Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Robust model predictive controller with output feedback and target tracking

A model predictive controller (MPC) is proposed, which is robustly stable for some classes of model uncertainty and to unknown disturbances. It is considered as the case of open-loop stable systems, where only the inputs and controlled outputs are measured. It is assumed that the controller will work in a scenario where target tracking is also required. Here, it is extended to the nominal infinite horizon MPC with output feedback. The method considers an extended cost function that can be made globally convergent for any finite input horizon considered for the uncertain system. The method is based on the explicit inclusion of cost contracting constraints in the control problem. The controller considers the output feedback case through a non-minimal state-space model that is built using past output measurements and past input increments. The application of the robust output feedback MPC is illustrated through the simulation of a low-order multivariable system.

A stable MPC with zone control

Several MPC applications implement a control strategy in which some of the system outputs are controlled within specified ranges or zones, rather than at fixed set points [J.M. Maciejowski, Predictive Control with Constraints, Prentice Hall, New Jersey, 2002]. This means that these outputs will be treated as controlled variables only when the predicted future values lie outside the boundary of their corresponding zones. The zone control is usually implemented by selecting an appropriate weighting matrix for the output error in the control cost function. When an output prediction is inside its zone, the corresponding weight is zeroed, so that the controller ignores this output. When the output prediction lies outside the zone, the error weight is made equal to a specified value and the distance between the output prediction and the boundary of the zone is minimized. The main problem of this approach, as long as stability of the closed loop is concerned, is that each time an output is switched from the status of non-controlled to the status of controlled, or vice versa, a different linear controller is activated. Thus, throughout the continuous operation of the process, the control system keeps switching from one controller to another. Even if a stabilizing control law is developed for each of the control configurations, switching among stable controllers not necessarily produces a stable closed loop system. Here, a stable M PC is developed for the zone control of open-loop stable systems. Focusing on the practical application of the proposed controller, it is assumed that in the control structure of the process system there is an upper optimization layer that defines optimal targets to the system inputs. The performance of the proposed strategy is illustrated by simulation of a subsystem of an industrial FCC system. (C) 2008 Elsevier Ltd. All rights reserved.

An Array Recursive Least-Squares Algorithm With Generic Nonfading Regularization Matrix

We present a novel array RLS algorithm with forgetting factor that circumvents the problem of fading regularization, inherent to the standard exponentially-weighted RLS, by allowing for time-varying regularization matrices with generic structure. Simulations in finite precision show the algorithm`s superiority as compared to alternative algorithms in the context of adaptive beamforming.

A Levinson algorithm based on an isometric transformation of Durbin`s

Starting from the Durbin algorithm in polynomial space with an inner product defined by the signal autocorrelation matrix, an isometric transformation is defined that maps this vector space into another one where the Levinson algorithm is performed. Alternatively, for iterative algorithms such as discrete all-pole (DAP), an efficient implementation of a Gohberg-Semencul (GS) relation is developed for the inversion of the autocorrelation matrix which considers its centrosymmetry. In the solution of the autocorrelation equations, the Levinson algorithm is found to be less complex operationally than the procedures based on GS inversion for up to a minimum of five iterations at various linear prediction (LP) orders.

Discrete-time mean variance optimal control of linear systems with Markovian jumps and multiplicative noise

In this article, we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noise under three kinds of performance criterions related to the final value of the expectation and variance of the output. In the first problem it is desired to minimise the final variance of the output subject to a restriction on its final expectation, in the second one it is desired to maximise the final expectation of the output subject to a restriction on its final variance, and in the third one it is considered a performance criterion composed by a linear combination of the final variance and expectation of the output of the system. We present explicit sufficient conditions for the existence of an optimal control strategy for these problems, generalising previous results in the literature. We conclude this article presenting a numerical example of an asset liabilities management model for pension funds with regime switching.

