Abstract
A genetic algorithm (GA), a well-known numerical method, is widely applied in different areas of optimal studies. It is found that if the solution-search space is wide or if the selected fitness function is highly nonlinear, the GA's solutions can strongly depend on the set parameters, which include population size, crossover rate, mutation rate, and the remaining size of the parent in the GA. This paper combines the Taguchi experimental method, which serves as a rough search tool, with the GA, which serves as a fine search tool, to find the best combination of the GA parameters for different flight-control problems. The purpose of such a combination is to make control more robust and closer to the optimal solution. To demonstrate this new idea, the writers consider its application to different flight-control problems for the F-16 fighter by using autostabilization, linear quadratic regulator (LQR) and H∞ design. The simulation results not only achieve the expected optimal design for flight-control problems but also justify the reliability and feasibility of the combination of the GA and the Taguchi method.
| Original language | English |
|---|---|
| Pages (from-to) | 232-241 |
| Number of pages | 10 |
| Journal | Journal of Aerospace Engineering |
| Volume | 18 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2005 Oct 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Materials Science(all)
- Aerospace Engineering
- Mechanical Engineering
Cite this
}
Applications of genetic-Taguchi algorithm in flight control designs. / Yang, Ciann-Dong; Luo, Chi Chung; Liu, Shiu Jeng; Chang, Yeong Hwa.
In: Journal of Aerospace Engineering, Vol. 18, No. 4, 01.10.2005, p. 232-241.Research output: Contribution to journal › Review article
TY - JOUR
T1 - Applications of genetic-Taguchi algorithm in flight control designs
AU - Yang, Ciann-Dong
AU - Luo, Chi Chung
AU - Liu, Shiu Jeng
AU - Chang, Yeong Hwa
PY - 2005/10/1
Y1 - 2005/10/1
N2 - A genetic algorithm (GA), a well-known numerical method, is widely applied in different areas of optimal studies. It is found that if the solution-search space is wide or if the selected fitness function is highly nonlinear, the GA's solutions can strongly depend on the set parameters, which include population size, crossover rate, mutation rate, and the remaining size of the parent in the GA. This paper combines the Taguchi experimental method, which serves as a rough search tool, with the GA, which serves as a fine search tool, to find the best combination of the GA parameters for different flight-control problems. The purpose of such a combination is to make control more robust and closer to the optimal solution. To demonstrate this new idea, the writers consider its application to different flight-control problems for the F-16 fighter by using autostabilization, linear quadratic regulator (LQR) and H∞ design. The simulation results not only achieve the expected optimal design for flight-control problems but also justify the reliability and feasibility of the combination of the GA and the Taguchi method.
AB - A genetic algorithm (GA), a well-known numerical method, is widely applied in different areas of optimal studies. It is found that if the solution-search space is wide or if the selected fitness function is highly nonlinear, the GA's solutions can strongly depend on the set parameters, which include population size, crossover rate, mutation rate, and the remaining size of the parent in the GA. This paper combines the Taguchi experimental method, which serves as a rough search tool, with the GA, which serves as a fine search tool, to find the best combination of the GA parameters for different flight-control problems. The purpose of such a combination is to make control more robust and closer to the optimal solution. To demonstrate this new idea, the writers consider its application to different flight-control problems for the F-16 fighter by using autostabilization, linear quadratic regulator (LQR) and H∞ design. The simulation results not only achieve the expected optimal design for flight-control problems but also justify the reliability and feasibility of the combination of the GA and the Taguchi method.
UR - http://www.scopus.com/inward/record.url?scp=26644453717&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26644453717&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)0893-1321(2005)18:4(232)
DO - 10.1061/(ASCE)0893-1321(2005)18:4(232)
M3 - Review article
AN - SCOPUS:26644453717
VL - 18
SP - 232
EP - 241
JO - Journal of Aerospace Engineering
JF - Journal of Aerospace Engineering
SN - 0893-1321
IS - 4
ER -