252 research outputs found

    A physics-based approach to flow control using system identification

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    Control of amplifier flows poses a great challenge, since the influence of environmental noise sources and measurement contamination is a crucial component in the design of models and the subsequent performance of the controller. A modelbased approach that makes a priori assumptions on the noise characteristics often yields unsatisfactory results when the true noise environment is different from the assumed one. An alternative approach is proposed that consists of a data-based systemidentification technique for modelling the flow; it avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design. This technique is applied to flow over a backward-facing step, a typical example of a noise-amplifier flow. Physical insight into the specifics of the flow is used to interpret and tailor the various terms of the auto-regressive model. The designed compensator shows an impressive performance as well as a remarkable robustness to increased noise levels and to off-design operating conditions. Owing to its reliance on only timesequences of observable data, the proposed technique should be attractive in the design of control strategies directly from experimental data and should result in effective compensators that maintain performance in a realistic disturbance environment

    SYSTEM IDENTIFICATION USING NEWTON–RAPHSON METHOD BASED ON SYNERGY OF HUBER AND PSEUDO–HUBER FUNCTIONS

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    In real situations the presence of outliers is unavoidable and that is why the distribution of a disturbance is non-Gaussian. A synthesis of an algorithm of identification based on the Newton-Raphson method is considered for this case. The method requires that the loss function should be twice differentiable. Huber loss function, relevant for the treatment of outliers, has just the first derivative. In order to overcome the problem, the pseudo- Huber loss function is introduced. This function behaves similarly to the Huber loss function and has derivatives of an arbitrary order. In this paper, the pseudo- Huber loss function is used for the second derivative of functional in the Newton-Raphson procedure. The main contributions of the paper are: (i) Design of a new robust recursive algorithm based on the synergy of Huber and pseudo – Huber functions; (ii) The convergence analysis

    Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory

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    This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon canonical form and presents a fully automatic data driven approach to model specification using a new technique to determine the Kronecker invariants. A novel feature of the inferential procedures developed here is that they work in terms of a canonical scalar ARMAX representation in which the exogenous regressors are given by predetermined contemporaneous and lagged values of other variables in the VARMA system. This feature facilitates the construction of algorithms which, from the perspective of macroeconomic modeling, are efficacious in that they do not use AR approximations at any stage. Algorithms that are applicable to both asymptotically stationary and unit-root, partially nonstationary (cointegrated) time series models are presented. A sequence of lemmas and theorems show that the algorithms are based on calculations that yield strongly consistent estimates.Keywords: Algorithms, asymptotically stationary and cointegrated time series, echelon

    DESIGN OF ROBUST RECURSIVE IDENTIFICATION ALGORITHMS FOR LARGE-SCALE STOCHASTIC SYSTEMS

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    The robust recursive algorithms, for identification of decentralized stochastic systems, are developed. It is supposed that stochastic disturbance belongs to a specified class of distributions which include the gross error model suitable for the description of outliers presence. Such an assumption introduces into the recursive algorithms a nonlinear transformation of prediction error. The given algorithms are robust with respect to uncertainty in the disturbance distribution. The individual subsystems are described with SISO (single-input single output) ARMAX model. Two algorithms are considered: the stochastic approximation and the least squares. Their comparison is based on simulations

    Parametric uncertainty in system identification

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    An introductory survey of probability density function control

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    YesProbability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.De Montfort University - DMU HEIF’18 project, Natural Science Foundation of Shanxi Province [grant number 201701D221112], National Natural Science Foundation of China [grant numbers 61503271 and 61603136

    Information Geometry

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    This Special Issue of the journal Entropy, titled “Information Geometry I”, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience

    Identification and Control of Chaotic Maps: A Frobenius-Perron Operator Approach

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