AbstractsFilter-based fault detection and diagnosis using output PDFs for stochastic systems with time delaysINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2006Y. M. Zhang Abstract In this paper, a fault detection and diagnosis (FDD) scheme is studied for general stochastic dynamic systems subjected to state time delays. Different from the formulation of classical FDD problems, it is supposed that the measured information for the FDD is the probability density function (PDF) of the system output rather than its actual value. A B-spline expansion technique is applied so that the output PDF can be formulated in terms of the dynamic weights of the B-spline expansion, by which a time delay model can be established between the input and the weights with non-linearities and modelling errors. As a result, the concerned FDD problem can be transformed into a classic FDD problem subject to an uncertain non-linear system with time delays. Feasible criteria to detect the system fault are obtained and a fault diagnosis method is further presented to estimate the fault. Simple simulations are given to demonstrate the efficiency of the proposed approach. Copyright © 2006 John Wiley & Sons, Ltd. [source] A reference model approach to performance monitoring of control loops with applications to irrigation channelsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2005P. Zhang Abstract In this paper a new method for detection of oscillatory and sluggish controllers is developed. The method is aimed at control systems where rejection of measured load disturbances is the main control objective, and it is based on comparing the actual system output with the output of a reference model. A number of performance indicators are defined taking the most important factors from a control perspective into consideration. Based on the performance indicators, the performance of the control loops is evaluated. The developed method has been successfully applied to real data from an irrigation channel. The method correctly detected the control loops which needed retuning, and it provided useful information about several aspects of the control performance such as speed of response, oscillations and interactions between control loops. Copyright © 2005 John Wiley & Sons, Ltd. [source] An efficient approach for computing non-Gaussian ARMA model coefficients using Pisarenko's methodINTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 3 2005Adnan Al-Smadi Abstract This paper addresses the problem of estimating the coefficients of a general autoregressive moving average (ARMA) model from only third order cumulants (TOCs) of the noisy observations of the system output. The observed signal may be corrupted by additive coloured Gaussian noise. The system is driven by a zero-mean independent and identically distributed (i.i.d.) non-Gaussian sequence. The input is not observed. The unknown model coefficients are obtained using eigenvalue,eigenvector decomposition. The derivation of this procedure is an extension of Pisarenko harmonic autocorrelation-based (PHA) method to third order statistics. It will be shown that the desired ARMA coefficients vector corresponds to the eigenvector associated with the minimum eigenvalue of a data covariance matrix of TOCs. The proposed method is also compared with well-known algorithms as well as with the PHA method. Copyright © 2005 John Wiley & Sons, Ltd. [source] Signal reconstruction in the presence of finite-rate measurements: finite-horizon control applicationsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 1 2010Sridevi V. Sarma Abstract In this paper, we study finite-length signal reconstruction over a finite-rate noiseless channel. We allow the class of signals to belong to a bounded ellipsoid and derive a universal lower bound on a worst-case reconstruction error. We then compute upper bounds on the error that arise from different coding schemes and under different causality assumptions. When the encoder and decoder are noncausal, we derive an upper bound that either achieves the universal lower bound or is comparable to it. When the decoder and encoder are both causal operators, we show that within a very broad class of causal coding schemes, memoryless coding prevails as optimal, imposing a hard limitation on reconstruction. Finally, we map our general reconstruction problem into two important control problems in which the plant and controller are local to each other, but are together driven by a remote reference signal that is transmitted through a finite-rate noiseless channel. The first problem is to minimize a finite-horizon weighted tracking error between the remote system output and a reference command. The second problem is to navigate the state of the remote system from a nonzero initial condition to as close to the origin as possible in finite-time. Our analysis enables us to quantify the tradeoff between time horizon and performance accuracy, which is not well studied in the area of control with limited information as most works address infinite-horizon control objectives (e.g. stability, disturbance rejection). Copyright © 2009 John Wiley & Sons, Ltd. [source] Sampled-data iterative learning control with well-defined relative degreeINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2004Mingxuan Sun Abstract This paper addresses the problem of iterative learning control with well-defined relative degree. The solution is a family of sampled-data learning algorithms using lower-order differentiations of the tracking error with the order less than the relative degree. A unified convergence condition for the family of learning algorithms is derived and is proved to be independent of the highest order of the differentiations. In the presence of initial condition errors, the system output is ensured to converge to the desired trajectory with a specified error bound at each sampling instant. The bound will reduce to zero whenever the bound on initial condition errors tends to zero. Numerical examples are provided to illustrate the tracking performance of the proposed learning algorithms. Copyright © 2004 John Wiley & Sons, Ltd. [source] The Metabolic Syndrome: A Brain Disease?JOURNAL OF NEUROENDOCRINOLOGY, Issue 9 2006Ruud M Buijs Summary The incidence of obesity with, as consequence, a rise in associated diseases such as diabetes, hypertension and dyslipidemia , the metabolic syndrome , is reaching epidemic proportions in industrialized countries. Here, we provide a hypothesis that the biological clock which normally prepares us each morning for the coming activity period is altered due to a modern life style of low activity during the day and late-night food intake. Furthermore, we review the anatomical evidence supporting the proposal that an unbalanced autonomic nervous system output may lead to the simultaneous occurrence of