Output feedback controller for polytopic systems exploiting the direct searching of the design space

This paper deals with the problem of designing a robust static output feedback controller for polytopic systems. The current research that tackled this problem is mainly based on LMI method, which is conservative by nature. In this paper, a novel approach is proposed, which considers the design space of the controller parameters and iteratively partitions the space to small simplexes. Then, by assessing the stability in each simplex, the solution space for design parameters is directly determined. It has been theoretically proved that, if there exists a feasible solution in the design space, the algorithm can find it. To validate the result of the proposed approach, comparative simulation examples are given to illustrate the performance of the design methodology as compared to those of previous approaches.     

Least-squares integration of one-dimensional codistributions with application to approximate feedback linearization

We study the problem of approximating one-dimensional nonintegrable codistributions by integrable ones and apply the resulting approximations to approximate feedback linearization of single-input systems. The approach derived in this paper allows a linearizable nonlinear system to be found that is close to the given system in a least-squares (L ₂) sense. A linearly controllable single-input affine nonlinear system is feedback linearizable if and only if its characteristic distribution is involutive (hence integrable) or, equivalently, any characteristic one-form (a one-form that annihilates the characteristic distribution) is integrable. We study the problem of finding (least-squares approximate) integrating factors that make a fixed characteristic one-form close to being exact in anL ₂ sense. A given one-form can be decomposed into exact and inexact parts using the Hodge decomposition. We derive an upper bound on the size of the inexact part of a scaled characteristic one-form and show that a least-squares integrating factor provides the minimum value for this upper bound. We also consider higher-order approximate integrating factors that scale a nonintegrable one-form in a way that the scaled form is closer to being integrable inL ₂ together with some derivatives and derive similar bounds for the inexact part. This allows a linearizable nonlinear system that is close to the given system in a least-squares (L ₂) sense together with some derivatives to be found. The Sobolev embedding techniques allow us to obtain an upper bound on the uniform (L _∞) distance between the nonlinear system and its linearizable approximation. This research was supported in part by NSF under Grant PYI ECS-9396296, by AFOSR under Grant AFOSR F49620-94-1-0183, and by a grant from the Hughes Aircraft Company.     

Daily impact of climate on the pattern of urban family life : Indigenous courtyard houses of Baghdad regions of the hot-dry climates Part I: Daily shifts or daily movements in summer

During the day, there are considerable variations in the climatic factors prevailing in these regions. This is especially so during the 24-hour daily cycle of the long, hot and dry summer (mid-May to midOctober). Such climatic conditions have had a considerable impact on the daily living pattern of family life in the same season, particularly in the urban areas. During the same 24-hour cycle, they have compelled the inhabitants to shift their living activities both vertically in section and horizontally in plan; they do this in pursuit of more acceptable, if not, desirable internal thermal environmental ambience.     

随机度量理论的最近进展及其应用

本文综述了随机度量理论在近十年来的重要进展及其具有代表性的某些应用。     

推广的Kantorovi多项式的L_p保持Lipschitz条件性

本文研究推广的Kantorovi多项式P_n~*(f, x)在L_p[0,1]空间中的保持Lipschitz条件性质。     

What the small Rubik’s cube taught me about data structures, information theory, and randomisation

This article discusses observations made when the state space of the 2 × 2 × 2 Rubik’s cube was constructed with various programs based on various data structures, gives theoretical explanations for the observations, and uses them to develop more memory-efficient data structures. The cube has 3,674,160 reachable states. The fastest program runs in 20 s and uses 11.1 million bytes of memory for the state set structure. It uses a 31-bit representation of the state and also stores the rotation through which each state was first found. Its memory consumption is remarkably small, considering that 3,674,160 × 31 bits is about 14.2 million bytes. Getting below this number was made possible by sharing common parts of states. Obviously, it is not possible to reduce memory consumption without limit. We derive an information-theoretic hard average lower bound of 6.07 million bytes that applies in this setting. We introduce a general-purpose variant of the data structure and end up with 8.9 million bytes and 48 s. We also discuss the performance of BDDs and perfect state packing in this application.     

Restricted overlapping balancing domain decomposition methods and restricted coarse problems for the Helmholtz problem

Overlapping balancing domain decomposition methods and their combination with restricted additive Schwarz methods are proposed for the Helmholtz equation. These new methods also extend previous work on non-overlapping balancing domain decomposition methods toward simplifying their coarse problems and local solvers. They also extend restricted Schwarz methods, originally designed to overlapping domain decomposition and Dirichlet local solvers, to the case of non-overlapping domain decomposition and/or Neumann and Sommerfeld local solvers. Finally, we introduce coarse spaces based on partitions of unity and planes waves, and show how oblique projection coarse problems can be designed from restricted additive Schwarz methods. Numerical tests are presented.     

Almost k-wise independence versus k-wise independence

We say that a distribution over {0,1}ⁿ is (ε,k)-wise independent if its restriction to every k coordinates results in a distribution that is ε-close to the uniform distribution. A natural question regarding (ε,k)-wise independent distributions is how close they are to some k-wise independent distribution. We show that there exist (ε,k)-wise independent distributions whose statistical distance is at least n^O(k)·ε from any k-wise independent distribution. In addition, we show that for any (ε,k)-wise independent distribution there exists some k-wise independent distribution, whose statistical distance is n^O(k)·ε.     

A continuous-time framework for least squares parameter estimation

This paper proposes a continuous-time framework for the least-squares parameter estimation method through evolution equations. Nonlinear systems in the standard state space representation that are linear in the unknown, constant parameters are investigated. Two estimators are studied. The first one consists of a linear evolution equation while the second one consists of an impulsive linear evolution equation. The paper discusses some theoretical aspects related to the proposed estimators: uniqueness of a solution and an attractive equilibrium point which solves for the unknown parameters. A deterministic framework for the estimation under noisy measurements is proposed using a Sobolev space with negative index to model the noise. The noise can be of large magnitude. Concrete signals issued from an electronic device are used to discuss numerical aspects.