Non-linear modal analysis for beams subjected to axial loads: Analytical and finite-element solutions

A rigorous derivation of non-linear equations governing the dynamics of an axially loaded beam is given with a clear focus to develop robust low-dimensional models. Two important loading scenarios were considered, where a structure is subjected to a uniformly distributed axial and a thrust force. These loads are to mimic the main forces acting on an offshore riser, for which an analytical methodology has been developed and applied. In particular, non-linear normal modes (NNMs) and non-linear multi-modes (NMMs) have been constructed by using the method of multiple scales. This is to effectively analyse the transversal vibration responses by monitoring the modal responses and mode interactions. The developed analytical models have been crosschecked against the results from FEM simulation. The FEM model having 26 elements and 77 degrees-of-freedom gave similar results as the low-dimensional (one degree-of-freedom) non-linear oscillator, which was developed by constructing a so-called invariant manifold. The comparisons of the dynamical responses were made in terms of time histories, phase portraits and mode shapes. (C) 2008 Elsevier Ltd. All rights reserved.

Bias-corrected Pearson estimating functions for Taylor`s power law applied to benthic macrofauna data

Estimation of Taylor`s power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating function. Furthermore, we investigate a more general regression model allowing for site-specific covariates. This method may be efficiently implemented using a Newton scoring algorithm, with standard errors calculated from the inverse Godambe information matrix. The method is applied to a set of biomass data for benthic macrofauna from two Danish estuaries. (C) 2011 Elsevier B.V. All rights reserved.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

ECONOMICAL OCCUPATION OF AGRICULTURAL AREA OF PIRACICABA (SAO PAULO STATE, BRAZIL), INCLUDING RISK THROUGH LINEAR PROGRAMMING

The economic occupation of an area of 500 ha for Piracicaba was studied with the irrigated cultures of maize, tomato, sugarcane and beans, having used models of deterministic linear programming and linear programming including risk for the Target-Motad model, where two situations had been analyzed. In the deterministic model the area was the restrictive factor and the water was not restrictive for none of the tested situations. For the first situation the gotten maximum income was of R$ 1,883,372.87 and for the second situation it was of R$ 1,821,772.40. In the model including risk a producer that accepts risk can in the first situation get the maximum income of R$ 1,883,372. 87 with a minimum risk of R$ 350 year(-1), and in the second situation R$ 1,821,772.40 with a minimum risk of R$ 40 year(-1). Already a producer averse to the risk can get in the first situation a maximum income of R$ 1,775,974.81 with null risk and for the second situation R$ 1.707.706, 26 with null risk, both without water restriction. These results stand out the importance of the inclusion of the risk in supplying alternative occupations to the producer, allowing to a producer taking of decision considered the risk aversion and the pretension of income.

Age-related association of rDNA and telomeres with the nuclear matrix in mouse hepatocytes

Transcribed sequences have been suggested to be associated with the nuclear matrix, differing from non-transcribing sequences, which have been reported to be contained in DNA loops. However, although a dozen of genes have their expression level affected by aging, data on chromatin-nuclear matrix interactions under this physiological condition are still scarce. In the present study, liver imprints from young, adult and old mice were subjected to FISH (fluorescence in situ hybridization) for 45S rDNA and telomeric sequences, with or without a lysis treatment to produce extended chromatin fibres. There was an increased amount of 45S rDNA sequences located in DNA loops as the animals grow older, while telomeric sequences were always observed in DNA loops irrespective of the animal age. We assume that active rRNA genes associate with the nuclear matrix, while DNA loops contain silent sequences. Transcription of each 45S rDNA repeat unit is suggested to be dependent on its interaction with the nuclear matrix.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Increased class A scavenger receptor and glomerular lipid precede mesangial matrix expansion in the bGH mouse model

