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Distributed joint parameter and state estimation algorithm for large-scale interconnected systems
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DOI：10.1002/acs.3756.png)
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Abstract
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This paper proposes a distributed joint parameter and state variables estimation algorithm for large-scale state-space interconnected systems. In this distributed estimation scheme, each interconnected sub-system is described by a linear discrete-time state space mathematical model. Each sub-system is supposed to be controlled by an intelligent controller that can communicate with its interconnected neighbors and exchange information, such as state variables. The proposed approach comprises two recursive estimation algorithms, a parameter estimation algorithm considering the state space model and a distributed Kalman filter for state variables estimation. It is a fully distributed cooperative approach that allows to reduce complexity and saves computational and communication resources. Theoretical analysis and numerical examples are provided to prove the feasibility and effectiveness of this joint estimation algorithm.
Keywords:
distributed estimation algorithm
joint estimation
Kalman filter
large-scale systems
parameter estimation
state estimation
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