robust adaptive control in the presence of bounded disturbances the model reference adaptive control of a linear plant subjected to bounded disturbances is considered by analyzing a set of nonlinear differential equations it is shown that the global behavior of the adaptive system depends upon the persistent excitation of the reference input as well as the amplitude of the external disturbances the principal contribution of the paper is the derivation of sufficient conditions on the persistent excitation of the reference input given the maximum amplitude of disturbance for the signals in the adaptive system to be globally bounded

technical communique performance of an adaptive algorithm for sinusoidal disturbance rejection in high noise the paper proposes a discrete time algorithm for the rejection of sinusoidal disturbances of unknown frequency the algorithm is adapted from an existing continuous time algorithm and the overall nonlinear system is analyzed using a linear approximation the paper shows that the noise rejection properties may be predicted using an in phase quadrature decomposition of the noise similar to the one encountered in the theory of phase locked loops estimates are obtained for the standard deviations of the plant output and of the adaptive parameters simulations validate the predictions of the analysis despite the nonlinear nature of the adaptive system and the high level of noise applied

adaptive systems and time varying plants abstract the behaviour of four adaptive algorithms lms equation error output error identification and the goodwin ramadge caines adaptive control algorithm are examined in the face of plant parameter variations the key point to note is that persistency of excitation conditions in the time invariant parameter case guarantees exponential convergence then standard and newly developed lyapunov stability results allow us to guarantee tracking error and parameter error boundedness when the plant parameters are actually time varying

stability analysis of an adaptive system with unmodelled dynamics stability bounds for fast and slow adaptation are derived for a simple adaptive system with one unmodelled parasitic pole which is approximated by a right half plane zero in fast adaptation the product of the adaptive gain and power of the reference input must be smaller than the parasitic pole in slow adaptation most of the input signal energy must be in the frequency range lower than the square root of the parasitic pole it is shown that a plant bypass ensures a global stability property by making the linear time invariant part of the adaptive loop strictly positive real spr the price paid is an increase of the tracking error at very low frequencies and with slow adaptation when the equilibrium of the system without bypass is exponentially stable

a robust direct adaptive controller this paper proposes a new direct adaptive control algorithm which is robust with respect to additive and multiplicative plant unmodeled dynamics the algorithm is designed based on the reduced order plant which is assumed to be minimum phase and of known order and relative degree but is analyzed with respect to the overall plant which due to the unmodeled dynamics may be nonminimum phase and of unknown order and relative degree it is shown that if the unmodeled dynamics are sufficiently small in the low frequency range then the algorithm guarantees boundedness of all signals in the adaptive loop and small residual tracking errors for any bounded initial conditions in the absence of unmodeled dynamics the residual tracking error is shown to be zero

multiple test procedures for identifying the maximum safe dose we consider dose response studies for safety assessment of crop protection compounds and drugs and we offer a hypothesis testing approach for identifying the maximum dose level that is guaranteed to be safe with preassigned confidence the focus is on step down sd multiple test procedures for identifying the maximum safe dose we propose two classes of contrasts among the dose means as test statistics for these procedures pairwise contrasts pc and helmert contrasts hc the first procedure sd2pc consists of a sequence of ordinary t tests and is thus easy to apply but it can have very low power for certain step response functions the second procedure sd1hc does not suffer from this drawback but it requires a weak monotonicity assumption for its mathematical validity the powers of sd2pc and sd1hc are studied via monte carlo simulation we recommend sd2pc for linear response functions and sd1hc for step response functions the procedures are illustrated by applying them to data from an aquatic

combining multiple comparisons and modeling techniques in dose response studies the analysis of data from dose response studies has long been divided according to two major strategies multiple comparison procedures and model based approaches model based approaches assume a functional relationship between the response and the dose taken as a quantitative factor according to a prespecified parametric model the fitted model is then used to estimate an adequate dose to achieve a desired response but the validity of its conclusions will highly depend on the correct choice of the a priori unknown dose response model multiple comparison procedures regard the dose as a qualitative factor and make very few if any assumptions about the underlying dose response model the primary goal is often to identify the minimum effective dose that is statistically significant and produces a relevant biological effect one approach is to evaluate the significance of contrasts between different dose levels while preserving the family wise error rate such procedures are relatively robust but inference is confined to the selection of the target dose among the dose levels under investigation we describe a unified strategy to the analysis of data from dose response studies which combines multiple comparison and modeling techniques we assume the existence of several candidate parametric models and use multiple comparison techniques to choose the one most likely to represent the true underlying dose response curve while preserving the family wise error rate the selected model is then used to provide inference on adequate doses

robust redesign of adaptive control effects of unmodeled high frequency dynamics on stability and performance of adaptive control schemes are analyzed in the regulation problem global stability properties are no longer guaranteed but a region of attraction exists for exact adaptive regulation the dependence of the region of attraction on unmodeled parasitics is examined first then the general case of model reference adaptive control is considered in which parasitics can destroy stability and boundedness properties a modified adaptive law is proposed guaranteeing the existence of a region of attraction from which all signals converge to a residual set which contains the equilibrium for exact tracking the size of this set depends on design parameters the frequency range of parasitics and the reference input signal characteristics

