exact and inexact lqg model matching problems the design of discrete time lqg linear quadratic gaussian controllers that yield closed loop systems that match a provided model is addressed the solution is in the form of a two degree of freedom controller having feedback and feedforward paths that independently provide the optimal actions for disturbance rejection and model matching exact and approximate model matching cases are discussed in a unified fashion

multivariable self tuning control a model following approach abstract a multivariable self tuning control algorithm based on the model following approach is proposed in this paper reference is made to systems described by arm ax models with an a apriori known interactor matrix the cost function is defined in terms of variance of the norm of a suitably defined generalized error a detuned version of the model following procedure is also given so as to cope with non minimum phase systems the proposed algorithm is finally applied to the self tuning control problem of a simulated binary distillation column

a note on the invariant properties of interactors lower left triangular interactors are used in multivariable control systems however since the triangular form is not essential for a polynomial matrix to be an interactor in this note we will focus our attention on the more general class of interactors and their properties it will be shown that interactors of this general class and the state equation of the inverted interactorized closed loop system are invariant under a state feedback

design and analysis of a fuzzy proportional integral derivative controller abstract this paper describes the design principle tracking performance and stability analysis of a fuzzy proportional integral pi plus a derivative d controller first the fuzzy pi d controller is derived from the conventional continuous time linear pi d controller then the fuzzification control rule base and defuzzification in the design of the fuzzy controller are discussed in detail the resulting controller is a discrete time fuzzy version of the conventional pi d controller which has the same linear structure in the proportional integral and derivative parts but has nonconstant gains the proportional integral and derivative gains are nonlinear functions of the input signals the new fuzzy pi d controller thus preserves the simple linear structure of its conventional counterpart yet enhances the self tuning control capability computer simulation results have demonstrated the advantages of the fuzzy controller particularly when the process to be controlled is nonlinear after a brief stability analysis where a simple and realistic sufficient condition for the bounded input bounded output stability of the overall feedback control system was derived several computer simulation results are shown to compare with the conventional pi d controller computer simulation results have shown the new fuzzy controller indeed has satisfactory tracking performance

random fuzzy dependent chance programming and its hybrid intelligent algorithm abstract a fuzzy random variable is a measurable function from a probability space to a collection of fuzzy sets while a random fuzzy variable is a function from a collection of random variables to 0 1 this paper provides a spectrum of random fuzzy dependent chance programming in which the underlying philosophy is based on selecting the decision with maximal chance to meet the event in order to speed up the solution process we train a neural network to approximate chance functions based on the training data generated by the random fuzzy simulation finally we integrate random fuzzy simulation neural network and genetic algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving random fuzzy dependent chance programming models and illustrate its effectiveness by some numerical examples

a class of stochastic optimization problems with one quadratic several linear objective functions and extended portfolio selection model in this paper a class of stochastic multiple objective programming problems with one quadratic several linear objective functions and linear constraints has been introduced the former model is transformed into a deterministic multiple objective nonlinear programming model by means of the introduction of random variables expectation the reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function this objective function is combined with the quadratic function using the weighted sums the quadratic problem is transformed into a linear parametric complementary problem the basic formula for the proposed approach the sufficient and necessary conditions for properly weakly efficient solutions and some construction characteristics of weakly efficient solution sets are obtained an interactive algorithm is proposed based on reference direction and weighted sums varying the parameter vector on the right hand side of the model the dm can freely search the efficient frontier with the model an extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk the interactive approach is illustrated with a practical example

project scheduling problem with mixed uncertainty of randomness and fuzziness abstract project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time this paper considers project scheduling problem with mixed uncertainty of randomness and fuzziness where activity duration times are assumed to be random fuzzy variables three types of random fuzzy models as expected cost minimization model cost minimization model and chance maximization model are built to meet different management requirements random fuzzy simulations for some uncertain functions are given and embedded into genetic algorithm to design a hybrid intelligent algorithm finally some numerical experiments are given for the sake of illustration of the effectiveness of the algorithm

