Abstract
We address the problem of synchronizing estimates of plant state. The approach proposed in the study is unique because it does not attempt to transform the data to a common representation. Rather we establish a framework which we call the Multiple Agent Hybrid Estimation Architecture in which we allow heterogeneous data to flow between individual agents in the network to improve their individual estimates of the current plant state.
Research supported by SDIO contract DAA H-04-93-C-0113, Dept. of Commerce Agreement 70-NANB5H1164.
Research supported by Dept. of Commerce Agreement 70-NANB5H1164 and ARO DOD MURI, “Integrated Approach to Intelligent Systems”, DAAH04-96-1-0341
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Kohn, W., Nerode, A., Remmel, J.B. (1999). Scalable Data and Sensor Fusion via Multiple Agent Hybrid Systems. In: Antsaklis, P., Lemmon, M., Kohn, W., Nerode, A., Sastry, S. (eds) Hybrid Systems V. HS 1997. Lecture Notes in Computer Science, vol 1567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49163-5_7
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DOI: https://doi.org/10.1007/3-540-49163-5_7
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