Novel L1 adaptive control methodology for aerial refueling with guaranteed transient performance

Jiang Wang; Vijay V. Patel; Chengyu Cao; Naira Hovakimyan; Eugene Lavretsky

doi:10.2514/1.31199

Novel L₁ adaptive control methodology for aerial refueling with guaranteed transient performance

Jiang Wang, Vijay V. Patel, Chengyu Cao, Naira Hovakimyan, Eugene Lavretsky

Research output: Contribution to journal › Article › peer-review

Abstract

Autonomous aerial refueling autopilot design is addressed in this paper using a novel L₁ neural-network-based adaptive control approach, which is capable of accommodating trailing-vortex-induced uncertainties and uncertainties in control effectiveness. The main advantage of the new approach is its ability of fast adaptation that leads to uniform transient performance for the system's signals, both inputs and outputs, simultaneously, with guaranteed performance specifications. Simulation results verify the benefit of this new approach.

Original language	English (US)
Pages (from-to)	182-193
Number of pages	12
Journal	Journal of Guidance, Control, and Dynamics
Volume	31
Issue number	1
DOIs	https://doi.org/10.2514/1.31199
State	Published - 2008
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Aerospace Engineering
Space and Planetary Science
Electrical and Electronic Engineering
Applied Mathematics

Online availability

10.2514/1.31199

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Cite this

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title = "Novel L1 adaptive control methodology for aerial refueling with guaranteed transient performance",

abstract = "Autonomous aerial refueling autopilot design is addressed in this paper using a novel L1 neural-network-based adaptive control approach, which is capable of accommodating trailing-vortex-induced uncertainties and uncertainties in control effectiveness. The main advantage of the new approach is its ability of fast adaptation that leads to uniform transient performance for the system's signals, both inputs and outputs, simultaneously, with guaranteed performance specifications. Simulation results verify the benefit of this new approach.",

author = "Jiang Wang and Patel, {Vijay V.} and Chengyu Cao and Naira Hovakimyan and Eugene Lavretsky",

note = "Funding Information: This material is based upon work supported by the U.S. Air Force under contracts FA8650-05-C-3563, FA9550-05-1-0157, and FA9550-04-C-0047 and the Office of Naval Research (ONR) under contract N00014-05-1-0828. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Air Force or the ONR. The authors are thankful to Manu Sharma for providing the 6-DOF simulator model for this problem. Many useful discussions with him were helpful toward the development of the results in this paper.",

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AU - Patel, Vijay V.

AU - Cao, Chengyu

AU - Hovakimyan, Naira

AU - Lavretsky, Eugene

N1 - Funding Information: This material is based upon work supported by the U.S. Air Force under contracts FA8650-05-C-3563, FA9550-05-1-0157, and FA9550-04-C-0047 and the Office of Naval Research (ONR) under contract N00014-05-1-0828. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Air Force or the ONR. The authors are thankful to Manu Sharma for providing the 6-DOF simulator model for this problem. Many useful discussions with him were helpful toward the development of the results in this paper.

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AB - Autonomous aerial refueling autopilot design is addressed in this paper using a novel L1 neural-network-based adaptive control approach, which is capable of accommodating trailing-vortex-induced uncertainties and uncertainties in control effectiveness. The main advantage of the new approach is its ability of fast adaptation that leads to uniform transient performance for the system's signals, both inputs and outputs, simultaneously, with guaranteed performance specifications. Simulation results verify the benefit of this new approach.

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