Neurocontrol of an aircraft: Application to windshear

S. M. Amin; E. Y. Rodin; Y. Wu

doi:10.1016/0895-7177(95)00101-7

Neurocontrol of an aircraft: Application to windshear

S. M. Amin, E. Y. Rodin, Y. Wu

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

3 Scopus citations

Abstract

In this report, we consider the part of our work which concerns the design of neuroidentifiers and neurocontrollers which attenuate the effects of disturbances. Examples for linear-systems identification and disturbance rejection, as well as nonlinear control of an aircraft encountering wind shear on take-off are briefly discussed, and the following three problems are addressed. 1. 1. System identification via dynamic neural networks. 2. 2. Disturbance attenuation via memory neurons. 3. 3. Aircraft control in the presence of wind shear after takeoff.

Original language	English (US)
Pages (from-to)	63-78
Number of pages	16
Journal	Mathematical and Computer Modelling
Volume	22
Issue number	1
DOIs	https://doi.org/10.1016/0895-7177(95)00101-7
State	Published - Jul 1995
Externally published	Yes

Bibliographical note

Funding Information:
This research was supported in part by AFOSR under Grant No. F49620-93-1-0012. Earlier versions of this work have been reported in references [l-5] and at NASA Ames Research Center.

Keywords

Aircraft control. Systems identification and control
Dynamic neural networks
Robust control of nonlinear systems

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10.1016/0895-7177(95)00101-7

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title = "Neurocontrol of an aircraft: Application to windshear",

abstract = "In this report, we consider the part of our work which concerns the design of neuroidentifiers and neurocontrollers which attenuate the effects of disturbances. Examples for linear-systems identification and disturbance rejection, as well as nonlinear control of an aircraft encountering wind shear on take-off are briefly discussed, and the following three problems are addressed. 1. 1. System identification via dynamic neural networks. 2. 2. Disturbance attenuation via memory neurons. 3. 3. Aircraft control in the presence of wind shear after takeoff.",

keywords = "Aircraft control. Systems identification and control, Dynamic neural networks, Robust control of nonlinear systems",

author = "Amin, {S. M.} and Rodin, {E. Y.} and Y. Wu",

note = "Funding Information: This research was supported in part by AFOSR under Grant No. F49620-93-1-0012. Earlier versions of this work have been reported in references [l-5] and at NASA Ames Research Center.",

year = "1995",

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AU - Amin, S. M.

AU - Rodin, E. Y.

AU - Wu, Y.

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PY - 1995/7

Y1 - 1995/7

N2 - In this report, we consider the part of our work which concerns the design of neuroidentifiers and neurocontrollers which attenuate the effects of disturbances. Examples for linear-systems identification and disturbance rejection, as well as nonlinear control of an aircraft encountering wind shear on take-off are briefly discussed, and the following three problems are addressed. 1. 1. System identification via dynamic neural networks. 2. 2. Disturbance attenuation via memory neurons. 3. 3. Aircraft control in the presence of wind shear after takeoff.

AB - In this report, we consider the part of our work which concerns the design of neuroidentifiers and neurocontrollers which attenuate the effects of disturbances. Examples for linear-systems identification and disturbance rejection, as well as nonlinear control of an aircraft encountering wind shear on take-off are briefly discussed, and the following three problems are addressed. 1. 1. System identification via dynamic neural networks. 2. 2. Disturbance attenuation via memory neurons. 3. 3. Aircraft control in the presence of wind shear after takeoff.

KW - Aircraft control. Systems identification and control

KW - Dynamic neural networks

KW - Robust control of nonlinear systems

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Neurocontrol of an aircraft: Application to windshear

Abstract

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