arbitrary adaptive pole placement for linear multivariable systems this paper presents a methodology for adaptively assigning the closed loop poles of either continuous or discrete time linear mnltivariable systems the scheme is direct in nature in that no parametrized model of the unknown plant needs to be explicitely identified rather the control parameters and an equivalent plant parameterization are simultaneously estimated from input output data using linear parameter estimation procedures implementation of the scheme requires only knowledge of the system controllability indexes and an upper bound on the observabilily index

adaptive asymptotic control of multivariable systems based on a one parameter estimation approach multivariable adaptive control is an important and challenging research area where it usually requires designing adaptive laws to estimate a number of unknown parameters hence this process may excessively consume limitedly available computational resources and large amount of computational time which may degrade system performances particularly for the case of online updating of parameter estimates thus reducing the number of parameters to be estimated is a promising way to solve the problem the state of the art result in the area is to update just one adaptive parameter however the tracking errors are only ensured bounded and thus the resulting closed loop system is not asymptotically stable hence a question that arises is whether such a result of non zero tracking errors is the price paid for reducing the number of updating parameters up to now there is still no answer to this question in this paper we address such an issue and realize asymptotic stability with online estimation of just one parameter to achieve such a goal a novel dynamic loop gain function based approach is proposed to incorporate with the backstepping control design procedure which enables us to solve the algebraic loop problem caused by all the existing traditional nussbaum function based approaches and thus establish system stability a bound for the tracking error is explicitly established in terms of l2 norm which helps improve the transient performance by selecting the control parameters moreover guaranteed by the barbalats lemma the tracking error is further ensured to approach zero asymptotically finally simulation examples are conducted to testify the effectiveness of the proposed approach

a robust mrac using variable structure design for multivariable plants it is well known that undesirable transient responses and tracking performances have been frequently observed in traditional model reference adaptive control mrac problems especially for multivariable plants with unmodeled dynamics and output disturbances in this paper a new robust model reference adaptive control using variable structure design vsd is proposed to investigate the solution based on a definition of generalized relative degree grd for multivariable plants the general case for plants with arbitrary grd is completely solved it is shown that even when the uncertainties are presnet global stability and robustness of the closed loop control system are achieved furthermore without any persistence of excitation the tracking errors will at least asymptotically converge to zero for grd one plants and to a small residual set for plants with any higher grd with a suitable choice of initial control parameters the tracking errors can even be driven to zero in finite time for grd one plants and to a small residual set exponentially for plants with any higher grd

new recursive parameter estimation algorithms with varying but bounded gain matrix the main disadvantage of the standard recursive weighted least squares algorithm is the unbounded increase in its gain matrix when the covariance vector is not sufficiently rich we propose a general method for updating the gain matrix this allows us to derive many new specific algorithms in which the boundedness of the gain matrix is ensured automatically

inverse interactorizing and triangularizing with an arbitrary pole assignment using the state feedback a simple method is presented on how to generate the state feedback which makes the closed loop transfer matrix the inverse of the interactor matrix this can be regarded as a generalization of decoupling control using state feedback an arbitrary pole assignment which preserves the lower left triangular form of the inverse of the interactor matrix is also considered

the design of a precompensator for multivariable adaptive control a network theoretic approach a network theoretic approach to the design of a dynamic precompensator c s for a multiinput multioutput plant t s is considered the design is based on the relative degree of each element of t s specifically an efficient algorithm is presented for determining whether a given plant t s has a diagonal precompensator c s such that for almost all cases t s c s has a diagonal interactor the algorithm also finds any optimal precompensator in the sense that the total relative degree is minimal the algorithm can be easily modified to work even when a t s represented by a nonsquare matrix is given

error estimation with guaranteed accuracy of finite element method in nonconvex polygonal domains a new method to estimate errors of the finite element method fem for nonconvex polygonal domain is proposed it gives mathematically rigorous upper bounds for the errors using calculations with guaranteed accuracy numerical examples are shown and their orders concerning mesh sizes are compared with theoretical orders

generalized minimum variance control of mimo systems a stability result abstract this note describes a generalized minimum variance gmv control algorithm for square systems the controller embeds an integral action on the error vector it is shown how an appropriate yet simple choice of some design parameters guarantees overall asymptotic stability for open loop asymptotically stable and possibly nonminimum phase plants

