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Exogenous Input (exogenous + input)
Selected AbstractsIdentification of Time-Variant Modal Parameters Using Time-Varying Autoregressive with Exogenous Input and Low-Order Polynomial FunctionCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 7 2009C. S. Huang By developing the equivalent relations between the equation of motion of a time-varying structural system and the TVARX model, this work proves that instantaneous modal parameters of a time-varying system can be directly estimated from the TVARX model coefficients established from displacement responses. A moving least-squares technique incorporating polynomial basis functions is adopted to approximate the coefficient functions of the TVARX model. The coefficient functions of the TVARX model are represented by polynomials having time-dependent coefficients, instead of constant coefficients as in traditional basis function expansion approaches, so that only low orders of polynomial basis functions are needed. Numerical studies are carried out to investigate the effects of parameters in the proposed approach on accurately determining instantaneous modal parameters. Numerical analyses also demonstrate that the proposed approach is superior to some published techniques (i.e., recursive technique with a forgetting factor, traditional basis function expansion approach, and weighted basis function expansion approach) in accurately estimating instantaneous modal parameters of a structure. Finally, the proposed approach is applied to process measured data for a frame specimen subjected to a series of base excitations in shaking table tests. The specimen was damaged during testing. The identified instantaneous modal parameters are consistent with observed physical phenomena. [source] Algorithms for time synchronization of wireless structural monitoring sensorsEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 6 2005Ying Lei Abstract Dense networks of wireless structural health monitoring systems can effectively remove the disadvantages associated with current wire-based sparse sensing systems. However, recorded data sets may have relative time-delays due to interference in radio transmission or inherent internal sensor clock errors. For structural system identification and damage detection purposes, sensor data require that they are time synchronized. The need for time synchronization of sensor data is illustrated through a series of tests on asynchronous data sets. Results from the identification of structural modal parameters show that frequencies and damping ratios are not influenced by the asynchronous data; however, the error in identifying structural mode shapes can be significant. The results from these tests are summarized in Appendix A. The objective of this paper is to present algorithms for measurement data synchronization. Two algorithms are proposed for this purpose. The first algorithm is applicable when the input signal to a structure can be measured. The time-delay between an output measurement and the input is identified based on an ARX (auto-regressive model with exogenous input) model for the input,output pair recordings. The second algorithm can be used for a structure subject to ambient excitation, where the excitation cannot be measured. An ARMAV (auto-regressive moving average vector) model is constructed from two output signals and the time-delay between them is evaluated. The proposed algorithms are verified with simulation data and recorded seismic response data from multi-story buildings. The influence of noise on the time-delay estimates is also assessed. Copyright © 2004 John Wiley & Sons, Ltd. [source] Kalman filter-based adaptive control for networked systems with unknown parameters and randomly missing outputsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 18 2009Y. Shi Abstract This paper investigates the problem of adaptive control for networked control systems with unknown model parameters and randomly missing outputs. In particular, for a system with the autoregressive model with exogenous input placed in a network environment, the randomly missing output feature is modeled as a Bernoulli process. Then, an output estimator is designed to online estimate the missing output measurements, and further a Kalman filter-based method is proposed for parameter estimation. Based on the estimated output and the available output, and the estimated model parameters, an adaptive control is designed to make the output track the desired signal. Convergence properties of the proposed algorithms are analyzed in detail. Simulation examples illustrate the effectiveness of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd. [source] Robust performance for both fixed and worst-case inputsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 3 2005Nicola Elia Abstract We consider the problem of robust performance analysis when some of the exogenous inputs acting on the system are assumed to be fixed and known, while others are unknown but bounded. In particular, we consider the case where performance is measured by the ,, norm of the output signals, and the uncertainty on the nominal model is described by LTV perturbations of bounded ,, -induced norm. We first address the special case when all the exogenous inputs are fixed and known. We propose upper and lower bounds for the measure of robust performance. Two upper bounds are derived, which trade off accuracy versus computational expense. Both conditions are much less conservative than what one would obtain from assuming a worst-case exogenous input. We then generalize the conditions to the more general case, where both fixed and worst-case inputs act on the system. All these conditions are readily computable, and yield much less conservative results than one would obtain from applying standard worst-case analysis methods. Copyright © 2005 John Wiley & Sons, Ltd. [source] Using ARX and NARX approaches for modeling and prediction of the process behavior: application to a reactor-exchangerASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2008Yahya Chetouani Abstract Chemical industries are characterized