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Set-membership-based distributed moving horizon estimation of large-scale systems
Pablo Segovia1, Vicenç Puig2, Eric Duviella3
1Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands.
ISA Transactions
|November 24, 2021
Summary
This study introduces a novel two-step distributed state estimation method for large-scale systems, enhancing accuracy with set-membership and moving horizon techniques for robust disturbance handling.
Area of Science:
- Control Engineering
- Systems Science
- Optimization
Background:
- Large-scale systems require accurate state estimation despite disturbances.
- Existing distributed methods may lack robustness to unknown-bounded noise.
- Set-membership provides guaranteed bounds for state estimation.
Purpose of the Study:
- To design a two-step distributed state estimation scheme.
- To incorporate set-membership for handling unknown-but-bounded disturbances and noise.
- To improve state estimation accuracy and robustness in large-scale systems.
Main Methods:
- Employed a set-membership approach to define feasible state sets.
- Utilized a moving horizon estimator for optimal state estimation.
- Applied decomposition and community detection for problem partitioning and coordination.
Main Results:
- The proposed scheme effectively estimates states in the presence of bounded disturbances and noise.
- Demonstrated improved performance compared to centralized and non-set-membership distributed methods.
- Validated the strategy on a reactor-separator system case study.
Conclusions:
- The two-step distributed set-membership state estimation is effective for large-scale systems.
- The method offers enhanced robustness and accuracy over existing approaches.
- The decomposition strategy enables efficient coordination of subproblems.
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