Objective: Elevated neutral lipid content and mRNA expression of class A scavenger receptor (SRA) have been found in the renal cortex of the bovine growth hormone (bGH) mouse model of progressive glomerulosclerosis (GS). We hypothesize that the increased expression of SRA precedes glomerular scarring in this model. Design: Real time RT-PCR and immunofluorescence were employed to measure SRA and collagen types I and IV in the bGH transgenic and control mice at 5 and 12 weeks (wk) of age to determine the chronology of change in SRA expression in relation to glomerular scarring. Alternative mechanisms for increasing glomerular lipid were assessed by measuring mRNA expression levels of low-density lipoprotein receptor (LDL-r), 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR), and ATP-binding cassette transporter A1 (ABCA1). In addition, the involvement of macrophages in early GS was assessed by CD68 mRNA expression in kidney cortex. Results: Both mRNA and protein levels of SRA were significantly increased in 5-wk bGH compared with control mice, whereas the expression of collagen I and IV was unaltered. Unchanged levels of LDL-r and HMGR mRNA indicate that neither regulated cholesterol uptake via LDL-r nor the cholesterol synthetic pathway played a role in the early lipid increase. The finding of increased ABCA1 expression was an indicator of excess intracellular lipid in the renal cortex of bGH mice at 5 wk. CD68 expression in bGH did not differ significantly from that of controls at 5 wk suggesting that cortical macrophage infiltration was not increased in bGH mice at this time point. Conclusion: An early increase in SRA mRNA and protein expression in the bGH kidney precedes glomerular scarring and is independent of macrophage influx. Published by Elsevier Ltd. on behalf of Growth Hormone Research Society.

Impedimetric immunosensor for electronegative low density lipoprotein (LDL(-)) based on monoclonal antibody adsorbed on (polyvinyl formal)-gold nanoparticles matrix

Monoclonal antibodies (MAb) have been commonly applied to measure LDL in vivo and to characterize modifications of the lipids and apoprotein of the LDL particles. The electronegative low density lipoprotein (LDL(-)) has an apolipoprotein B-100 modified at oxidized events in vivo. In this work, a novel LDL-electrochemical biosensor was developed by adsorption of anti-LDL(-) MAb on an (polyvinyl formal)-gold nanoparticles (PVF-AuNPs)-modified gold electrode. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) were used to characterize the recognition of LDL-. The interaction between MAb-LDL(-) leads to a blockage in the electron transfer of the [Fe(CN)(6)](4-)/K(4)[Fe(CN)(6)](3-) redox couple, which may could result in high change in the electron transfer resistance (R(CT)) and decrease in the amperometric responses in CV analysis. The compact antibody-antigen complex introduces the insulating layer on the assembled surface, which increases the diameter of the semicircle, resulting in a high R(CT), and the charge transferring rate constant k(0) decreases from 18.2 x 10(-6) m/s to 4.6 x 10(-6) m/s. Our results suggest that the interaction between MAb and lipoprotein can be quantitatively assessed by the modified electrode. The PVF-AuNPs-MAb system exhibited a sensitive response to LDL(-), which could be used as a biosensor to quantify plasmatic levels of LDL(-). (C) 2011 Elsevier B.V. All rights reserved.

Capacity of the extracellular matrix of the bone marrow to induce proliferation of myeloid cells in vitro in model of protein malnutrition in mice

The aim of this study was to verify the capacity of the extracellular matrix (ECM) obtained from bone marrow of malnourished mice to sustain survival and to induce the proliferation of myeloid cells. We also verified the capacity of the tests to interact with in vitro hematopoietic cytokines. Male ""Swiss"" mice were submitted to protein malnutrition with a diet contents of 4% casein until they lost 20% of the original weight, while the group-control was kept with a diet content of 14% of casein. The bone marrow was extracted with 1.0 mg of aprotinin/mL in PBS. The proliferation tests were carried out with myeloid cell line FDCP-1, by the colorimetric method of reduction of the MTT. The obtained ECM from nourished and undernourished mice induced cellular proliferation in vitro. Tests performed with Il-3 and GM-CSF cytokines in a concentration of 10 and 500 rho g/mL displayed synergic and regulatory effects respectively. The ECM obtained from the malnourished group submitted to the binding to GM-CSF demonstrated higher cellular proliferation than the ECM obtained from the control group (p<0.05). The results suggest that the alterations in the composition of ECM of bone marrow caused by malnutrition might lead to modification of the GM-CSF activity modulation.