Neural network based adaptive control with Hierarchy-Structured Dynamic Inversion applied to nonlinear aircraft model

Tetsujiro Ninomiya; Yoshikazu Miyazawa

doi:10.2514/6.2010-7869

Neural network based adaptive control with Hierarchy-Structured Dynamic Inversion applied to nonlinear aircraft model

Tetsujiro Ninomiya, Yoshikazu Miyazawa

Flight Dynamics Lab

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Japan Aerospace Exploration Agency plans a flight demonstration project for a super sonic transport. A typical supersonic transport has a larger flight envelope than conventional one, so that the flight controller is required to be more flexible to the flight environment. Therefore, the adaptive control method is a promising candidate for these flight control system. In this paper, we propose an adaptive control method which is comprised of neural networks and Hierarchical-Structured Dynamic Inversion. Its structure is clearly comprehensible to the users and numerical simulation results show that proposed method is effective even with model errors.

Original language	English
Title of host publication	AIAA Guidance, Navigation, and Control Conference
DOIs	https://doi.org/10.2514/6.2010-7869
Publication status	Published - Dec 1 2010
Event	AIAA Guidance, Navigation, and Control Conference - Toronto, ON, Canada Duration: Aug 2 2010 → Aug 5 2010

Other

Other	AIAA Guidance, Navigation, and Control Conference
Country	Canada
City	Toronto, ON
Period	8/2/10 → 8/5/10

All Science Journal Classification (ASJC) codes

Aerospace Engineering
Control and Systems Engineering

Access to Document

10.2514/6.2010-7869

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Cite this

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title = "Neural network based adaptive control with Hierarchy-Structured Dynamic Inversion applied to nonlinear aircraft model",

abstract = "Japan Aerospace Exploration Agency plans a flight demonstration project for a super sonic transport. A typical supersonic transport has a larger flight envelope than conventional one, so that the flight controller is required to be more flexible to the flight environment. Therefore, the adaptive control method is a promising candidate for these flight control system. In this paper, we propose an adaptive control method which is comprised of neural networks and Hierarchical-Structured Dynamic Inversion. Its structure is clearly comprehensible to the users and numerical simulation results show that proposed method is effective even with model errors.",

author = "Tetsujiro Ninomiya and Yoshikazu Miyazawa",

year = "2010",

month = dec,

day = "1",

doi = "10.2514/6.2010-7869",

language = "English",

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note = "AIAA Guidance, Navigation, and Control Conference ; Conference date: 02-08-2010 Through 05-08-2010",

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AU - Miyazawa, Yoshikazu

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N2 - Japan Aerospace Exploration Agency plans a flight demonstration project for a super sonic transport. A typical supersonic transport has a larger flight envelope than conventional one, so that the flight controller is required to be more flexible to the flight environment. Therefore, the adaptive control method is a promising candidate for these flight control system. In this paper, we propose an adaptive control method which is comprised of neural networks and Hierarchical-Structured Dynamic Inversion. Its structure is clearly comprehensible to the users and numerical simulation results show that proposed method is effective even with model errors.

AB - Japan Aerospace Exploration Agency plans a flight demonstration project for a super sonic transport. A typical supersonic transport has a larger flight envelope than conventional one, so that the flight controller is required to be more flexible to the flight environment. Therefore, the adaptive control method is a promising candidate for these flight control system. In this paper, we propose an adaptive control method which is comprised of neural networks and Hierarchical-Structured Dynamic Inversion. Its structure is clearly comprehensible to the users and numerical simulation results show that proposed method is effective even with model errors.

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Y2 - 2 August 2010 through 5 August 2010

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