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Robustness Properties (robustness + property)

Distribution by Scientific Domains

Engineering	66%
Mathematics and Statistics	20%
Business, Economics, Finance and Accounting	8%

Selected Abstracts

Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairs

GENETIC EPIDEMIOLOGY, Issue 4 2001
Darlene R. Goldstein
Abstract Identification of genes involved in complex traits by traditional (lod score) linkage analysis is difficult due to many complicating factors. An unfortunate drawback of non-parametric procedures in general, though, is their low power to detect genetic effects. Recently, Dudoit and Speed [2000] proposed using a (likelihood-based) score test for detecting linkage with IBD data on sib pairs. This method uses the likelihood for ,, the recombination fraction between a trait locus and a marker locus, conditional on the phenotypes of the two sibs to test the null hypothesis of no linkage (, = ½). Although a genetic model must be specified, the approach offers several advantages. This paper presents results of simulation studies characterizing the power and robustness properties of this score test for linkage, and compares the power of the test to the Haseman-Elston and modified Haseman-Elston tests. The score test is seen to have impressively high power across a broad range of true and assumed models, particularly under multiple ascertainment. Assuming an additive model with a moderate allele frequency, in the range of p = 0.2 to 0.5, along with heritability H = 0.3 and a moderate residual correlation , = 0.2 resulted in a very good overall performance across a wide range of trait-generating models. Generally, our results indicate that this score test for linkage offers a high degree of protection against wrong assumptions due to its strong robustness when used with the recommended additive model. Genet. Epidemiol. 20:415,431, 2001. © 2001 Wiley-Liss, Inc. [source]

Adaptive controller design and disturbance attenuation for SISO linear systems with zero relative degree under noisy output measurements

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2010
Sheng Zeng
Abstract In this paper, we present robust adaptive controller design for SISO linear systems with zero relative degree under noisy output measurements. We formulate the robust adaptive control problem as a nonlinear H, -optimal control problem under imperfect state measurements, and then solve it using game theory. By using the a priori knowledge of the parameter vector, we apply a soft projection algorithm, which guarantees the robustness property of the closed-loop system without any persistency of excitation assumption of the reference signal. Owing to our formulation in state space, we allow the true system to be uncontrollable, as long as the uncontrollable part is stable in the sense of Lyapunov, and the uncontrollable modes on the j,-axis are uncontrollable from the exogenous disturbance input. This assumption allows the adaptive controller to asymptotically cancel out, at the output, the effect of exogenous sinusoidal disturbance inputs with unknown magnitude, phase, and frequency. These strong robustness properties are illustrated by a numerical example. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Reduced-order robust adaptive control design of uncertain SISO linear systems

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7 2008
Qingrong Zhao
Abstract In this paper, a stability and robustness preserving adaptive controller order-reduction method is developed for a class of uncertain linear systems affected by system and measurement noises. In this method, we immediately start the integrator backstepping procedure of the controller design without first stabilizing a filtered dynamics of the output. This relieves us from generating the reference trajectory for the filtered dynamics of the output and thus reducing the controller order by n, n being the dimension of the system state. The stability of the filtered dynamics is indirectly proved via an existing state signal. The trade-off for this order reduction is that the worst-case estimate for the expanded state vector has to be chosen as a suboptimal choice rather than the optimal choice. It is shown that the resulting reduced-order adaptive controller preserves the stability and robustness properties of the full-order adaptive controller in disturbance attenuation, boundedness of closed-loop signals, and output tracking. The proposed order-reduction scheme is also applied to a class of single-input single-output linear systems with partly measured disturbances. Two examples are presented to illustrate the performance of the reduced-order controller in this paper. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Dual high-gain-based adaptive output-feedback control for a class of nonlinear systems,

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2008
P. Krishnamurthy
Abstract We propose an adaptive output-feedback controller for a general class of nonlinear triangular (strict-feedback-like) systems. The design is based on our recent results on a new high-gain control design approach utilizing a dual high-gain observer and controller architecture with a dynamic scaling. The technique provides strong robustness properties and allows the system class to contain unknown functions dependent on all states and involving unknown parameters (with no magnitude bounds required). Unlike our earlier result on this problem where a time-varying design of the high-gain scaling parameter was utilized, the technique proposed here achieves an autonomous dynamic controller by introducing a novel design of the observer, the scaling parameter, and the adaptation parameter. This provides a time-invariant dynamic output-feedback globally asymptotically stabilizing solution for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Constrained closed-loop control of depth of anaesthesia in the operating theatre during surgery