discrete time stochastic adaptive control this paper establishes global convergence of a stochastic adaptive control algorithm for discrete time linear systems it is shown that with probability one the algorithm will ensure the system inputs and outputs are sample mean square bounded and the conditional mean square output tracking error achieves its global minimum possible value for linear feedback control thus asymptotically the adaptive control algorithm achieves the same performance as could be achieved if the system parameters were known

paper the role of the interactor matrix in multivariable stochastic adaptive control this paper is concerned with the extension of the well known minimum variance control strategy to the multi input multi output case it is shown that this extension is straightforward when the system interactor matrix is diagonal but presents some unexpected difficulties in the general case we develop a suitable stochastic controller for the general case as a logical extension of the single input algorithm we also explore the properties of the algorithm in detail we also address the question of adaptive control of multivariable stochastic systems and investigate one possible strategy for overcoming the requirement of knowing the system interactor matrix a priori

an alternative approach to norm bound computation for inverses of linear operators in hilbert spaces abstract in the present paper we propose a computer assisted procedure to prove the invertibility of a linear operator in a hilbert space and to compute a verified norm bound of its inverse a number of the authors have previously proposed two verification approaches that are based on projection and constructive a priori error estimates the approach of the present paper is expected to bridge the gap between the two previous procedures in actual numerical verifications several verification examples that confirm the actual effectiveness of the proposed procedure are reported

multivariable self tuning regulator with generalized cost function abstract the control of a class of multivariable systems described by linear vector difference equations with constant but unknown parameters is discussed a strategy using a generalized cost function is first presented a multivariable self tuning regulator based on this generalized minimum variance strategy is then proposed it uses a recursive multiple least squares estimator and a linear matrix controller obtained directly from the estimates the algorithm can be considered as a multivariable generalization of clarke s self tuning controller

frequency domain conditions for parameter convergence in multivariable recursive identification abstract a multivariable adaptive identifier is designed on the basis of a canonical form leading to a unique set of parameters to identify frequency domain conditions for parameter convergence are derived the sufficient condition to guarantee convergence in all cases is that n v distinct frequency components be present in each input of the plant where v is the maximum of the observability indices and n the order of the plant on the other hand the necessary condition requires n mv distinct frequency components distributed over the inputs where m is the number of inputs and at least v frequency components are in each separate input through examples it is shown that convergence may or may not occur for intermediate numbers of frequency components depending on their location and on the plant parameters

a multivariable self tuning controller based on pole placement design abstract the design problem of controller for discrete time linear multi input output systems is discussed the basic idea is to use pole placement approach a new pole placement tracking problem formulation and its solution is given the design of controller requires no a priori information either about stability or minimum phase of controlled plants the designed controller consists of a feedback part a feedforward part and a new precompensator and is suitable for self tuning control of continuous systems with differing and unknown dead times in each path the controller can have integral action the number of outputs can be different from the number of inputs of the controlled system a reference vector sequence and an effect of the initial state on the system output are given as a left matrix fraction the self tuning algorithm is based on identification of explicit plant model the self tuning algorithm is illustrated by examples

suboptimal exact model matching for multivariable systems with measurement noise this paper describes a method for the design of discrete time model matching control systems for a multivariable plant with measurement noise this method uses a new parametrization for model matching controllers simulation results are presented which confirm the validity of the design method

the canonical diophantine equations with applications a fundamental relationship between appropriate pairs of polynomial matrices is presented this relationship termed canonical diophantine equations can be used to resolve a number of standard polynomial matrix problems here the general diophantine equation is constructively resolved in a unique minimal way in addition prime canonical factorizations of a system transfer matrix are derived from knowledge of any dual factorization

