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Distributed system identification for linear stochastic systems with binary sensors
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DOI：10.1016/j.automatica.2022.110298.png)
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Abstract
En 中文
The problem of distributed identification of linear stochastic system with unknown coefficient theta* over time-varying networks is considered. For estimating theta*, each agent in the network can only access the input and the binary-valued output of the local system. Compared with the existing works on distributed optimization and estimation, the binary-valued local output observation considered in the paper makes the problem challenging. By assuming that the agent in the network can communicate with its adjacent neighbors, a stochastic approximation-based distributed identification algorithm is proposed, and the consensus and convergence of the estimates are established. Finally, a numerical example is given showing that the simulation results are consistent with the theoretical analysis. (C) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Distributed system identification
Binary-valued sensor
Stochastic approximation
Consensus
Convergence
Strong consistency
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