### Abstract

This paper introduces the Global-Local Mapping Approximation algorithm as a candidate for identifying nonlinear, six degree-of-freedom rigid body aircraft dynamics. The technique models the nonlinear dynamical model as a sum of linear model and nonlinear model. The linear model dynamics are assumed to be perturbed by a nonlinear term which represents the system nonlinearities that are not captured by the linear model. Lyapunov stability analysis is used to derive the learning laws. To demonstrate the suitability of the algorithm for nonlinear system identification of aircraft dynamics, a longitudinal and a lateral/directional example using nonlinear simulation data, and flight test data are conducted. The true nonlinear model is generated using both the six degree-of-freedom nonlinear equations of motion of an aircraft, and by flight test data. Results presented in the paper demonstrate the utility of the Global-Local Mapping Approximation for the realistic cases of an unknown control distribution matrix B and unknown influence coefficient matrix C.

Original language | English (US) |
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Title of host publication | AIAA Atmospheric Flight Mechanics Conference and Exhibit |

State | Published - Dec 1 2008 |

Event | AIAA Atmospheric Flight Mechanics Conference and Exhibit - Honolulu, HI, United States Duration: Aug 18 2008 → Aug 21 2008 |

### Publication series

Name | AIAA Atmospheric Flight Mechanics Conference and Exhibit |
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### Other

Other | AIAA Atmospheric Flight Mechanics Conference and Exhibit |
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Country | United States |

City | Honolulu, HI |

Period | 8/18/08 → 8/21/08 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Aerospace Engineering
- Mechanical Engineering

### Cite this

*AIAA Atmospheric Flight Mechanics Conference and Exhibit*[2008-6895] (AIAA Atmospheric Flight Mechanics Conference and Exhibit).

}

*AIAA Atmospheric Flight Mechanics Conference and Exhibit.*, 2008-6895, AIAA Atmospheric Flight Mechanics Conference and Exhibit, AIAA Atmospheric Flight Mechanics Conference and Exhibit, Honolulu, HI, United States, 8/18/08.

**GLOMAP approach for nonlinear system identification of aircraft dynamics using flight data.** / Marwaha, Monika; Valasek, John; Singla, Puneet.

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

TY - GEN

T1 - GLOMAP approach for nonlinear system identification of aircraft dynamics using flight data

AU - Marwaha, Monika

AU - Valasek, John

AU - Singla, Puneet

PY - 2008/12/1

Y1 - 2008/12/1

N2 - This paper introduces the Global-Local Mapping Approximation algorithm as a candidate for identifying nonlinear, six degree-of-freedom rigid body aircraft dynamics. The technique models the nonlinear dynamical model as a sum of linear model and nonlinear model. The linear model dynamics are assumed to be perturbed by a nonlinear term which represents the system nonlinearities that are not captured by the linear model. Lyapunov stability analysis is used to derive the learning laws. To demonstrate the suitability of the algorithm for nonlinear system identification of aircraft dynamics, a longitudinal and a lateral/directional example using nonlinear simulation data, and flight test data are conducted. The true nonlinear model is generated using both the six degree-of-freedom nonlinear equations of motion of an aircraft, and by flight test data. Results presented in the paper demonstrate the utility of the Global-Local Mapping Approximation for the realistic cases of an unknown control distribution matrix B and unknown influence coefficient matrix C.

AB - This paper introduces the Global-Local Mapping Approximation algorithm as a candidate for identifying nonlinear, six degree-of-freedom rigid body aircraft dynamics. The technique models the nonlinear dynamical model as a sum of linear model and nonlinear model. The linear model dynamics are assumed to be perturbed by a nonlinear term which represents the system nonlinearities that are not captured by the linear model. Lyapunov stability analysis is used to derive the learning laws. To demonstrate the suitability of the algorithm for nonlinear system identification of aircraft dynamics, a longitudinal and a lateral/directional example using nonlinear simulation data, and flight test data are conducted. The true nonlinear model is generated using both the six degree-of-freedom nonlinear equations of motion of an aircraft, and by flight test data. Results presented in the paper demonstrate the utility of the Global-Local Mapping Approximation for the realistic cases of an unknown control distribution matrix B and unknown influence coefficient matrix C.

UR - http://www.scopus.com/inward/record.url?scp=78651259443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78651259443&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78651259443

SN - 9781563479458

T3 - AIAA Atmospheric Flight Mechanics Conference and Exhibit

BT - AIAA Atmospheric Flight Mechanics Conference and Exhibit

ER -