scholarly journals Distributed moving horizon state estimation for sensor networks with low computation capabilities

2021 ◽  
Vol 1 (1) ◽  
pp. 81-87
Author(s):  
Antonello Venturino ◽  
Cristina Stoica Maniu ◽  
Sylvain Bertrand ◽  
Teodoro Alamo ◽  
Eduardo F. Camacho
Keyword(s):  
State Estimation ◽  
Information Fusion ◽  
Estimation Error ◽  
Computation Time ◽  
Local Problem ◽  
Luenberger Observer ◽  
Distributed State Estimation ◽  
Consensus Information ◽  
Computation Load ◽  
Time Linear

This paper focuses on distributed state estimation for sensor network observing a discrete-time linear system. The provided solution is based on a Distributed Moving Horizon Estimation (DMHE) algorithm considering a pre-estimating Luenberger observer in the formulation of the local problem solved by each sensor. This leads to reduce the computation load, while preserving the accuracy of the estimation. Moreover, observability properties of local sensors are used for tuning the weights related to consensus information fusion built on a rank-based condition, in order to improve the convergence of the estimation error. Results obtained by Monte Carlo simulations are provided to compare the performance with existing approaches, in terms of accuracy of the estimations and computation time.

scholarly journals Distributed State Estimation Based Distributed Model Predictive Control

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1327
Author(s):  
Jing Zeng ◽  
Jinfeng Liu
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
Predictive Control ◽  
Estimation Error ◽  
The State ◽  
Distributed Model ◽  
Actual State ◽  
Output Measurement ◽  
Distributed Model Predictive Control ◽  
Distributed State Estimation

In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.

scholarly journals ADMM-based Distributed State Estimation for Power Systems: Evaluation of Performance

2020 ◽  
Vol 53 (5) ◽  
pp. 182-188
Author(s):  
Sergei Parsegov ◽  
Samal Kubentayeva ◽  
Elena Gryazina ◽  
Alexander Gasnikov ◽  
Federico Ibáñez
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Distributed State Estimation ◽  
Evaluation Of Performance

scholarly journals A new approach to constrained state estimation for discrete-time linear systems with unknown inputs

2017 ◽  
Vol 28 (1) ◽  
pp. 326-341 ◽  
Author(s):  
Jose Fernando Garcia Tirado ◽  
Alejandro Marquez-Ruiz ◽  
Hector Botero Castro ◽  
Fabiola Angulo
Keyword(s):  
State Estimation ◽  
Linear Systems ◽  
Discrete Time ◽  
New Approach ◽  
Unknown Inputs ◽  
Time Linear

scholarly journals Plug-and-play distributed state estimation for linear systems

2013 ◽  
Author(s):  
Stefano Riverso ◽  
Marcello Farina ◽  
Riccardo Scattolini ◽  
Giancarlo Ferrari-Trecate
Keyword(s):  
State Estimation ◽  
Linear Systems ◽  
Plug And Play ◽  
Distributed State Estimation
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