Output feedback regulation control for a class of cascade nonlinear systems and its application to fan speed control

The output feedback regulation problem is considered for a class of nonlinear systems with integral input-to-state stable (iISS) inverse dynamics and unknown control direction. The system output together with the complete unmeasured state components appears in the system uncertainties. A systematic output feedback control scheme is presented with the help of a dynamic observer, whose gain comes from an off-line time-varying Riccati matrix differential equation. The proposed scheme can be applied to the analysis of the speed tracking control of a fan. The simulation results demonstrate the validity of the presented algorithm.     

Synchronization of fractional‐order chaotic systems with disturbances via novel fractional‐integer integral sliding mode control and application to neuron models

In this paper, a novel fractional‐integer integral type sliding mode technique for control and generalized function projective synchronization of different fractional‐order chaotic systems with different dimensions in the presence of disturbances is presented. When the upper bounds of the disturbances are known, a sliding mode control rule is proposed to insure the existence of the sliding motion in finite time. Furthermore, an adaptive sliding mode control is designed when the upper bounds of the disturbances are unknown. The stability analysis of sliding mode surface is given using the Lyapunov stability theory. Finally, the results performed for synchronization of three‐dimensional fractional‐order chaotic Hindmarsh‐Rose (HR) neuron model and two‐dimensional fractional‐order chaotic FitzHugh‐Nagumo (FHN) neuron model.     

A second-order cone cutting surface method: complexity and application

We present an analytic center cutting surface algorithm that uses mixed linear and multiple second-order cone cuts. Theoretical issues and applications of this technique are discussed. From the theoretical viewpoint, we derive two complexity results. We show that an approximate analytic center can be recovered after simultaneously adding p second-order cone cuts in O(plog (p+1)) Newton steps, and that the overall algorithm is polynomial. From the application viewpoint, we implement our algorithm on mixed linear-quadratic-semidefinite programming problems with bounded feasible region and report some computational results on randomly generated fully dense problems. We compare our CPU time with that of SDPLR, SDPT3, and SeDuMi and show that our algorithm outperforms these software packages on problems with fully dense coefficient matrices. We also show the performance of our algorithm on semidefinite relaxations of the maxcut and Lovasz theta problems. M.R. Oskoorouchi’s work has been completed with the support of the partial research grant from the College of Business Administration, California State University San Marcos, and the University Professional Development Grant. J.E. Mitchell’s material is based upon work supported by the National Science Foundation under Grant No. 0317323. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.     

A new algorithm for generalized Sylvester‐observer equation and its application to state and velocity estimations in vibrating systems

We propose a new algorithm for block‐wise solution of the generalized Sylvester‐observer equation XA?FXE = GC, where the matrices A, E, and C are given, the matrices X, F, and G need to be computed, and matrix E may be singular. The algorithm is based on an orthogonal decomposition of the triplet (A, E, C) into the observer‐Hessenberg‐triangular form. It is a natural generalization of the widely known observer‐Hessenberg algorithm for the Sylvester‐observer equation: XA?FX = GC, which arises in state estimation of a standard first‐order state‐space control system. An application of the proposed algorithm is made to state and velocity estimations of second‐order control systems modeling a wide variety of vibrating structures. For dense un‐structured data, the proposed algorithm is more efficient than the recently proposed SVD‐based algorithm of the authors. Copyright © 2010 John Wiley & Sons, Ltd.     

Transition to turbulence, small disturbances, and sensitivity analysis I: A motivating problem

For over 100 years, researchers have attempted to predict transition to turbulence in fluid flows by analyzing the spectrum of the linearized Navier-Stokes equations. However, for many simple flows this approach fails to match experimental results. Recently, new scenarios for transition have been proposed that are based on the interaction of the linearized equations of motion with small disturbances to the flow system. These new “mostly linear” theories have increased our understanding of the transition process, but the role of nonlinearity has not been explored in detail. This paper is the first of a two part work in which sensitivity analysis is used to study the effects of small disturbances on transition to turbulence. In this part, we study a highly sensitive one-dimensional Burgers' equation as a motivating problem. Sensitivity analysis is used to predict the large changes in solutions in the presence of a small disturbance. Also, sensitivity analysis is shown to provide more information about the disturbed nonlinear problem than a purely linear analysis of the problem. In the second part of this work, this analysis will be extended to the three-dimensional Navier-Stokes equations to show that small disturbances have great potential to trigger transition to turbulence.     

