scholarly journals A Sequential Identification Method for Discrete-Time Linear Dynamic Systems

1973 ◽  
Vol 9 (4) ◽  
pp. 456-462
Author(s):  
Yasuyuki FUNAHASHI ◽  
Yoshiaki ITO ◽  
Kahei NAKAMURA
Keyword(s):  
Discrete Time ◽  
Dynamic Systems ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Identification Method ◽  
Time Linear

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.

scholarly journals Robust Multi-Parametric Control of Continuous-Time Linear Dynamic Systems

2017 ◽  
Vol 50 (1) ◽  
pp. 4660-4665
Author(s):  
Muxin Sun ◽  
Mario E. Villanueva ◽  
Efstratios N. Pistikopoulos ◽  
Benoît Chachuat
Keyword(s):  
Dynamic Systems ◽  
Continuous Time ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Parametric Control ◽  
Time Linear

scholarly journals On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3837
Author(s):  
Rafael Orellana ◽  
Rodrigo Carvajal ◽  
Pedro Escárate ◽  
Juan C. Agüero
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Dynamic Systems ◽  
Gaussian Mixture ◽  
Error Model ◽  
Fault Detection And Diagnosis ◽  
Deterministic Models ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

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