direct adaptive decoupling control for general stochastic multivariable systems this paper presents a novel adaptive decoupling controller for general stochastic multivariable systems with arbitrary time delay structure the decoupling controller is constructed using a diagonal dynamic precompensator to diagonalize the system interactor matrix and then combining the feedforward control strategy and the generalized minimum variance approach it cannot only control unstable and or non minimum phase processes but also decouple the closed loop systems both dynamically and in the static state it is adaptively implemented in direct form the global convergence properties and parameter estimate consistency for this adaptive decoupling algorithm are also discussed it is shown that this adaptive controller not only has globally convergent properties but also generates strongly consistent parameter estimates the results of simulations and an application are proposed to demonstrate the effectiveness of the algorithm

dependent chance programming a class of stochastic optimization this paper provides a theoretical framework of dependent chance programming as well as dependent chance multiobjective programming and dependent chance goal programming which are new types of stochastic optimization a stochastic simulation based genetic algorithm is also designed for solving dependent chance programming models

edge covering problem under hybrid uncertain environments edge covering problem ecp is to find an edge cover with the minimum weight in a graph by quantifying costs time or opponent s payoffs as weights on edges ecp is employed to model many real life problems in engineering and management since various types of uncertainty always exist in real world this paper considers ecp under hybrid uncertain environments where randomness and fuzziness coexist we propose three decision models and present a hybrid intelligent algorithm to solve the proposed models where genetic algorithm and random fuzzy simulation are embedded numerical experiments are performed to show the robustness of the proposed algorithm

randomly generating portfolio selection covariance matrices with specified distributional characteristics abstract in portfolio selection there is often the need for procedures to generate realistic covariance matrices for security returns for example to test and benchmark optimization algorithms for application in portfolio optimization such a procedure should allow the entries in the matrices to have distributional characteristics which we would consider realistic for security returns deriving motivation from the fact that a covariance matrix can be viewed as stemming from a matrix of factor loadings a procedure is developed for the random generation of covariance matrices a whose off diagonal covariance entries possess a pre specified expected value and standard deviation and b whose main diagonal variance entries possess a likely different pre specified expected value and standard deviation the paper concludes with a discussion about the futility one would likely encounter if one simply tried to invent a valid covariance matrix in the absence of a procedure such as in this paper

discrete time model following adaptive control for multivariable non minimum phase systems in this paper a control system design that is applicable to both minimum and non minimum phase plants is discussed the output of this system can follow an arbitrary reference output with a bounded input in the first part of this paper we propose a new concept which is a bounded input calculation method of following a reference output it is then used to design a model following control system mfcs for multivariable non minimum phase plants corresponding to the right and left coprime factorizations of the plant two mfcs designs are given an extension of the mfcs to adaptive control is discussed and numerical examples are given to illustrate the effectiveness of the proposed adaptive system design

an algorithm for the calculation of a nilpotent interactor matrix for linear multivariable systems a new algorithm for the calculation of the delay structure for a linear multivariable system is presented the algorithm evaluates a nilpotent interactor matrix from the matrix of coefficients of the numerator of a right matrix fraction description of the system the resulting nilpotent interactor is a product of first degree nilpotent polynomial matrices

a note on multivariable generalized predictive control this note presents a stability result for the multivariable version of the generalized predictive control algorithm shah et al 1989 in particular closed loop stability is obtained for any open loop asymptotically stable plant by a proper selection of the future output horizon and of the control weighting matrix the control law contains an integral action thus guaranteeing regulation with zero steady state error for constant exogeneous signals

a new decoupling precompensator for indirect adaptive control of multivariable linear systems this note presents an indirect adaptive control algorithm for a class of multiple input multiple output mimo linear systems the controller consists of four parts a precompensator and a feedback filter diagonalize the transfer function between a set of auxiliary control variables v i and a set of auxiliary output variables x i each siso system with input v i and output x i is then easily controlled by standard siso pole placement techniques a model of the system is identified on line and is used to update the parameters of the precompensator the feedback filter and the siso controllers the main advantage of such an algorithm lies in the decoupling of the closed loop system this quality is fundamental for tracking performances the a priori knowledge required for implementation of the algorithm consists of the system observability indexes