an adaptive generalized predictive control method for nonlinear systems based on anfis and multiple models in this paper an adaptive generalized predictive control method using adaptive network based fuzzy inference system anfis and multiple models is proposed for a class of uncertain discrete time nonlinear systems with unstable zero dynamics the proposed controller consists of a linear and robust generalized predictive adaptive controller a nonlinear generalized predictive adaptive controller based on anfis and a switching mechanism it has been shown that the linear generalized predictive adaptive controller can ensure the boundedness of the input and output signals and the nonlinear generalized predictive controller can improve the transient performance of the system by switching between the two earlier described controllers the switching mechanism can simultaneously improve the performance and ensure the closed loop stability moreover the method has relaxed the global boundedness assumption of the higher order nonlinear term and established the analysis of stability and convergence of the closed loop system in the proposed controller anfis is adopted to estimate and compensate the unmodeled dynamics which avoids some possible flaws of a backpropagation bp neural network simulation results have demonstrated the superiority of the proposed method and verified the theoretical analysis

an adaptive control algorithm for linear systems having unknown time delay abstract this paper presents an algorithm for adaptive control of linear systems having unknown time delay a rational approximation is used for the delay and it is argued that within this framework the analysis of the properties of the corresponding adaptive control algorithm essentially reduces to the issue of robustness to unmodelled dynamics a global convergence theory is presented using recent methods for analyzing robustness properties of adaptive control algorithms the analysis is supported by simulation results

stepwise confidence intervals without multiplicity adjustment for dose response and toxicity studies abstract not all simultaneous inferences need multiplicity adjustment if the sequence of individual inferences is predefined and failure to achieve the desired inference at any step renders subsequent inferences unnecessary then multiplicity adjustment is not needed this can be justified using the closed testing principle to test appropriate hypotheses that are nested in sequence starting with the most restrictive one but what hypotheses are appropriate may not be obvious in some problems we give a fundamentally different confidence set based justification by partitioning the parameter space naturally and using the principle that exactly one member of the partition contains the true parameter in dose response studies designed to show superiority of treatments over a placebo negative control or a drug known to be efficacious active control the confidence set approach generates methods with meaningful guarantee against incorrect decision whereas previous applications of the closed testing app

on adaptive state regulation an indirect adaptive state regulator consisting of an adaptive observer and an asymptotic feedback matrix synthesis is discussed in a rigorous analysis lyapunov stability of the resulting closed loop system without the need for external excitation possible global instabilities as well as global convergence subject to sufficient excitation are shown moreover the behavior of the closed loop system is analyzed for cases in which the true plant differs from the assumed linear time invariant model in that it is of higher order contains slight nonlinearities and time variation effects and is subject to plant and measurement disturbances

a new adaptive law for robust adaptation without persistent excitation a new adaptive law motivated by the work of loannou and kokotovic 1983 is proposed for the robust adaptive control of plants with unknown parameters in this adaptive law the output error plays a dual role in the adjustment of the control parameter vector the advantages of using the adaptive law over others proposed in the literature are discussed in the ideal case the adaptive system has bounded solutions in addition the origin of the error equations is exponentially stable when the reference input is persistently exciting and has a sufficiently large amplitude the adaptive system is also shown to be robust under bounded external disturbances finally it is shown that by suitably modifying the adaptive law the overall system can be made robust in the presence of a class of unmodeled dynamics of the plant simulation results are presented throughout the paper to complement the theoretical developments

a two stage stepwise estimation procedure this article proposes a two stage simultaneous confidence procedure for the comparisons of k pairs of population means without using multiplicity adjustment of more than two populations the proposed procedure can be broadly applied to parametric or nonparametric models it is robust and versatile because its derivation only utilizes a partitioning approach in conjunction with a bivariate adjustment without any assumption on the underlying distribution to elucidate the application the proposed procedure is intertwined with the estimation of the therapeutic window of a drug it provides confidence limits for the efficacy and the toxicity of the effective doses highest ineffective dose safe doses and lowest unsafe dose simultaneously such estimation information facilitates follow up studies in clinical trials as an illustrative example the new procedure is applied to analyze a data set on molecular cancer therapeutics regarding the apoptotic killing effects of different chemical compounds on two leukemia cell lines

a finite element formulation for sliding beams part i we use the updated lagrangian and the co rotational finite element methods to obtain solutions for geometrically non linear flexible sliding beams finite element formulations are normally carried out for fixed domains since the sliding beam is a system of changing mass first we discretize the system by introducing a variable domain beam element and model the sliding beam by a fixed number of elements with changing length second we transform the system governing equations of motion to a fixed domain and use conventional finite elements fixed size and number to discretize the system then our investigation is followed by a comparison between two formulations finally we use the co rotational method in conjunction with a variable domain beam element to obtain the discretized system equations to do so we consider the beam to slide with respect to a fixed channel and later we consider a formulation in which the beam remains at rest and the channel slides with a prescribed velocity we show that both formulations end up with identical discretized equations of motion 1998 john wiley sons ltd