often by nonlinear processes. Therefore, it is often difficult to obtain nonlinear models that accurately describe a plant in all regimes. The main contribution of this work is to establish a reliable model of a process behavior. The use of this model should reflect the normal behavior of the process and allow distinguishing it from an abnormal one. Consequently, the black-box identification based on the neural network (NN) approach by means of a nonlinear autoregressive with exogenous input (NARX) model has been chosen in this study. A comparison with an autoregressive with exogenous input (ARX) model based on the least squares criterion is carried out. This study also shows the choice and the performance of ARX and NARX models in the training and test phases. Statistical criteria are used for the validation of the experimental data of these approaches. The identified neural model is implemented by training a multilayer perceptron artificial neural network (MLP-ANN) with input,output experimental data. An analysis of the inputs number, hidden neurons and their influence on the behavior of the neural predictor is carried out. In order to illustrate the proposed ideas, a reactor-exchanger is used. Satisfactory agreement between identified and experimental data is found and results show that the neural model predicts the evolution of the process dynamics in a better way. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd. [source] Adaptive regulation of MIMO linear systems against unknown sinusoidal exogenous inputsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2009Maurizio Ficocelli Abstract This paper deals with the adaptive regulation problem in linear multi-input multi-output systems subject to unknown sinusoidal exogenous inputs, where the frequencies, amplitudes, and phases of the sinusoids are unknown and where the number of sinusoids is assumed to be known. The design of an adaptive regulator for the system under consideration is performed within a set of Q -parameterized stabilizing controllers. To facilitate the design of the adaptive regulator, triangular decoupling is introduced in part of the closed-loop system dynamics. This is achieved through the proper selection of the controller state feedback gain and the structure of the Q parameter. Regulation conditions are then presented for the case where the sinusoidal exogenous input properties are known. For the case where the sinusoidal exogenous input properties are unknown, an adaptation algorithm is proposed to tune the Q parameter in the expression of the parameterized controller. The online tuning of the Q parameter allows the controller to converge to the desired regulator. Convergence results of the adaptation algorithm are presented. A simulation example involving a retinal imaging adaptive optics system is used to illustrate the performance of the proposed adaptive system. Copyright © 2008 John Wiley & Sons, Ltd. [source] Robust performance for both fixed and worst-case inputsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 3 2005Nicola Elia Abstract We consider the problem of robust performance analysis when some of the exogenous inputs acting on the system are assumed to be fixed and known, while others are unknown but bounded. In particular, we consider the case where performance is measured by the ,, norm of the output signals, and the uncertainty on the nominal model is described by LTV perturbations of bounded ,, -induced norm. We first address the special case when all the exogenous inputs are fixed and known. We propose upper and lower bounds for the measure of robust performance. Two upper bounds are derived, which trade off accuracy versus computational expense. Both conditions are much less conservative than what one would obtain from assuming a worst-case exogenous input. We then generalize the conditions to the more general case, where both fixed and worst-case inputs act on the system. All these conditions are readily computable, and yield much less conservative results than one would obtain from applying standard worst-case analysis methods. Copyright © 2005 John Wiley & Sons, Ltd. [source] Effects of Body Condition and Protein Supplementation on LH Secretion and Luteal Function in SheepREPRODUCTION IN DOMESTIC ANIMALS, Issue 5 2007CA Meza-Herrera Contents In ruminants, nutrition is one of the exogenous inputs affecting reproductive function at different levels of the hypothalamic,hypophyseal,gonadal axis. However, the exact mechanisms or even the identification of the signalling metabolic compounds by which nutrition affects reproductive function still need further clarification. The role of static body condition (BC) and its interaction with a short-term protein supplementation (PL), on secretion of metabolic hormones [growth hormone (GH), insulin and insulin-like growth factor-1 (IGF-1)], as well as on secretion of LH and progesterone (P4) was evaluated in sheep. Twenty-four Rambouillet ewes divided into two groups, with lower (LBC) and higher body condition (HBC), were randomly assigned within BC to one of two PL levels: low (LPL, 24% of crude protein; 14 g/animal/day), and high (HPL, 44% of crude protein; 30 g/animal/day). The secretion of GH, insulin, IGF-1 and LH was evaluated on day 10 of the oestrous cycle; appearance and timing of oestrous behaviour were previously detected using rams. Progesterone secretion was evaluated on day 13 of the same cycle. No differences were found (p > 0.05) between PL groups on serum GH concentrations during the sampling period (overall mean of 4.0 ± 0.3 ng/ml), but a trend for lower values in HBC sheep was found (3.6 ± 0.4 vs 4.4 ± 0.4 ng/ml, p = 0.06). A BC effect was observed (p < 0.05) on serum IGF-1 level, with higher values in HBC sheep (p < 0.05). Neither BC nor PL affected (p > 0.05) secretion of LH and the number of corpora lutea, nor serum P4 and insulin concentrations. Results indicate a predominance of the static component of nutrition on sheep metabolic hormone responses, GH and IGF-1, with no effect of short-term PL on secretion of pituitary and ovarian hormones as well as luteal number and activity. [source] | ||||||||||