5 Combined Observer Design for Robust State Estimation and Kalman filtering

2005 ◽  
pp. 89-111
Author(s):  
SangJoo Kwon ◽  
Wan Kyun Chung
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Observer Design

scholarly journals Robust Interval Observer Design for Fractional-Order Models with Applications to State Estimation of Batteries

2020 ◽  
Vol 53 (2) ◽  
pp. 3683-3688
Author(s):  
Erik Hildebrandt ◽  
Julia Kersten ◽  
Andreas Rauh ◽  
Harald Aschemann
Keyword(s):  
State Estimation ◽  
Fractional Order ◽  
Observer Design ◽  
Interval Observer

Observer Design for State Estimation of UV Curing Processes

2014 ◽  
Author(s):  
Adamu Yebi ◽  
Beshah Ayalew ◽  
Satadru Dey
Keyword(s):  
State Estimation ◽  
Online Monitoring ◽  
Uv Curing ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Advanced Manufacturing ◽  
Monitoring And Control ◽  
State Observers ◽  
State Measurement ◽  
And Control

This article discusses the challenges of non-intrusive state measurement for the purposes of online monitoring and control of Ultraviolet (UV) curing processes. It then proposes a two-step observer design scheme involving the estimation of distributed temperature from boundary sensing cascaded with nonlinear cure state observers. For the temperature observer, backstepping techniques are applied to derive the observer partial differential equations along with the gain kernels. For subsequent cure state estimation, a nonlinear observer is derived along with analysis of its convergence characteristics. While illustrative simulation results are included for a composite laminate curing application, it is apparent that the approach can also be adopted for other UV processing applications in advanced manufacturing.

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.

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