diabetes type 2, dyslipidemia, hypertension and visceral obesity. [source] State and output feedback design for robust tracking of linear systems with rate limited actuatorsOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 1 2002Zongli Lin Abstract A design technique (Control of Uncertain Systems with Bounded Inputs, Tarbouriech S, Garcia G, (Eds), Lecture Notes in Control and Information Sciences, vol. 227, Springer: Berlin, 1997; 173,186) recently proposed for stabilization of a linear system with rate-limited actuators is utilized to design feedback laws that cause the system output to track a desired command signal. This design technique combines two design techniques recently developed for linear systems with position limited actuators, piecewise-linear LQ control (Automatica, 1994; 30: 403,416) and low-and-high gain feedback (IEEE Trans. Automat. Control, 1996; 41: 368,378), and hence takes advantage of both design techniques, while avoiding their disadvantages. In the case that only the output is available for feedback, the performance of the state feedback law is preserved by the use of a fast observer. An open-loop exponentially unstable fighter aircraft is used to demonstrate the effectiveness of the proposed control design method. Copyright © 2002 John Wiley & Sons, Ltd. [source] Nonlinear Control VIA Generalized Feedback Linearization Using Neural NetworksASIAN JOURNAL OF CONTROL, Issue 2 2001Graham C. Goodwin ABSTRACT A novel approach to nonlinear control, called Generalized Feedback Linearization (GFL), is presented. This new strategy overcomes one important drawback of the well known Feedback Linearization strategy, in the sense that it is able to handle a broader class of nonlinear systems, namely those having unstable zero dynamics. It is shown that the use of a nonlinear predictor for the system output is a key feature in the derivation of the control strategy. For certain types of systems this predictor can be found as a nonlinear function of the system input and output, allowing an output feedback control solution. The use of Artificial Neural Networks (ANN) to directly parameterize the predictor of the controlled variable when an explicit model for the system is not available, is investigated via computer simulations. This approach is based on the functional approximation capability of multi layer ANN. [source] Parameter estimation for differential equations: a generalized smoothing approachJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 5 2007J. O. Ramsay Summary., We propose a new method for estimating parameters in models that are defined by a system of non-linear differential equations. Such equations represent changes in system outputs by linking the behaviour of derivatives of a process to the behaviour of the process itself. Current methods for estimating parameters in differential equations from noisy data are computationally intensive and often poorly suited to the realization of statistical objectives such as inference and interval estimation. The paper describes a new method that uses noisy measurements on a subset of variables to estimate the parameters defining a system of non-linear differential equations. The approach is based on a modification of data smoothing methods along with a generalization of profiled estimation. We derive estimates and confidence intervals, and show that these have low bias and good coverage properties respectively for data that are simulated from models in chemical engineering and neurobiology. The performance of the method is demonstrated by using real world data from chemistry and from the progress of the autoimmune disease lupus. [source] A fast second-order signal separation algorithms with on-line capabilitiesINTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 4 2002M. F. Fahmy In correlation-based signal separation algorithms, the received mixed signals are fed to a de-coupling system designed to minimize the output cross-correlation functions. If minimizaion is perfect, each of the system's outputs carries only one signal independent of the others. In these algorithms, the computation burden of the output cross-correlation functions normally slows down the separation algorithm. This paper, describes a computationally efficient method for off-line pre-computation of the needed cross-correlation functions. Explicit formulas have been derived for the output cross-correlation functions in terms of the received input signals and the de-coupling system parameters. Then, it is shown that signal separation amounts to the least-squares solution of a system of linear equations describing these output cross-correlation functions, evaluated over a batch of lags. Next, a fast RLS-type adaptive algorithm is devised for on-line signal separation. In this respect, an algorithm is derived for updating the de-coupling parameters as data comes in. This update is achieved recursively, along the negative of the steepest descent directions of an objective cost function describing the output cross-correlation functions over a batch of lags, subject to equal output power constraints. Illustrative examples are given to demonstrate the effectiveness of the proposed algorithms. Copyright © 2002 John Wiley & Sons, Ltd. [source] Consistent dynamics suggests tight regulation of biophysical parameters in a small network of bursting neuronsDEVELOPMENTAL NEUROBIOLOGY, Issue 14 2006Attila Szücs Abstract The neuronal firing patterns in the pyloric network of crustaceans are remarkably consistent among animals. Although this characteristic of the pyloric network is well-known, the biophysical mechanisms underlying the regulation of the systems output are receiving renewed attention. Computer simulations of the pyloric network recently demonstrated that consistent motor output can be achieved from neurons with disparate biophysical parameters among animals. Here we address this hypothesis by pharmacologically manipulating the pyloric network and analyzing the emerging voltage oscillations and firing patterns. Our results show that the pyloric network of the lobster stomatogastric ganglion maintains consistent and regular firing patterns even when entire populations of specific voltage-gated channels and synaptic receptors are blocked. The variations of temporal parameters used to characterize the burst patterns of the neurons as well as their intraburst spike dynamics do not display statistically significant increase after blocking the transient K-currents (with 4-aminopyridine), the glutamatergic inhibitory synapses (with picrotoxin), or the cholinergic synapses (with atropine) in pyloric networks from different animals. These data suggest that in this very compact circuit, the biophysical parameters are cell-specific and tightly regulated. © 2006 Wiley Periodicals, Inc. J Neurobiol, 2006 [source] | ||||||||||