scholarly journals New Viewpoints about Pseudo Measurements Method in Equality-Constrained State Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Bingjie Zhu ◽  
Yingting Luo ◽  
Yunmin Zhu
Keyword(s):  
Dynamic System ◽  
State Estimation ◽  
Kalman Filtering ◽  
Measurement Method ◽  
Good Choice ◽  
Equality Constraint ◽  
System Model ◽  
Fundamental Theory ◽  
Original Estimate ◽  
Constraint Set

We discuss the pseudo measurement method which is one of the main approaches to equality-constrained state estimation for a dynamic system. We demonstrate by the fundamental theory of Kalman filtering that reviewing the equality constraint as a pseudo measurement seems questionable. The main reason is that the additional pseudo measurement is actually a constant here which cannot help to estimate the state. More specifically, when the states in an unconstrained dynamic system model have already satisfied the equality constraint, the extra constraint is obviously not necessary. When the true equality-constrained states do not satisfy the unconstrained dynamic process equation, the effect of pseudo measurement is projecting the estimate which is not optimal onto the constraint set. However, since the performance of a projected estimate is also certainly influenced by its original estimate, we show through a numerical example that the pseudo measurement method is not always a good choice, especially when the process equation mismatch is large.

Robust State Estimation for Linear Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 193-196
Author(s):  
S. S. Garimella ◽  
K. Srinivasan
Keyword(s):  
Dynamic System ◽  
State Estimation ◽  
Real Time ◽  
Linear Systems ◽  
Upper Bound ◽  
Estimation Error ◽  
System Model ◽  
Linear Dynamic ◽  
Modeling Errors ◽  
System States

Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

scholarly journals Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2976 ◽  
Author(s):  
Yali Ruan ◽  
Yingting Luo ◽  
Yunmin Zhu
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Optimal Algorithm ◽  
The State ◽  
Unknown Input ◽  
Initial Value ◽  
State Estimate ◽  
Multisensor Systems ◽  
Unknown Inputs ◽  
Augmented State

In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input.

scholarly journals MODEL DINAMIS SISTEM KETERSEDIAAN DAGING SAPI NASIONAL

2011 ◽  
Vol 12 (1) ◽  
pp. 128 ◽  
Author(s):  
Harmini Harmini ◽  
Ratna Winandi Asmarantaka ◽  
Juniar Atmakusuma
Keyword(s):  
Computer Program ◽  
Dynamic System ◽  
Artificial Insemination ◽  
System Model ◽  
Percentage Error ◽  
National Program ◽  
Cross Breeding ◽  
Self Sufficiency ◽  
Innovative Solution ◽  
Dynamic System Model

The purpose of this paper is to assess whether the national program on beef self sufficiency could be achieved at 2014. A dynamic system model with Vensim computer program is applied. The model validated by Mean Absolute Percentage Error. The results shows high accuracies of the model. The assessment show that, first, the beef self sufficiency would not be achieved at 2014 if the program are treated and running as usual (Scenario I). Second, the beef self sufficiency would be achieved at 2015 if government increase the cow population by reducing the slaughter of local cows and expanding the cross breeding program through artificial insemination (Scenario II). Third, the beef self sufficiency would not be achieved at 2014 if the actual beef consumption are higher than the supply that produce through Scenario II (Scenario III). Another innovative solution for increasing local cow population is needed.

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