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2005
M. Mahfouf
Abstract The constrained version of generalized predictive control (GPC) which employs the quadratic programming (QP) approach is evaluated for on-line administration of an anaesthetic drug in the operating theatre during surgery. In the first instance, a patient simulator was developed using a physiological model of the patient and the necessary control software was validated via a series of extensive simulation experiments. Such a validated system was then transferred into the operating theatre for a series of clinical evaluation trials. The clinical trials, which were performed with little involvement of the design engineer, led to a good regulation of unconsciousness using fixed-parameters as well the adaptive version of the algorithm. Furthermore, the constrained algorithm displayed good robustness properties against disturbances such as high stimulus levels and allowed for safe and economically effective administration of the anaesthetic agent isoflurane. Copyright © 2005 John Wiley & Sons, Ltd. [source]

On a class of switched, robustly stable, adaptive systems

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2001
Felipe M. Pait
Abstract A class of switched algorithms for adaptive control of siso linear systems is described. The systems considered are assumed to belong to one among a finite number of classes of admissible process models, and each class is robustly stabilizable by some linear time-invariant controller. The control used is chosen in real time by a tuner or supervisor, according to observations of suitably defined ,identification errors.' The method preserves the robustness properties of the linear control design in an adaptive context, thus extending earlier ideas in multiple-model adaptive control by presenting a more flexible and less conservative framework for considering such systems. One motivating application is fault-tolerant control. Copyright © 2001 John Wiley & Sons, Ltd. [source]

On robust control algorithms for nonlinear network consensus protocols

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 3 2010
Qing Hui
Abstract Even though many consensus protocol algorithms have been developed over the last several years in the literature, robustness properties of these algorithms involving nonlinear dynamics have been largely ignored. Robustness here refers to sensitivity of the control algorithm achieving semistability and consensus in the face of model uncertainty. In this paper, we examine the robustness of several control algorithms for network consensus protocols with information model uncertainty of a specified structure. In particular, we develop sufficient conditions for robust stability of control protocol functions involving higher-order perturbation terms that scale in a consistent fashion with respect to a scaling operation on an underlying space with the additional property that the protocol functions can be written as a sum of functions, each homogeneous with respect to a fixed scaling operation, that retain system semistability and consensus. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Fast implementations and rigorous models: Can both be accommodated in NMPC?

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2008
Victor M. Zavala
Abstract In less than two decades, nonlinear model predictive control has evolved from a conceptual framework to an attractive, general approach for the control of constrained nonlinear processes. These advances were realized both through better understanding of stability and robustness properties as well as improved algorithms for dynamic optimization. This study focuses on recent advances in optimization formulations and algorithms, particularly for the simultaneous collocation-based approach. Here, we contrast this approach with competing approaches for online application and discuss further advances to deal with applications of increasing size and complexity. To address these challenges, we adapt the real-time iteration concept, developed in the context of multiple shooting (Real-Time PDE-Constrained Optimization. SIAM: Philadelphia, PA, 2007; 25,52, 3,24), to a collocation-based approach with a full-space nonlinear programming solver. We show that straightforward sensitivity calculations from the Karush,Kuhn,Tucker system also lead to a real-time iteration strategy, with both direct and shifted variants. This approach is demonstrated on a large-scale polymer process, where online calculation effort is reduced by over two orders of magnitude. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Robust feedforward design in the presence of LTI/LTV uncertainties

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 14 2007
Gilles Ferreres
Abstract A practical method is proposed for the convex design of robust feedforward controllers which ensures H,/L2 performance in the face of LTI and arbitrarily time-varying model uncertainties. A technique that computes the global minimum of this difficult infinite dimensional optimization problem is proposed, as well as a suboptimal but computationally less involved algorithm. Convergence is proved. An efficient way to analyse the robustness properties of a closed loop with or without feedforward controller is obtained as a subproblem. A missile example illustrates the efficiency of the scheme: a robust feedforward controller is designed either on the continuum of linearized time-invariant models (corresponding to trim points) or on a quasi-LPV model representing the non-linear one. Copyright © 2007 John Wiley & Sons, Ltd. [source]

