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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Philipp, P. Schmid-Zurek, T. |
| Copyright Year | 2012 |
| Description | Author affiliation: Institute of Automatic Control (Prof. Dr. B. Lohmann), Technische Universität München, Boltzmann Str. 15, D-85748 Garching, Germany (Philipp, P.; Schmid-Zurek, T.) |
| Abstract | This paper presents a distributed moving horizon estimator (DMHE) based on dual decomposition. The DMHE is equivalent to a centralized Kalman filter and allows the distributed implementation of any centralized controller. This equivalence is achieved by formulating the estimation problem as a suitable convex optimization problem. The cost function is defined on a sliding window involving a finite number of past measurements. These measurements are allocated to the estimators without requiring local observability of the complete state. The communication topology of the estimators is represented by a graph and reflected in the optimization problem by additional consensus constraints. A subgradient method is used for solving the decoupled dual optimization problem. The resulting distributed algorithm alternates between updating and transmitting the primal and dual variables. Simulation results of a closed-loop weir system are provided in order to show the main features of the proposed method. |
| Starting Page | 4792 |
| Ending Page | 4798 |
| File Size | 225414 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467320658 |
| ISSN | 07431546 |
| e-ISBN | 9781467320665 |
| e-ISBN | 9781467320641 |
| DOI | 10.1109/CDC.2012.6426275 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-12-10 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vectors Kalman filters Cost function Sensors Estimation Covariance matrix |
| Content Type | Text |
| Resource Type | Article |
| Subject | Control and Optimization Control and Systems Engineering Modeling and Simulation |
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