Helicopter trimming and tracking control using direct neural dynamic programming

Russell Enns, Jennie Si

Research output: Contribution to journalArticle

195 Citations (Scopus)

Abstract

This paper advances a neural-netvcork-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input-multiple-output non-linear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.

Original languageEnglish (US)
Pages (from-to)929-939
Number of pages11
JournalIEEE Transactions on Neural Networks
Volume14
Issue number4
DOIs
StatePublished - Jul 2003

Fingerprint

Trimming
Helicopter
Tracking Control
Dynamic programming
Helicopters
Dynamic Programming
Approximate Dynamic Programming
Nonlinear Systems
Nonlinear systems
Control System
Flight Control
Multiple-input multiple-output (MIMO) Systems
Methodology
Control systems
Control Design
Nonlinear Model
Baseline
Control Problem
Complex Systems
Disturbance

Keywords

  • Approximate dynamic programming
  • Helicopter flight control
  • Helicopter trim
  • Neural dynamic programming

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture

Cite this

Helicopter trimming and tracking control using direct neural dynamic programming. / Enns, Russell; Si, Jennie.

In: IEEE Transactions on Neural Networks, Vol. 14, No. 4, 07.2003, p. 929-939.

Research output: Contribution to journalArticle

@article{8d86a63a1c5146c7824c677f481bcb57,
title = "Helicopter trimming and tracking control using direct neural dynamic programming",
abstract = "This paper advances a neural-netvcork-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input-multiple-output non-linear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.",
keywords = "Approximate dynamic programming, Helicopter flight control, Helicopter trim, Neural dynamic programming",
author = "Russell Enns and Jennie Si",
year = "2003",
month = "7",
doi = "10.1109/TNN.2003.813839",
language = "English (US)",
volume = "14",
pages = "929--939",
journal = "IEEE Transactions on Neural Networks and Learning Systems",
issn = "2162-237X",
publisher = "IEEE Computational Intelligence Society",
number = "4",

}

TY - JOUR

T1 - Helicopter trimming and tracking control using direct neural dynamic programming

AU - Enns, Russell

AU - Si, Jennie

PY - 2003/7

Y1 - 2003/7

N2 - This paper advances a neural-netvcork-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input-multiple-output non-linear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.

AB - This paper advances a neural-netvcork-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input-multiple-output non-linear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.

KW - Approximate dynamic programming

KW - Helicopter flight control

KW - Helicopter trim

KW - Neural dynamic programming

UR - http://www.scopus.com/inward/record.url?scp=0043026775&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0043026775&partnerID=8YFLogxK

U2 - 10.1109/TNN.2003.813839

DO - 10.1109/TNN.2003.813839

M3 - Article

VL - 14

SP - 929

EP - 939

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

SN - 2162-237X

IS - 4

ER -