large scale mv efficient frontier computation via a procedure of parametric quadratic programming abstract despite the volume of research conducted on efficient frontiers in many cases it is still not the easiest thing to compute a mean variance mv efficient frontier even when all constraints are linear this is particularly true of large scale problems having dense covariance matrices and hence they are the focus in this paper because standard approaches for constructing an efficient frontier one point at a time tend to bog down on dense covariance matrix problems with many more than about 500 securities we propose as an alternative a procedure of parametric quadratic programming for more effective usage on large scale applications with the proposed procedure we demonstrate through computational results on problems in the 1000 3000 security range that the efficient frontiers of dense covariance matrix problems in this range are now not only solvable but can actually be computed in quite reasonable time

numerical verifications of solutions for elliptic equations in nonconvex polygonal domains in this paper methods for numerical verifications of solutions for elliptic equations in nonconvex polygonal domains are studied in order to verify solutions using computer it is necessary to determine some constants which appear in a priori error estimations we propose some methods for determination of these constants in numerical examples calculating these constants for anl shaped domain we verify the solution of a nonlinear elliptic equation

indirect adaptive pole placement control of mimo stochastic systems self tuning results in this paper we consider indirect adaptive pole placement control appc of linear multivariable stochastic systems instead of the canonical representation often used in the literature we propose using a non minimal but otherwise uniquely identifiable pseudo canonical parameterization that is more suitable for multivariable armax model identification to identify the plant we use the weighted extended least squares wels algorithm a least squares method with slowly decreasing weights which was introduced in bercu 1995 the pole placement controller parameters are then calculated by using a certain perturbation of the parameter estimates such that the linear models corresponding to the perturbed estimates are uniformly controllable and observable we prove that with a reasonable amount of prior information the resulting appc scheme is globally stabilizing and asymptotically self tuning regardless of the degree of persistency of external excitation these results represent the most complete study of stochastic multivariable appc systems to this date

fuzzy random chance constrained programming by fuzzy random programming we mean the optimization theory dealing with fuzzy random decision problems this paper presents a new concept of chance of fuzzy random events and constructs a general framework of fuzzy random chance constrained programming we also design a spectrum of fuzzy random simulations for computing uncertain functions arising in the area of fuzzy random programming to speed up the process of handling uncertain functions we train a neural network to approximate uncertain functions based on the training data generated by fuzzy random simulation finally we integrate the fuzzy random simulation neural network and genetic algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving fuzzy random programming models and illustrate its effectiveness by some numerical examples

a parameter adaptive control structure for linear multivariable systems this paper presents an adaptive control structure which can be used to assign all poles and zeros of a continuous time linear multivariable system represented by an m times m strictly proper transfer matrix t s provided t s has no right half plane zeros the controller parameters can be directly estimated from input output data the paper also serves to point out the type of a priori information necessary for multivariable adaptive controller design this information is a natural extension of that required in the scalar case

a division algorithm for polynomial matrices a new algorithm is presented for directly determining both the quotient and the remainder associated with the division of one polynomial matrix by another the procedure requires that the denominator matrix be column proper with nonzero column degrees however unlike earlier algorithms only real matrix multiplications are employed i e there is no need for matrix inversions either explicit or implicit

prior knowledge in model reference adaptive control of multiinput multioutput systems this note shows how the prior knowledge necessary for model reference adaptive control of multiinput multioutput systems can be weakened in particular it is shown that the real variables appearing in the system interactor matrix can be estimated along with the other parameters appearing in the system model this represents a significant reduction in the amount of prior knowledge required relative to previous work which assumed that the full interactor matrix was available a theoretical convergence analysis of the resulting algorithm is presented together with simulation results

on error estimation of finite element approximations to the elliptic equations in nonconvex polygonal domains numerical verification methods so called nakao s methods on existence or uniqueness of solutions to pdes have been developed by nakao and his group including the authors they are based on the error estimation of approximate solutions which are mainly computed by fem it is a standard way of the error estimation of fem to estimate the projection errors by elementwise interpolation errors there are some constants in the error estimation which depend on the mesh size parameters h the explicit values of the constants are necessary in order to use nakao s method however there were not so many researches for the computation of the explicit values of the constants then we had to develop the computation by ourselves especially with guaranteed accuracy note that the methods of the computation depend on the dimension the degree of bases and the shape of the domain etc the present paper shows how we have developed the methods to calculate the constants and describes new results for nonconvex domains

