Discrete-Time Observers and Parameter Determination for Distributed Parameter Systems with Discrete-Time Input–Output Data

1983 ◽  
Vol 21 (3) ◽  
pp. 331-351 ◽  
Author(s):  
Toshihiro Kobayashi
Keyword(s):  
Discrete Time ◽  
Distributed Parameter Systems ◽  
Parameter Determination ◽  
Distributed Parameter ◽  
Input Output ◽  
Output Data

On the Control of Discrete-Time Distributed Parameter Systems

1972 ◽  
Vol 10 (2) ◽  
pp. 361-376 ◽  
Author(s):  
Kwang Yun Lee ◽  
Shui-Nee Chow ◽  
Robert O. Barr
Keyword(s):  
Discrete Time ◽  
Distributed Parameter Systems ◽  
Distributed Parameter

Engineering Methods and Software Support for Modeling and Design of Discrete-time Control of Distributed Parameter Systems

2009 ◽  
Vol 15 (3-4) ◽  
pp. 407-417 ◽  
Author(s):  
G. Hulkó ◽  
C. Belavý ◽  
A. Mészáros ◽  
P. Bucek ◽  
K. Ondrejkovic ◽  
...  
Keyword(s):  
Discrete Time ◽  
Distributed Parameter Systems ◽  
Distributed Parameter ◽  
Software Support ◽  
Time Control

A Discrete-Time Adaptive Filter for Stochastic Distributed Parameter Systems

1981 ◽  
Vol 103 (3) ◽  
pp. 266-278 ◽  
Author(s):  
K. Watanabe ◽  
T. Yoshimura ◽  
T. Soeda
Keyword(s):  
Discrete Time ◽  
Adaptive Filter ◽  
Adaptive Estimation ◽  
Gaussian White Noise ◽  
Numerical Procedure ◽  
Linear Partial Differential Equation ◽  
Distributed Parameter Systems ◽  
Complete Orthonormal System ◽  
Distributed Parameter ◽  
Nonlinear Part

A discrete-time adaptive filter is derived for a distributed system described by a linear partial differential equation with some unknown random constants whose a priori probabilities are known. The system concerned contains the Gaussian white noise in time, and its measurement system is treated as a so-called pointwise observation in which the measurement is taken at the finite discrete subdomains in the coordinate spaces. The use is conceptually made of an adaptive technique based on the Bayesian method and it is shown that the optimal distributed filter proposed here can be partitioned into two parts, a linear nonadaptive part that consists of a bank of distributed Kalman-Bucy type filters and a nonlinear part that incorporates the learning nature of the estimator. For the derivation of each “elemental filter”, the discrete-time innovation theory is utilized. The eigenfunction expanding method in a complete orthonormal system is applied for the numerical procedure of the proposed filter. From the simulation of the estimation problem for the neutron flux distribution in a slab type nuclear reactor, the proposed adaptive filter is shown to have attractive characteristics and therefore can be recommended for practical online adaptive estimation of distributed parameter systems.

Minimum-Order Mathematical Models of Discrete Linear Dynamic Systems

1972 ◽  
Vol 94 (2) ◽  
pp. 139-146 ◽  
Author(s):  
A. G. Behring ◽  
V. J. Flanigan
Keyword(s):  
Mathematical Models ◽  
Discrete Time ◽  
Dynamic Systems ◽  
Input Output ◽  
Output Data ◽  
Minimum Order ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Common Problems ◽  
Time Invariant

An orthonormal filter is used to obtain minimum-order, mathematical models of linear, multivariable, discrete, time-invariant systems from input/output data. Some common problems associated with obtaining such descriptions are considered, including the problem of noise-corrupted observations.

Sign in / Sign up

Export Citation Format

Share Document