scholarly journals Discrete-Time Steady-State Minimum-Variance Prediction and Filtering

10.5772/39253 ◽  
2012 ◽  
Author(s):  
Garry Einicke
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Minimum Variance

Steady-state feedforward compensation for discrete time multivariable systems

1991 ◽  
Vol 1 (3) ◽  
pp. 253-276 ◽  
Author(s):  
H. A. Pak ◽  
Rowmau Shieh
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Multivariable Systems

Minimum-variance fault isolation observers for discrete-time linear systems

2013 ◽  
Author(s):  
Arne Wahrburg ◽  
Dominik Haumann ◽  
Volker Willert
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Minimum Variance ◽  
Fault Isolation ◽  
Time Linear

Steady-state errors in discrete-time control systems

Automatica ◽  
1993 ◽  
Vol 29 (2) ◽  
pp. 523-526 ◽  
Author(s):  
Irwin W. Sandberg ◽  
Lilian Y. Xu
Keyword(s):  
Steady State ◽  
Control Systems ◽  
Discrete Time ◽  
Time Control

scholarly journals Steady-state response of constant coefficient discrete-time differential systems

2016 ◽  
Vol 28 (1) ◽  
pp. 29-32 ◽  
Author(s):  
Manuel D. Ortigueira ◽  
Margarita Rivero ◽  
Juan J. Trujillo
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Constant Coefficient ◽  
Steady State Response ◽  
Differential Systems ◽  
State Response ◽  
Time Differential

Estimation of Steady-State Optimal Filter Gain From Nonoptimal Kalman Filter Residuals

1994 ◽  
Vol 116 (3) ◽  
pp. 550-553 ◽  
Author(s):  
Chung-Wen Chen ◽  
Jen-Kuang Huang
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Finite Number ◽  
Discrete Time ◽  
Stochastic System ◽  
Moving Average ◽  
Ar Model ◽  
Numerical Example ◽  
Measurement Noises ◽  
Time Invariant

This paper proposes a new algorithm to estimate the optimal steady-state Kalman filter gain of a linear, discrete-time, time-invariant stochastic system from nonoptimal Kalman filter residuals. The system matrices are known, but the covariances of the white process and measurement noises are unknown. The algorithm first derives a moving average (MA) model which relates the optimal and nonoptimal residuals. The MA model is then approximated by inverting a long autoregressive (AR) model. From the MA parameters the Kalman filter gain is calculated. The estimated gain in general is suboptimal due to the approximations involved in the method and a finite number of data. However, the numerical example shows that the estimated gain could be near optimal.

scholarly journals Sojourn Times in A Queueing System with Breakdowns and General Retrial Times

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2882
Author(s):  
Ivan Atencia ◽  
José Luis Galán-García
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Queueing System ◽  
Retrial Queue ◽  
Stochastic Decomposition ◽  
Discrete Time System ◽  
Main Research ◽  
Extensive Analysis ◽  
Complete Study ◽  
Number Of Customers

This paper centers on a discrete-time retrial queue where the server experiences breakdowns and repairs when arriving customers may opt to follow a discipline of a last-come, first-served (LCFS)-type or to join the orbit. We focused on the extensive analysis of the system, and we obtained the stationary distributions of the number of customers in the orbit and in the system by applying the generation function (GF). We provide the stochastic decomposition law and the application bounds for the proximity between the steady-state distributions for the queueing system under consideration and its corresponding standard system. We developed recursive formulae aimed at the calculation of the steady-state of the orbit and the system. We proved that our discrete-time system approximates M/G/1 with breakdowns and repairs. We analyzed the busy period of an auxiliary system, the objective of which was to study the customer’s delay. The stationary distribution of a customer’s sojourn in the orbit and in the system was the object of a thorough and complete study. Finally, we provide numerical examples that outline the effect of the parameters on several performance characteristics and a conclusions section resuming the main research contributions of the paper.

scholarly journals Optimal guaranteed cost filtering for Markovian jump discrete-time systems

2004 ◽  
Vol 2004 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Magdi S. Mahmoud ◽  
Peng Shi
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Estimation Error ◽  
Linear Matrix ◽  
State Estimator ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Discrete Time Systems ◽  
Linear State ◽  
Time Systems

This paper develops a result on the design of robust steady-state estimator for a class of uncertain discrete-time systems with Markovian jump parameters. This result extends the steady-state Kalman filter to the case of norm-bounded time-varying uncertainties in the state and measurement equations as well as jumping parameters. We derive a linear state estimator such that the estimation-error covariance is guaranteed to lie within a certain bound for all admissible uncertainties. The solution is given in terms of a family of linear matrix inequalities (LMIs). A numerical example is included to illustrate the theory.

scholarly journals The Complex Adaptive Delta-Modulator in Sliding Mode Theory

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 814
Author(s):  
Dhafer Almakhles
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Sliding Mode ◽  
Stability Conditions ◽  
Mode Theory ◽  
Dynamical Behaviors ◽  
Complex Adaptive ◽  
The Stability ◽  
Parameter Values

In this paper, we consider the stability and various dynamical behaviors of both discrete-time delta modulator (Δ-M) and adaptive Δ-M. The stability constraints and conditions of Δ-M and adaptive Δ-M are derived following the theory of quasi-sliding mode. Furthermore, the periodic behaviors are explored for both the systems with steady-state inputs and certain parameter values. The results derived in this paper are validated using simulated examples which confirms the derived stability conditions and the existence of periodicity.

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