A feedforward,feedback controller for infinite-dimensional systems and regulation of bounded uniformly continuous signals

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 5 2006
Eero Immonen
Abstract We design a controller for infinite-dimensional linear systems (with bounded control, observation and feedthrough operators) which, under certain assumptions, achieves asymptotic tracking of arbitrary bounded uniformly continuous reference signals in the presence of disturbances. The proposed controller is of feedforward,feedback type: The dynamic feedback part is used to stabilize the closed-loop system consisting of the plant and the controller, whereas the feedforward part is tuned using the regulator equations to achieve the regulation of desired signals. We also completely solve the regulator equations for SISO systems, and we discuss robustness properties of the proposed controller. A useful feature in our design is that the feedforward part of the controller can be designed independently of the feedback part. This automatically leads to a degree of robustness in the stabilizing part of the controller, which is not present in the existing state feedback controllers solving the same output regulation problem. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Robustness analysis of flexible structures: practical algorithms

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2003
Gilles Ferreres
Abstract When analysing the robustness properties of a flexible system, the classical solution, which consists of computing lower and upper bounds of the structured singular value (s.s.v.) at each point of a frequency gridding, appears unreliable. This paper describes two algorithms, based on the same technical result: the first one directly computes an upper bound of the maximal s.s.v. over a frequency interval, while the second one eliminates frequency intervals, inside which the s.s.v. is guaranteed to be below a given value. Various strategies are then proposed, which combine these two techniques, and also integrate methods for computing a lower bound of the s.s.v. The computational efficiency of the scheme is illustrated on a real-world application, namely a telescope mock-up which is significant of a high order flexible system. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Guaranteed H, robustness bounds for Wiener filtering and prediction

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 1 2002
P. Bolzern
Abstract The paper deals with special classes of H, estimation problems, where the signal to be estimated coincides with the uncorrupted measured output. Explicit bounds on the difference between nominal and actual H, performance are obtained by means of elementary algebraic manipulations. These bounds are new in continuous-time filtering and discrete-time one-step ahead prediction. As for discrete-time filtering, the paper provides new proofs that are alternative to existing derivations based on the Krein spaces formalism. In particular, some remarkable H, robustness properties of Kalman filters and predictors are highlighted. The usefulness of these results for improving the estimator design under a mixed H2/H, viewpoint is also discussed. The dualization of the analysis allows one to evaluate guaranteed H, robustness bounds for state-feedback regulators of systems affected by actuator disturbances. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Robustness of alternative non-linearity tests for SETAR models

JOURNAL OF FORECASTING, Issue 3 2004
Wai-Sum Chan
Abstract In recent years there has been a growing interest in exploiting potential forecast gains from the non-linear structure of self-exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR-type non-linearities in an observed time series. It is important to study the power and robustness properties of these tests since erroneous test results might lead to misspecified prediction problems. In this paper we investigate the robustness properties of several commonly used non-linearity tests. Both the robustness with respect to outlying observations and the robustness with respect to model specification are considered. The power comparison of these testing procedures is carried out using Monte Carlo simulation. The results indicate that all of the existing tests are not robust to outliers and model misspecification. Finally, an empirical application applies the statistical tests to stock market returns of the four little dragons (Hong Kong, South Korea, Singapore and Taiwan) in East Asia. The non-linearity tests fail to provide consistent conclusions most of the time. The results in this article stress the need for a more robust test for SETAR-type non-linearity in time series analysis and forecasting. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Monetary Policy in a Forward-Looking Input,Output Economy

JOURNAL OF MONEY, CREDIT AND BANKING, Issue 4 2009
BRAD E. STRUM
inflation targeting; price-level targeting; intermediate goods This paper examines the implications for monetary policy of sticky prices in both final and intermediate goods in a New Keynesian model. Both optimal policy under commitment and discretionary policy under simple loss functions are studied. Household utility losses under alternative loss functions are compared; additionally, the robustness of policy performance to model and shock misperceptions and parameter uncertainty is examined. Targeting inflation in both consumer and intermediate goods performs better than targeting inflation in one sector; targeting price levels of both final and intermediate goods performs significantly better. Moreover, targeting price levels in both sectors yields superior robustness properties. [source]