self tuning pid controllers algorithms and implementation a class of controllers with integral action is shown to arise directly from appropriate system models via the zero gain predictor approach a corresponding class of hybrid self tuning controllers is shown to have both integral action in the controller and offset removal in the tuning algorithm implementation details and some experimental results are given

mean absolute deviation portfolio optimization model and its applications to tokyo stock market the purpose of this paper is to demonstrate that a portfolio optimization model using the l1 risk mean absolute deviation risk function can remove most of the difficulties associated with the classical markowitz s model while maintaining its advantages over equilibrium models in particular the l1 risk model leads to a linear program instead of a quadratic program so that a large scale optimization problem consisting of more than 1 000 stocks may be solved on a real time basis numerical experiments using the historical data of nikkei 225 stocks show that the l1 risk model generates a portfolio quite similar to that of the markowitz s model within a fraction of time required to solve the latter

stable robust tracking by sliding mode control sliding mode control is examined from the perspective of obtaining stable and robust tracking of an arbitrary time varying reference by a multi input output linear time invariant system driven by a certain class of bounded errors nonlinearities and disturbances most existing schemes for such systems are subsumed by the one presented here the results are developed via the use of inverse models and make clear the constraints imposed by the finite and infinite zero structure of the system in particular stable and robust tracking is shown to be obtained by the scheme in this paper if and only if the system is minimum phase

adaptive decoupling of multivariable systems for many industrial processes it is of interest to design a decoupling precompensator the precompensator makes it possible to design controllers based on single input single output models of the process a model of the process must be known to design the precompensator this paper shows how the precompensator can be designed adaptively using input output measurements the precompensator is first designed for the case when the process is known the adaptive precompensator is then constructed using the certainty equivalence principle the convergence properties and the implementation of the adaptive decoupler are discussed it is shown that the adaptive scheme will converge provided the system is persistently excited and that a suitable model structure is used in the estimation

optimization models and a ga based algorithm for stochastic time cost trade off problem in real life projects both the trade off between the project cost and the project completion time and the uncertainty of the environment are considerable aspects for decision makers however the research on the time cost trade off problem seldom concerns stochastic environments besides optimizing the expected value of the objective is the exclusive decision making criterion in the existing models for the stochastic time cost trade off problem in this paper two newly developed alternative stochastic time cost trade off models are proposed in which the philosophies of chance constrained programming and dependent chance programming are adopted for decision making in addition a hybrid intelligent algorithm integrating stochastic simulations and genetic algorithm is designed to search the quasi optimal schedules under different decision making criteria the goal of the paper is to reveal how to obtain the optimal balance of the project completion time and the project cost in stochastic environments

minimax lq exact and inexact model matching problems abstract a mixed h 2 h optimal problem is presented for both disturbance attenuation and model mismatching minimization its solution is in the form of a two degrees of freedom controller whose feedback and feedforward paths can be independently designed if the conditions for exact model matching are not met an inexact model matching problem is addressed this solution provides an effective model matching design methodology for non minimum phase plants

paper parameterization issues in multivariable adaptive control this paper gives both an overview of multivariable adaptive control algorithms which have evolved to date and presents some new approaches to the design of indirect multivariable adaptive control systems all algorithms are presented using a unified pole placement approach furthermore the emphasis is on parameterization issues such as types of control structures necessary for implementation required prior information necessary for implementation and techniques for reducing the size of the resulting parameter estimation problem

dependent chance programming with fuzzy decisions dependent chance programming dcp is a new type of stochastic programming and has been extended to the area of fuzzy programming this paper provides a spectrum of dcp and dependent chance multiobjective programming dcmop as well as dependent chance goal programming dcgp models with fuzzy rather than crisp decisions the terms of uncertain environment event chance function and induced constraints are discussed in the case of fuzzy decisions a technique of fuzzy simulation is also designed for computing chance functions finally we present a fuzzy simulation based genetic algorithm for solving these models and illustrate its effectiveness by some numerical examples

