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Transfer State Estimator for New Operation Modes Using Variable-Structure Multiple Models
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AI
DOI：10.1109/TASE.2026.3670369.png)
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Abstract
En 中文
This paper addresses the state estimation problem for new operation modes when there is insufficient measurement data available to learn model parameters. The proposed method, called the transfer state estimator, is formulated using variable-structure multiple model estimation, which enables one to improve estimation performance by transferring model knowledge from different source modes to the target mode. Specifically, first, to track system parameter changes, this work utilizes residuals, which represent the deviations between the actual state and the predicted state. These residuals play a crucial role in determining which knowledge needs to be updated. The transfer state estimator is then derived by integrating knowledge from source models. Through this fusion process, the estimator leverages the existing knowledge to handle the new mode in the target domain. Finally, we provide numerical examples and practical simulations to show the efficacy of the proposed method. The results illustrate that the proposed state estimator is a competitive alternative to various existing state estimation methods when dealing with state estimation in the presence of a new mode. Note to Practitioners—Automation systems often experience unexpected changes in their operating conditions due to equipment aging, environmental disturbances, or system reconfigurations. These new operation modes pose a significant challenge for conventional state estimation methods that rely on predefined models and sufficient training data. This paper proposes a transfer state estimator based on variable-structure multiple models, which enables the system to adapt to unknown modes by reusing knowledge from previously learned models. The estimator dynamically adjusts the model set to incorporate new modes without requiring complete parameter identification. This approach is particularly suitable for practitioners working in domains such as fault-tolerant control, industrial automation, and intelligent monitoring, where rapid adaptation to uncertain or evolving conditions is crucial for maintaining system reliability and performance.
Keywords:
Markovian jump linear systems
state estimation
transfer learning
new mode monitoring
KL divergence
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