Regression analysis based on semicompeting risks data

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2008
Jin-Jian Hsieh
Summary., Semicompeting risks data are commonly seen in biomedical applications in which a terminal event censors a non-terminal event. Possible dependent censoring complicates statistical analysis. We consider regression analysis based on a non-terminal event, say disease progression, which is subject to censoring by death. The methodology proposed is developed for discrete covariates under two types of assumption. First, separate copula models are assumed for each covariate group and then a flexible regression model is imposed on the progression time which is of major interest. Model checking procedures are also proposed to help to choose a best-fitted model. Under a two-sample setting, Lin and co-workers proposed a competing method which requires an additional marginal assumption on the terminal event and implicitly assumes that the dependence structures in the two groups are the same. Using simulations, we compare the two approaches on the basis of their finite sample performances and robustness properties under model misspecification. The method proposed is applied to a bone marrow transplant data set. [source]

Highly Robust Estimation of the Autocovariance Function

JOURNAL OF TIME SERIES ANALYSIS, Issue 6 2000
Yanyuan Ma
In this paper, the problem of the robustness of the sample autocovariance function is addressed. We propose a new autocovariance estimator, based on a highly robust estimator of scale. Its robustness properties are studied by means of the influence function, and a new concept of temporal breakdown point. As the theoretical variance of the estimator does not have a closed form, we perform a simulation study. Situations with various size of outliers are tested. They confirm the robustness properties of the new estimator. An S-Plus function for the highly robust autocovariance estimator is made available on the Web at http://www-math.mit.edu/~yanyuan/Genton/Time/time.html. At the end, we analyze a time series of monthly interest rates of an Austrian bank. [source]

A mathematical programming approach for improving the robustness of least sum of absolute deviations regression

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 4 2006
Avi Giloni
Abstract This paper discusses a novel application of mathematical programming techniques to a regression problem. While least squares regression techniques have been used for a long time, it is known that their robustness properties are not desirable. Specifically, the estimators are known to be too sensitive to data contamination. In this paper we examine regressions based on Least-sum of Absolute Deviations (LAD) and show that the robustness of the estimator can be improved significantly through a judicious choice of weights. The problem of finding optimum weights is formulated as a nonlinear mixed integer program, which is too difficult to solve exactly in general. We demonstrate that our problem is equivalent to a mathematical program with a single functional constraint resembling the knapsack problem and then solve it for a special case. We then generalize this solution to general regression designs. Furthermore, we provide an efficient algorithm to solve the general nonlinear, mixed integer programming problem when the number of predictors is small. We show the efficacy of the weighted LAD estimator using numerical examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006 [source]

Decentralized control strategies for dynamic routing

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 6 2002
ftar
Abstract The routing problem in multi-destination data communication networks is considered. A dynamic model, which can incorporate arbitrary, different, time-varying processing delays at different nodes, is developed to describe the network dynamics. Based on this model, controllers for routing control are proposed. The structures of the proposed controllers are motivated by an optimal control problem. These proposed controllers are completely decentralized in the sense that all necessary on-line computations are done locally at each node. Furthermore, the information needed for these computations is related only to the queue lengths at the present node and the adjacent downstream nodes. Both cases when the controls can be continuously changed and when the controls are updated at discrete time instants are considered. In the latter case the controls at different nodes may be updated at different time instants (i.e. the network is not necessarily synchronous). It is shown that the controllers enjoy many desirable properties; in particular, they clear all the queues of the network in the absence of external message arrivals, in finite time. Furthermore, the controllers do not direct messages around a loop. They also have certain robustness properties. Some simulation results relating to a number of realistic problems are presented to illustrate various features of the controllers. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Optimal control of fuel processing system using generalized linear quadratic Gaussian and loop transfer recovery method,,

ASIAN JOURNAL OF CONTROL, Issue 5 2010
Huan-Liang Tsai
Abstract This paper proposes an optimal control system that consists of both feedforward and state-feedback controllers designed using a generalized linear quadratic Gaussian and loop transfer recovery (GLQG/LTR) method for a fuel processing system (FPS). This FPS uses natural gas as fuel and reacts with atmospheric air through a catalytic partial oxidation (CPO) response. The control objective is focused on the regulatory performance of the output vector in response to a desired stack current command in the face of load variation. The proposed method provides another degree of freedom in the optimal control design and gives the compensated system a prescribed degree of stability. Finally, the numerical simulations of compensated FPS reveal that the proposed method displays better performance and robustness properties in both time-domain and frequency-domain responses than those obtained by the traditional LQ Method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Test of Marginal Compatibility and Smoothing Methods for Exchangeable Binary Data with Unequal Cluster Sizes

