Singularly perturbed linear control systems have been traditionally studied with one-vector input that controls both the slow and fast state variables. Motivated by examples of real physical systems, in this paper we ...
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Singularly perturbed linear control systems have been traditionally studied with one-vector input that controls both the slow and fast state variables. Motivated by examples of real physical systems, in this paper we extend the study to corresponding two-vector input systems. We have identified several classes of real physical singularly perturbed systems that can be more efficiently controlled using two-vector inputs. In such systems, one vector input is used to control the slow state space variables and the other vector input is used to control the fast state variables. This control structure offers more flexibility controlling given systems and appears to be more effective, We study the linear-quadratic regulator (LQR) problem for this new class of singularly perturbed systems and develop a numerical algorithm based on fixed-point iterations to obtain its solution. The latter is obtained in terms of reduced-order sub-problems which avoid possible numerical stiffness issues and are computationally advantageous for large scale systems. (C) 2017 Elsevier Ltd. All rights reserved.
A new state equation and non-linear operator-based approach to estimation is introduced for discrete-time multi-channel systems. This is a type of deconvolution or inferential estimation problem, where a signal enters...
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A new state equation and non-linear operator-based approach to estimation is introduced for discrete-time multi-channel systems. This is a type of deconvolution or inferential estimation problem, where a signal enters a communications channel involving both non-linearities and transport delays. The measurements are corrupted by a coloured noise signal, which is correlated with the signal to be estimated both at the inputs and outputs of the channel. The communications channel may include either static or dynamic non-linearities represented in a general non-linear operator form. The optimal non-linear estimator is derived in terms of the state equations and non-linear operators that describe the system. The algorithm is relatively simple to derive and to implement in the form of a recursive algorithm. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. The results may be applied to the solution of channel equalisation problems in communications or fault detection problems in control applications.
This paper presents a new recursive Newton type algorithm devoted to frequency relaying. The algorithm is designed from the nonrecursive Newton type algorithm and recursive least error squares algorithm. The frequency...
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This paper presents a new recursive Newton type algorithm devoted to frequency relaying. The algorithm is designed from the nonrecursive Newton type algorithm and recursive least error squares algorithm. The frequency is estimated from the uniformly sampled voltage signal. The algorithm testing based on computer simulated and experimentally obtained data record processing confirmed the good features of the algorithm developed. Due to its computational efficiency, the algorithm is suitable for various real-time power system measurement applications.
An alternative polynomial approximation for the activation sigmoid function is developed here. It can considerably simplify the input/output operations of a neural network. The recursive algorithm is found for Chebysh...
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An alternative polynomial approximation for the activation sigmoid function is developed here. It can considerably simplify the input/output operations of a neural network. The recursive algorithm is found for Chebyshev expansion of all constituting polynomials.
A recursive LU decomposition scheme extends the capabilities of nonrecursive algorithms for the reduction of computation time when geometrical modifications are made to a precomputed scatterer. Significant savings are...
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A recursive LU decomposition scheme extends the capabilities of nonrecursive algorithms for the reduction of computation time when geometrical modifications are made to a precomputed scatterer. Significant savings are achieved for cases of many different attachments, or when the initial scatterer is amenable to a particular efficient solution. (C) 1994 John Wiley & Sons, Inc.
We present subroutines for the Cholesky factorization of a positive-definite symmetric matrix and for solving corresponding sets of linear equations. They exploit cache memory by using the block hybrid format proposed...
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We present subroutines for the Cholesky factorization of a positive-definite symmetric matrix and for solving corresponding sets of linear equations. They exploit cache memory by using the block hybrid format proposed by the authors in a companion article. The matrix is packed into n(n + 1)/2 real variables, and the speed is usually better than that of the LAPACK algorithm that uses full storage (n(2) variables). Included are subroutines for rearranging a matrix whose upper or lower-triangular part is packed by columns to this format and for the inverse rearrangement. Also included is a kernel subroutine that is used for the Cholesky factorization of the diagonal blocks since it is suitable for any positive-definite symmetric matrix that is small enough to be held in cache. We provide a comprehensive test program and simple example programs.
An optimal estimation scheme is presented, which determines the satellite attitude using the gyro readings and the star tracker measurements of a commonly used satellite attitude measuring unit. The scheme is mainly b...
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An optimal estimation scheme is presented, which determines the satellite attitude using the gyro readings and the star tracker measurements of a commonly used satellite attitude measuring unit. The scheme is mainly based on the exponential Fourier densities that have the desirable closure property under conditioning. By updating a finite and fixed number of parameters, the conditional probability density, which is an exponential Fourier density, is recursively determined. Simulation results indicate that the scheme is more accurate and robust than extended Kalman filtering. It is believed that this approach is applicable to many other attitude measuring units. As no linearization and approximation are necessary in the approach, it is ideal for systems involving high levels of randomness and/or low levels of observability and systems for which accuracy is of overriding importance.
The task of simultaneous tracking of time-varying parameters and estimation of the state is treated for a linear system described by a time-varying input-output ARMAX or Delta model with known c (noise) parameters. Fi...
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The task of simultaneous tracking of time-varying parameters and estimation of the state is treated for a linear system described by a time-varying input-output ARMAX or Delta model with known c (noise) parameters. First, a Bayesian approach-based conceptual solution is presented. Then it is shown that utilizing the properties of the observer canonical state model, algebraic recursion operating on the joint parameter and state mean and covariance matrix can be obtained with no approximation involved. Several illustrative examples are included.
作者:
DUPUIS, PKUSHNER, HJBrown Univ
Div of Applied Mathematics Providence RI USA Brown Univ Div of Applied Mathematics Providence RI USA
Asymptotic properties of Robbins–Munro and Kiefer–Wolfowitz type stochastic approximation algorithms are obtained via the theory of large deviations. The conditions are weak and can even yield w.p.l. convergence res...
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Asymptotic properties of Robbins–Munro and Kiefer–Wolfowitz type stochastic approximation algorithms are obtained via the theory of large deviations. The conditions are weak and can even yield w.p.l. convergence results. The probability of escape of the iterates from a neighborhood of a stable point of the algorithm is estimated and shown to be considerably smaller than suggested by the classical “asymptotic normality of local normalized errors” method of getting the asymptotic properties. The escape probabilities are a natural quantity of interest. In many applications, they are more useful than the “local normalized mean square errors.” Other large deviations estimates are also obtained. Typically, if ${{a_n = 1} / {n^\rho }}$, $\rho \leqq 1$, then the probability of escape from a neighborhood of a stable point in some (normalized) time interval $[n,m]:\sum_n^m {a_i \sim T} $ is $\exp - n^\rho V_\rho $, where $V_\rho $ does not depend on $\rho $ for $\rho < 1$ and is the solution to an optimal control problem. If the noise is Gaussian, then the optimal control problem is relatively easy. Under quite broad conditions, in the Kiefer–Wolfowitz case the control problem has the Gaussian form, whether or not the noise is Gaussian. The techniques are expected to be quite useful in the analysis of the asymptotic properties of recursive algorithms generally.
The use of parameter estimation techniques in practical applications requires accurate analysis of the associated measurement and computation problems. With reference to an already proposed model identification proced...
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The use of parameter estimation techniques in practical applications requires accurate analysis of the associated measurement and computation problems. With reference to an already proposed model identification procedure, this paper deals with the experimental tests carried out in order to highlight problems and to find the most appropriate solutions. In particular, a synchronization method is described, and some suggestions concerning the optimal working conditions of all the necessary devices are reported.
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