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Decentralized optimization, with application to multiple aircraft coordination

Inalhan, G., Stipanovic, D.M., Tomlin, C.J.·2003·Proceedings of the 41st IEEE Conference on Decision and Control, 2002.

Abstract

We present a decentralized optimization method for solving the coordination problem of interconnected nonlinear discrete-time dynamic systems with multiple decision makers. The optimization framework embeds the inherent structure in which each decision maker has a mathematical model that captures only the local dynamics and the associated interconnecting global constraints. A globally convergent algorithm based on sequential local optimizations is presented. Under assumptions of differentiability and linear independence constraint qualification, we show that the method results in global convergence to /spl epsiv/-feasible Nash solutions that satisfy the Karush-Kuhn-Tucker necessary conditions for Pareto-optimality. We apply this methodology to a multiple unmanned air vehicle system, with kinematic aircraft models, coordinating in a common airspace with separation requirements between the aircraft.

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Metadata

Authors
Inalhan, G., Stipanovic, D. M., & Tomlin, C. J.
Venue
Proceedings of the 41st IEEE Conference on Decision and Control, 2002.
Publisher
IEEE
Published
2003-08-27
Volume
1
Pages
1147-1155
ISSN
0191-2216 (ISSN-L)
OpenAlex
W2166344854
Cited by
261
References
0
FWCI
8.39
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Decentralized optimization, with application to multiple aircraft coordination. https://app.cochat.ai/papers/10.1109/cdc.2002.1184667
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Inalhan, G., Stipanovic, D. M., & Tomlin, C. J. (2003). Decentralized optimization, with application to multiple aircraft coordination. Proceedings of the 41st IEEE Conference on Decision and Control, 2002. https://doi.org/10.1109/cdc.2002.1184667

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.