A global Implicit Function Theorem without initial point and its applications to control of non-affine systems of high dimensions

Control system design for non-affine systems is a difficult problem because of the lack of mathematical tools. The key to the problem is solving for an implicit function but the known results for implicit functions are not applicable for higher dimensional systems except for single-input and single-output systems. In this paper, a new version of a global implicit function theorem in higher dimension is presented and proved. This result can be applied to show the controllability of a class of non-affine multi-input and multi-output (MIMO) system so that approximation based control system design can be applied with ease.     

A new improved Adomian decomposition method and its application to fractional differential equations

In this paper, a new improved Adomian decomposition method is proposed, which introduces a convergence-control parameter into the standard Adomian decomposition method and establishes a new iterative formula. The examples prove that the presented method is reliable, efficient, easy to implement from a computational viewpoint and can be employed to derive successfully analytical approximate solutions of fractional differential equations.     

A feasible decomposition method for constrained equations and its application to complementarity problems

In this paper, by analyzing the propositions of solution of the convex quadratic programming with nonnegative constraints, we propose a feasible decomposition method for constrained equations. Under mild conditions, the global convergence can be obtained. The method is applied to the complementary problems. Numerical results are also given to show the efficiency of the proposed method.     

A stepsize control for continuation methods and its special application to multiple shooting techniques

Summary A numerically applicable stepsize control for discrete continuation methods of orderp is derived on a theoretical basis. Both the theoretical results and the performance of the proposed algorithm are invariant under affine transformation of the nonlinear system to be solved. The efficiency and reliability of the method is demonstrated by solving three real life two-point boundary value problems using multiple shooting techniques. In two of the examples bifurcations occur and are significantly marked by sharp changes in the stepsize estimates.     

A dual-interval vertex analysis method and its application to environmental decision making under uncertainty

In this study, a dual-interval vertex analysis (DIVA) method is developed, through incorporating the vertex method within an interval-parameter programming framework. The developed DIVA method can tackle uncertainties presented as dual intervals that exist in the objective function and the left- and right-hand sides of the modeling constraints. An interactive algorithm and a vertex analysis approach are proposed for solving the DIVA model. Solutions under an associated α-cut level can be generated by solving a series of deterministic submodels. They can help quantify relationships between the objective function value and the membership grade, which is meaningful for supporting in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability. A management problem in terms of regional air pollution control is studied to illustrate applicability of the proposed approach. The results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers to identify desired pollution-abatement strategies with minimized costs and maximized environmental efficiencies.     

A quantitative weak law of large numbers and its application to the delta method

Let T _n be a statistic of the form T _n = f(), where is the samplemean of a sequence of independent random variables and f denotes a prescribed function taking values in a separable Banach space. In order to establish asymptotic expansions for bias and variance of T _n conventional theorems typically impose restrictive boundedness conditions upon f or its derivatives; moreover, these conditions are sufficient but not necessary. It is shown that a quantitative version of the weak law of large numbers is both sufficient and necessary for the accuracy of Taylor expansions of T _n . In particular, boundedness conditions may be replaced by mild requirements upon the global growth of f.      

A new method for ranking fuzzy numbers and its application to group decision making

In this paper, a new method for comparing fuzzy numbers based on a fuzzy probabilistic preference relation is introduced. The ranking order of fuzzy numbers with the weighted confidence level is derived from the pairwise comparison matrix based on 0.5-transitivity of the fuzzy probabilistic preference relation. The main difference between the proposed method and existing ones is that the comparison result between two fuzzy numbers is expressed as a fuzzy set instead of a crisp one. As such, the ranking order of n fuzzy numbers provides more information on the uncertainty level of the comparison. Illustrated by comparative examples, the proposed method overcomes certain unreasonable (due to the violation of the inequality properties) and indiscriminative problems exhibited by some existing methods. More importantly, the proposed method is able to provide decision makers with the probability of making errors when a crisp ranking order is obtained. The proposed method is also able to provide a probability-based explanation for conflicts among the comparison results provided by some existing methods using a proper ranking order, which ensures that ties of alternatives can be broken.     