minimization of the root of a quadratic functional under a system of affine equality constraints with application to portfolio management we present an explicit closed form solution of the problem of minimizing the root of a quadratic functional subject to a system of affine constraints the result generalizes z landsman minimization of the root of a quadratic functional under an affine equality constraint j comput appl math 2007 to appear see articles in press where the optimization problem was solved under only one linear constraint this is of interest for solving significant problems pertaining to financial economics as well as some classes of feasibility and optimization problems which frequently occur in tomography and other fields the results are illustrated in the problem of optimal portfolio selection and the particular case when the expected return of finance portfolio is certain is discussed

uncertain programming a unifying optimization theory in various uncertain environments by uncertain programming we mean the optimization theory in generally uncertain random fuzzy fuzzy random grey etc environments three broad classes of uncertain programming are expected value models and chance constrained programming as well as dependent chance programming in order to solve general uncertain programming models a simulation based genetic algorithm is also documented finally some applications and further research problems appearing in this area are posed

a reference direction approach to multiple objective quadratic linear programming in this paper we propose an interactive procedure for solving multiple criteria problems with one quadratic objective several linear objectives and a set of linear constraints the procedure is based on the use of reference directions and weighted sums reference directions for the linear functions and weighted sums for combining the quadratic function with the linear ones are used as parameters to implement the free search of nondominated solutions the idea leads to the parametric linear complementarity problem formulation an approach to deal with this type of problems is given as well the approach is illustrated with a numerical example

a multivariable self tuning pid controller abstract a multivariable self tuning controller with a proportional plus integral plus derivative pid structure is derived the algorithm features a combination of the self tuning property in which the controller parameters are tuned automatically on line and also the structure of a multivariable pid controller making it more favourable to be used in industry a stability proof of the algorithm is also presented here comparison of the performance of the algorithm with a fixed tuned multivariable pid controller in the simulation examples demonstrates the effectiveness of the proposed algorithm

a simple method for error bounds of eigenvalues of symmetric matrices abstract we propose a simple method for validated computation of eigenvalues of symmetric matrices the method is based on ldl t decomposition and its error estimation the indices of eigenvalues with respect to magnitude can also be obtained by this method

hysteresis switching adaptive control of linear multivariable systems this paper presents a model reference adaptive control scheme for deterministic continuous time multivariable systems represented by square strictly proper minimum phase transfer function matrices a typical requirement of existing algorithms is to assume that the zero structure at infinity and the high frequency gain matrix are fully or at least partially known it is well known that these requirements may be very restrictive since in general both the zero structure at infinity and the high frequency gain matrix depend on plant parameters in this paper we show that these restrictive assumptions may be considerably weakened using morse et al s hysteresis switching control strategy 1992 the strategy entails running a finite number of parameter estimators in parallel and using a switching algorithm to select between candidate estimators based on their associated prediction errors hysteresis in the switching algorithm precludes switching arbitrarily rapidly between estimators and all switching ceases within a finite time the results represent a significant step forward in understanding the minimal amount of prior knowledge necessary to design a stabilizing controller for a linear multivariable system

numerical verification of solutions for nonlinear elliptic problems using anl residual method abstract we consider a numerical enclosure method with guaranteedl error bounds for the solution of nonlinear elliptic problems of second order by using an a posteriori error estimate for the approximate solution of the problem with a higher orderc0 finite element it is shown that we can obtain the guaranteedl error bounds with high accuracy a particular emphasis is that our method needs no assumption of the existence of the solution of the original nonlinear equation but it follows as the result of computation itself a numerical example that confirms the effectiveness of the method is presented

convergence of a multivariable self tuning controller with integral action abstract in this note global convergence is established for the multivariable self tuning control algorithm presented in 11 the plant is assumed to be described by a carima model the control algorithm relies on the generalized minimum variance approach the convergence proof is carried out by resorting to an interlaced stochastic gradient identification algorithm

