Helicopter tracking control using direct neural dynamic programming

R. Enns; Jennie Si

Helicopter tracking control using direct neural dynamic programming

R. Enns
, Jennie Si

Electrical Engineering

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

6 Scopus citations

Abstract

This paper advances a newly introduced neural learning control mechanism for helicopter flight control design. Based on direct neural dynamic programming (DNDP), the control system is tailored to learn to maneuver a helicopter in addition to its trimming and stabilization capabilities presented in earlier works. The paper consists of a comprehensive treatise of DNDP and extensive simulation studies of DNDP designs for controlling an Apache helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industry-scale non-linear validated model of the Apache helicopter. Though illustrated for helicopters, our DNDP control system framework should be applicable for general purpose tracking control.

Original language	English (US)
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Pages	1019-1024
Number of pages	6
Volume	2
State	Published - 2001
Event	International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: Jul 15 2001 → Jul 19 2001

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'01)
Country/Territory	United States
City	Washington, DC
Period	7/15/01 → 7/19/01

ASJC Scopus subject areas

Software

Cite this

@inproceedings{17ed23c469ec4e7581f67fa1b8a33773,

title = "Helicopter tracking control using direct neural dynamic programming",

abstract = "This paper advances a newly introduced neural learning control mechanism for helicopter flight control design. Based on direct neural dynamic programming (DNDP), the control system is tailored to learn to maneuver a helicopter in addition to its trimming and stabilization capabilities presented in earlier works. The paper consists of a comprehensive treatise of DNDP and extensive simulation studies of DNDP designs for controlling an Apache helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industry-scale non-linear validated model of the Apache helicopter. Though illustrated for helicopters, our DNDP control system framework should be applicable for general purpose tracking control.",

author = "R. Enns and Jennie Si",

year = "2001",

language = "English (US)",

volume = "2",

pages = "1019--1024",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "International Joint Conference on Neural Networks (IJCNN'01) ; Conference date: 15-07-2001 Through 19-07-2001",

}

TY - GEN

T1 - Helicopter tracking control using direct neural dynamic programming

AU - Enns, R.

AU - Si, Jennie

PY - 2001

Y1 - 2001

N2 - This paper advances a newly introduced neural learning control mechanism for helicopter flight control design. Based on direct neural dynamic programming (DNDP), the control system is tailored to learn to maneuver a helicopter in addition to its trimming and stabilization capabilities presented in earlier works. The paper consists of a comprehensive treatise of DNDP and extensive simulation studies of DNDP designs for controlling an Apache helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industry-scale non-linear validated model of the Apache helicopter. Though illustrated for helicopters, our DNDP control system framework should be applicable for general purpose tracking control.

AB - This paper advances a newly introduced neural learning control mechanism for helicopter flight control design. Based on direct neural dynamic programming (DNDP), the control system is tailored to learn to maneuver a helicopter in addition to its trimming and stabilization capabilities presented in earlier works. The paper consists of a comprehensive treatise of DNDP and extensive simulation studies of DNDP designs for controlling an Apache helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industry-scale non-linear validated model of the Apache helicopter. Though illustrated for helicopters, our DNDP control system framework should be applicable for general purpose tracking control.

UR - https://www.scopus.com/pages/publications/0034870661

UR - https://www.scopus.com/pages/publications/0034870661#tab=citedBy

M3 - Conference contribution

VL - 2

SP - 1019

EP - 1024

BT - Proceedings of the International Joint Conference on Neural Networks

T2 - International Joint Conference on Neural Networks (IJCNN'01)

Y2 - 15 July 2001 through 19 July 2001

ER -

Helicopter tracking control using direct neural dynamic programming

Abstract

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this