BIOMETRICS, Issue 1 2007
Zhen Pang
Summary Exchangeable binary data are often collected in developmental toxicity and other studies, and a whole host of parametric distributions for fitting this kind of data have been proposed in the literature. While these distributions can be matched to have the same marginal probability and intra-cluster correlation, they can be quite different in terms of shape and higher-order quantities of interest such as the litter-level risk of having at least one malformed fetus. A sensible alternative is to fit a saturated model (Bowman and George, 1995, Journal of the American Statistical Association90, 871,879) using the expectation-maximization (EM) algorithm proposed by Stefanescu and Turnbull (2003, Biometrics59, 18,24). The assumption of compatibility of marginal distributions is often made to link up the distributions for different cluster sizes so that estimation can be based on the combined data. Stefanescu and Turnbull proposed a modified trend test to test this assumption. Their test, however, fails to take into account the variability of an estimated null expectation and as a result leads to inaccurate p -values. This drawback is rectified in this article. When the data are sparse, the probability function estimated using a saturated model can be very jagged and some kind of smoothing is needed. We extend the penalized likelihood method (Simonoff, 1983, Annals of Statistics11, 208,218) to the present case of unequal cluster sizes and implement the method using an EM-type algorithm. In the presence of covariate, we propose a penalized kernel method that performs smoothing in both the covariate and response space. The proposed methods are illustrated using several data sets and the sampling and robustness properties of the resulting estimators are evaluated by simulations. [source]

Adaptive controller design and disturbance attenuation for SISO linear systems with zero relative degree under noisy output measurements

Robust stabilization of a class of uncertain system via block decomposition and VSC

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 15 2002
Alexander G. Loukianov
Abstract In this paper, a block decomposition procedure for sliding mode control of a class of nonlinear systems with matched and unmatched uncertainties, is proposed. Based on the nonlinear block control principle, a sliding manifold design problem is divided into a number of sub-problems of lower dimension which can be solved independently. As a result, the nominal parts of the sliding mode dynamics is linearized. A discontinuous feedback is then used to compensate the matched uncertainty. Finally, a step-by-step Lyapunov technique and a high gain approach is applied to obtain hierarchical fast motions on the sliding manifolds and to achieve the robustness property of the closed-loop system motion with respect to unmatched uncertainty. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Composition cascade control for chemical reactors

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 13 2002
Jose Alvarez-Ramirez
Abstract Eventhough the composition control of chemical reactors is an old, widely studied, and still relevant problem in chemical process control, it still presents some aspects that remain unexplored or unresolved. For instance, a unifying approach is needed to systematize the existing ad hoc controller constructions, to rigorously explain their remarkable robustness property, and to explore the possibility of improving their construction and functioning. In this paper, some aspects of these control problems are addressed by resorting to recently developed approaches in constructive non-linear control, yielding a systematic controller construction coupled to a simple tuning scheme that can be executed with standard tuning rules, a closed-loop stability criterion, and an explanation of the closed-loop dynamics behaviour. Specifically, a linear cascade (master/slave) control configuration is proposed, which leads to global internal stability of the controlled system with asymptotic regulation of the output-stream composition about a given desired setpoint. A simulation example is used to illustrate the results. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression

BIOMETRICS, Issue 1 2010
Donglin Zeng
Summary In this article, we study the estimation of mean response and regression coefficient in semiparametric regression problems when response variable is subject to nonrandom missingness. When the missingness is independent of the response conditional on high-dimensional auxiliary information, the parametric approach may misspecify the relationship between covariates and response while the nonparametric approach is infeasible because of the curse of dimensionality. To overcome this, we study a model-based approach to condense the auxiliary information and estimate the parameters of interest nonparametrically on the condensed covariate space. Our estimators possess the double robustness property, i.e., they are consistent whenever the model for the response given auxiliary covariates or the model for the missingness given auxiliary covariate is correct. We conduct a number of simulations to compare the numerical performance between our estimators and other existing estimators in the current missing data literature, including the propensity score approach and the inverse probability weighted estimating equation. A set of real data is used to illustrate our approach. [source]