A modified functional delta method and its application to the estimation of risk functionals

The classical functional delta method (FDM) provides a convenient tool for deriving the asymptotic distribution of statistical functionals from the weak convergence of the respective empirical processes. However, for many interesting functionals depending on the tails of the underlying distribution this FDM cannot be applied since the method typically relies on Hadamard differentiability w.r.t. the uniform sup-norm. In this article, we present a version of the FDM which is suitable also for nonuniform sup-norms, with the outcome that the range of application of the FDM enlarges essentially. On one hand, our FDM, which we shall call the modified FDM, works for functionals that are “differentiable” in a weaker sense than Hadamard differentiability. On the other hand, it requires weak convergence of the empirical process w.r.t. a nonuniform sup-norm. The latter is not problematic since there exist strong respective results on weighted empirical processes obtained by Shorack and Wellner (1986) [25], Shao and Yu (1996) [23], Wu (2008) [32], and others. We illustrate the gain of the modified FDM by deriving the asymptotic distribution of plug-in estimates of popular risk measures that cannot be treated with the classical FDM.     

A new trigonometric method of summation and its application to the degree of approximation

The object of the present investigation is to introduce a new trigonometric method of summation which is both regular and Fourier effective and determine its status with reference to other methods of summation (see §2-§4) and also give an application of this method to determine the degree of approximation in a new Banach space of functions conceived as a generalized Holder metric (see §5).     

A Filippov-type lemma for functions involving delays and its application to time-delayed optimal control problems

A Filippov-type lemma for functions involving delays is derived. This lemma is used to prove the existence of an optimal control for a class of nonlinear control processes with delays appearing in both state and control variables.The author wishes to express his gratitude to Dr. E. Noussair and Dr. K. L. Teo for their guidance in the preparation of this paper. Also, the author wishes to thank Professor L. Cesari for his valuable suggestion.     

On the worst scenario method: A modified convergence theorem and its application to an uncertain differential equation

We propose a theoretical framework for solving a class of worst scenario problems. The existence of the worst scenario is proved through the convergence of a sequence of approximate worst scenarios. The main convergence theorem modifies and corrects the relevant results already published in literature. The theoretical framework is applied to a particular problem with an uncertain boundary value problem for a nonlinear ordinary differential equation with an uncertain coefficient. This research was supported in part by the project MSM4781305904 from the Ministry of Education, Youth and Sports of the Czech Republic.     

PBDW: A non-intrusive Reduced Basis Data Assimilation method and its application to an urban dispersion modeling framework

The challenges of understanding the impacts of air pollution require detailed information on the state of air quality. While many modeling approaches attempt to treat this problem, physically-based deterministic methods are often overlooked due to their costly computational requirements and complicated implementation. In this work we extend a non-intrusive Reduced Basis Data Assimilation method (known as PBDW state estimation) to large pollutant dispersion case studies relying on equations involved in chemical transport models for air quality modeling. This, with the goal of rendering methods based on parameterized partial differential equations (PDE) feasible in air quality modeling applications requiring quasi-real-time approximation and correction of model error in imperfect models. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a less intrusive reduced order method using data assimilation for measured pollution concentrations, adapted for consideration of the scale and specific application to exterior pollutant dispersion as can be found in urban air quality studies. Common statistical techniques of data assimilation in use in these applications require large historical data sets, or time-consuming iterative methods. The method proposed here avoids both disadvantages. In the case studies presented in this work, the method allows to correct for unmodeled physics and treat cases of unknown parameter values, all while significantly reducing online computational time.     

A improved F‐expansion method and its application to Kudryashov–Sinelshchikov equation

On the basis of the F‐expansion method with a new sub‐equation and Exp‐function method, an improved F‐expansion method is introduced. As illustrative examples, the exact solutions expressed by exponential function, hyperbolic function of Kudryashov–Sinelshchikov equation for arbitrary α,β are derived. Some previous results are extended. The method is straightforward, concise and is a promising and powerful method for other nonlinear evolution equations in mathematical physics. Copyright © 2013 John Wiley & Sons, Ltd.