the mean absolute deviation portfolio selection problem with interval valued returns in real world investments one may care more about the future earnings than the current earnings of the assets this paper discusses the uncertain portfolio selection problem where the asset returns are represented by interval data since the parameters are interval valued the gain of returns is interval valued as well according to the concept of the mean absolute deviation function we construct a pair of two level mathematical programming models to calculate the lower and upper bounds of the investment return of the portfolio selection problem using the duality theorem and applying the variable transformation technique the pair of two level mathematical programs is transformed into a conventional one level mathematical program solving the pair of mathematical programs produces the interval of the portfolio return of the problem the calculated results conform to an essential idea in finance and economics that the greater the amount of risk that an investor is willing to take on the greater the potential return

multivariable adaptive dynamic surface control based on norm estimation of unknown parameter matrices in this article a novel output feedback adaptive dynamic surface control scheme is proposed for linear time invariant multivariable plants based on the norm estimation of unknown parameter matrices besides avoiding the explosion of complexity problem in traditional multivariable backstepping design the proposed scheme has the following features 1 only one parameter needs to be updated on line regardless of the plant order and input output dimension 2 only the hurwitz condition is required for the high frequency gain matrix and 3 the performance of the tracking error can be guaranteed it is shown that all signals of the closed loop system are semi globally uniformly bounded simulation results are given to illustrate the effectiveness of the proposed scheme

a multivariable self tuning controller with integral action abstract in this paper a novel stochastic self tuning control algorithm for multivariable linear systems described by controlled autoregressive integrating moving average carima models is proposed in particular the presence of an integral action on each component of the error vector ensures the robust offset rejection for any constant load disturbance acting on the plant the leading assumption is made that the system interactor matrix is known a priori the algorithm is derived by resorting to the generalized minimum variance approach two simulation examples illustrate the main features of the method

multivariable adaptive backstepping control a norm estimation approach in this note a novel output feedback adaptive backstepping control is proposed for linear time invariant multivariable plants under a mild assumption that the hurwitz condition for high frequency gain matrix is satisfied by estimating the norm of those unknown parameter matrices instead of their elements it is shown that only one parameter needs to be updated online and therefore the computational burden can be greatly reduced compared with any current multivariable adaptive control scheme

large scale portfolio optimization this paper describes a practical algorithm for large scale mean variance portfolio optimization the emphasis is on developing an efficient computational approach applicable to the broad range of portfolio models employed by the investment community what distinguishes these from the usual quadratic program is i the form of the covariance matrix arising from the use of factor and scenario models of return and ii the inclusion of transactions limits and costs a third aspect is the question of whether the problem should be solved parametrically in the risk reward trade off parameter or separately for several discrete values of we show how the parametric algorithm can be made extremely efficient by sparsifying the covariance matrix with the introduction of a few additional variables and constraints and by treating the transaction cost schedule as an essentially nonlinear nondifferentiable function then we show how these two seemingly unrelated approaches can be combined to yield good approximate solutions when minimum trading size restrictions buy or sell at least a certain amount or not at all are added in combination these approaches make possible the parametric solution of problems on a scale not heretofore possible on computers where cpu time and storage are the constraining factors

reduced order adaptive pole placement for multivariable systems a method is suggested for adaptively assigning the closed loop poles of a continuous time linear multivariable system the resulting design significantly reduces the complexity in comparison to any other known multivariable scheme for implementation one needs to know the order of the system and an upper bound on the observability index

minimum weight covering problems in stochastic environments minimum weight covering problems have played an important role in many real applications in this paper two typical minimum weight covering problems namely vertex covering problem and edge covering problem are studied under stochastic environments according to different decision criteria we propose three stochastic programming models the expected minimum weight cover model a minimum weight cover model and the most minimum weight cover model a hybrid intelligent algorithm that integrates stochastic simulation with genetic algorithm is designed to solve the proposed models finally numerical experiments are performed to show the effectiveness and robustness of the proposed algorithm

controller design oriented model identification method for hammerstein system abstract the paper proposes a new polynomial identification method for the hammerstein system by combining the results of parameter estimation theory and functions approximation theory the method circumvents many disadvantages of the earlier methods it is suitable for producing the controller design oriented model of hammerstein system consistency and convergency analysis are given for the obtained parameter estimates of the system s linear dynamics and approximating polynomial of its nonlinear memoryless element respectively simulation study verifies the effectiveness of the method