Correction to "On the Stabilization of Discrete Linear Time-Varying Stochastic Systems"

1982 ◽  
Vol 12 (6) ◽  
pp. 934-934 ◽  
Author(s):  
Yannis A. Phillis
Keyword(s):  
Stochastic Systems ◽  
Linear Time ◽  
Time Varying

RECURSIVE SUBSPACE PREDICTION OF LINEAR TIME-VARYING STOCHASTIC SYSTEMS

2005 ◽  
Vol 38 (1) ◽  
pp. 916-921 ◽  
Author(s):  
Kentaro Kameyama ◽  
Akira Ohsumi
Keyword(s):  
Stochastic Systems ◽  
Linear Time ◽  
Time Varying

scholarly journals JOINT ESTIMATION OF STATES AND PARAMETERS OF LINEAR SYSTEMS WITH PARAMETER FAULTS UNDER NON-GAUSSIAN NOISES

2020 ◽  
Vol 18 (2) ◽  
pp. 113
Author(s):  
Vladimir Stojanović ◽  
Dragan Pršić ◽  
Ljubiša Dubonjić
Keyword(s):  
State Space ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Linear Time ◽  
Practical Importance ◽  
Joint Estimation ◽  
State Space Models ◽  
Time Varying ◽  
Linear State ◽  
Non Gaussian

Joint estimation of states and time-varying parameters of linear state space models is of practical importance for the fault diagnosis and fault tolerant control. Previous works on this topic consider the joint estimation in the Gaussian noise environment, but not in the presence of outliers. The known fact is that the measurements have inconsistent observations with the largest part of the observation population (outliers). They can significantly make worse the properties of linearly recursive algorithms which are designed to work in the presence of Gaussian noises. This paper proposes the strategy of the joint parameter-state robust estimation of linear state space models in the presence of non-Gaussian noises. The case of parameter-dependent matrices is considered. Because of its good features in robust filtering, the extended Masreliez-Martin filter represents a cornerstone for realization of the robust algorithms for joint state-parameter estimation of linear time-varying stochastic systems in the presence of non-Gaussian noises. The good features of the proposed robust algorithm for joint estimation of linear time-varying stochastic systems are illustrated by intensive simulations.

scholarly journals Prediction of Modal Parameters of Linear Time-Varying Systems

2010 ◽  
Vol 17 (4-5) ◽  
pp. 483-490 ◽  
Author(s):  
S. Marchesiello ◽  
A. Bellino ◽  
L. Garibaldi
Keyword(s):  
Stochastic Systems ◽  
Linear Time ◽  
State Space Model ◽  
Modal Parameters ◽  
Time Varying ◽  
Engineering Structures ◽  
Angle Variation ◽  
Linear Time Invariant ◽  
Stochastic Subspace Identification ◽  
Robotic Devices

Many engineering structures, such as cranes, traffic-excited bridges, flexible mechanisms and robotic devices exhibit characteristics that vary with time and are referred to as time-varying or nonstationary. In particular, linear time-varying (LTV) systems have been often dealt with on a case-by-case basis. Many concepts and analytic methods of linear time-invariant (LTI) systems cannot be applied to LTV systems, as for example the conventional definition of modal parameters. In fact, LTV systems violate one of the assumptions of the conventional modal analysis, which is stationarity.Subspace-based identification methods, proposed in the 1970s, have been attracting much attention due to their affinity to the modern control theory, which is based on the state space model. These methods are now successfully applied to many industrial cases and may be considered reference methods for identifying LTI systems.In this paper the use of a subspace-based method for identifying LTV systems is discussed and applied to both numerical and experimental systems. More precisely a modified version of the SSI method, referred to here as ST-SSI (Short Time Stochastic Subspace Identification) is introduced as well as a method for predicting time-varying stochastic systems using the angle variation between the subspaces; the latter is able to predict the system parameter in the “near” future.

Subspace-based prediction of linear time-varying stochastic systems

Automatica ◽  
2007 ◽  
Vol 43 (12) ◽  
pp. 2009-2021 ◽  
Author(s):  
Kentaro Kameyama ◽  
Akira Ohsumi
Keyword(s):  
Stochastic Systems ◽  
Linear Time ◽  
Time Varying
Sign in / Sign up

Export Citation Format

Share Document