brief paper stability analysis of adaptively controlled systems subject to bounded disturbances an approach for studying stability of adaptively controlled systems subject to bounded disturbances with unknown statistics is given a shorter proof of some results established for minimum phase systems is also given new results are established for nonminimum phase systems in each case the uniform boundedness of all signals is proven under a condition that is discussed examples of adaptive schemes that can be treated with this approach are given

multiple test procedures for dose finding the problem of identifying the lowest dose level for which the mean response differs from that at the zero dose level is considered a general framework for stepwise testing procedures that use contrasts among the dose level means is proposed using this framework several new procedures are derived these and some existing procedures including that of williams 1971 biometrics 27 103 117 1972 biometrics 28 519 531 are compared analytically and by an extensive simulation study for the normal theory balanced one way layout case it is pointed out that the procedures based on the so called step and basin contrasts proposed by ruberg 1989 journal of american statistical association 84 816 822 have excessively high type i familywise error rates fwes and hence they should not be used some findings of the simulation study are as follows for monotone dose mean configurations williams procedure and two step down test procedures based on helmert and linear contrasts offer the best performance for nonmonotone dose mean configurations the performance of williams procedure does degrade somewhat but the other two procedures are still the best for more complex designs a simple step down test procedure that uses any alpha level tests not necessarily t tests to compare each dose level with the zero dose level controls the fwe and is the only alternative available but its power is rather low especially under nonmonotone configurations step up procedures are generally dominated by step down procedures when the same contrasts are used although the differences are not great

a stable adaptive implementation of the internal model principle an adaptive implementation of the internal model principle for linear time invariant systems that allows for rejection of unknown deterministically modeled disturbances is provided the minimal representation of the system model is used for parameter estimation and the global convergence and stability of the algorithm are investigated with unmodeled dynamics with proper modification of the parameter estimation algorithm global convergence and stability for the algorithm are obtained without the requirement of persistent excitation some simulation results are given to support the analysis

design issues in adaptive control an integrated approach to the design of practical adaptive control algorithms is presented many existing ideas are brought together and the effect of various design parameters available to a user is explored the theory is extended by showing how the problem of stabilizability of the estimated model can be overcome by running parallel estimators it is shown how asymptotic tracking of deterministic set points can be achieved in the presence of unmodeled dynamics

stable model reference adaptive control in the presence of bounded disturbances the adaptive control of a linear time invariant plant in the presence of bounded disturbances is considered in addition to the usual assumptions made regarding the plant transfer function it is also assumed that the high frequency gain k p of the plant and an upper bound on the magnitude of the controller parameters are known under these conditions the adaptive controller suggested assures the boundedness of all signals in the overall system

decision theory results for one sided multiple comparison procedures a resurgence of interest in multiple hypothesis testing has occurred in the last decade motivated by studies in genomics microarrays dna sequencing drug screening clinical trials bioassays education and psychology statisticians have been devoting considerable research energy in an effort to properly analyze multiple endpoint data in response to new applications new criteria and new methodology many ad hoc procedures have emerged the classical requirement has been to use procedures which control the strong familywise error rate fwe at some predetermined level a that is the probability of any false rejection of a true null hypothesis should be less than or equal to a finding desirable and powerful multiple test procedures is difficult under this requirement one of the more recent ideas is concerned with controlling the false discovery rate fdr that is the expected proportion of rejected hypotheses which are in fact true many multiple test procedures do control the fdr a much earlier approach to multiple testing was formulated by lehmann ann math statist 23 1952 541 552 and 28 1957 1 25 lehmann s approach is decision theoretic and he treats the multiple endpoints problem as a 2 k finite action problem when there are k endpoints this approach is appealing since unlike the fwe and fdr criteria the finite action approach pays attention to false acceptances as well as false rejections in this paper we view the multiple endpoints problem as a 2 k finite action problem we study the popular procedures single step step down and step up from the point of view of admissibility bayes and limit of bayes properties for our model which is a prototypical one and our loss function we are able to demonstrate the following results under some fairly general conditions to be specified i the single step procedure is admissible ii a sequence of prior distributions is given for which the step down procedure is a limit of a sequence of bayes procedures iii for a vector risk function where each component is the risk for an individual testing problem various admissibility and inadmissibility results are obtained in a companion paper cohen and sackrowitz ann statist 33 2005 145 158 we are able to give a characterization of bayes procedures and their limits the characterization yields a complete class and the additional useful result that the step up procedure is inadmissible the inadmissibility of step up is demonstrated there for a more stringent loss function additional decision theoretic type results are also obtained in this paper